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Level IIBook 2 (Topics 7-11)

September 2009 Exam

CAIANotes ®

C AI A

Not es

S eptemb er2 0 0 9 E xam

L e

vel I I B ook 2

T opi c s 7 1 1

®



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CAIA ® Notes

CAIA Level IIBook 2 (Topics 7-11)

September 2009 Exam

Copyright © 2009 Institutional Investor, Inc. (ISBN #0-9800485-4-0 978-0-9800485-4-4) POSTMASTER: Send address changes for CAIA® Notes to Circulation

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About the ExamPrep Faculty

ERIK BENRUD, CAIA, CFA, FRM

Curriculum Director

Erik Benrud is an associate clinical professor of finance at Drexel University’s LeBow College of Business, Philadelphia. His research interests are hedge funds, swaps and options, and competi-tion in financial services. Dr. Benrud has earned the CAIA, CFA and FRM designations. He hasextensive experience in teaching and writing exam preparatory material. He has authored sever-al papers in derivatives, financial services, and financial forecasting. Dr. Benrud received hisPh.D. from the University of Virginia.

VIKAS AGARWAL

Topic Author

Vikas Agarwal, who was Curriculum Director of IIExamPrep for two exam cycles in 2008-09, isan assistant professor of finance at Georgia State University in Atlanta. He is widely published onhedge fund strategy and performance, and has been teaching courses on asset pricing and thefinancial system at Georgia State University since 2001. He has a Ph.D. in finance from theLondon Business School

DONALD R. CHAMBERS, CAIA

Topic Author

Don Chambers, who was Curriculum Director of IIExamPrep until March 2008, is the Walter E.Hanson/KPMG Peat Marwick Professor of Business and Finance at Lafayette College in Easton,

Penn. He is widely published on investments, corporate finance, risk management, and alterna-tive investments. Dr. Chambers was one of the first candidates to earn the CAIA® designation,and has played a leading role in designing learning materials for those taking the CAIA® exami-nation.

HENRY A. DAVIS

Topic Author

Henry (Hal) Davis, an independent consultant, is editor of The Journal of Structured Financeand The Journal of Investment Compliance. He has written and co-authored 15 books andnumerous articles in the areas of corporate finance and the financial markets.

URBI GARAY

Topic Author

Urbi Garay is a professor of finance at the IESA Business School in Caracas,Venezuela, and is cur-rently a visiting professor and researcher at the Isenberg School of Management and the Centerfor International Securities and Derivatives Markets (CISDM) at the University of Massachus-etts, Amherst. He teaches both MBA and undergraduate courses in investments, derivatives prod-ucts, corporate finance and international finance.



RAJ GUPTA

Topic Author

Raj Gupta is research director of the Center for International Securities and Derivatives Markets (CISDM) at theUniversity of Massachusetts, Amherst. He supervises the CISDM Hedge Funds and Managed Futures DatabaseHe is also a visiting faculty at Clark University. Dr. Gupta is assistant editor for The Journal of Alternative

Investments, and has published widely in leading financial journals and alternative investment books.

SANJAY K. NAWALKHA

Topic Author

Sanjay Nawalkha is an associate professor of finance at the Isenberg School of Management, University ofMassachusetts, Amherst, where he teaches fixed income, asset pricing, and finance theory. He has published sev-eral books and articles on interest rate risk and fixed income valuation. His most recent book series The Fixed

Income Valuation Course, includes Dynamic Term Structure Modeling and the forthcoming Credit Risk Modeling.



T ABLE OF CONTENTS

PART5:CURRENTAND INTEGRATEDTOPICS

Topic 7: Structured Products, New Products and New Strategies ........................................................ 1

Topic 8: Asset Allocation .............................................................................................................................. 19

Topic 9: Current Topics .............................................................................................................................. 47

Topic 10: Portfolio and Risk Management .............................................................................................. 73

Topic 11: Research Issues in Alternative Investments .......................................................................... 81

GLOSSARY .............................................................................................................................................................................................................. 105

INDEX ............................................................................................................................................................................................................................ 117

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Navigating the CAIA ® Notes

CAIA® Notes are comprehensive study materials organized to prepare you or the

orthcoming CAIA® Exam. Te CAIA® Notes are most eective when used in con- junction with the CAIA® Study Guide and original reading materials, and Institu-tional Investor’s CAIA® Prep soware.

CAIA® Notes Level 2, Book 2 (opics 7-12) is organized around the CAIA® Study Guide’s “Learning Objectives”: it gives you summaries and explanations o whatour experienced authors believe are the most important issues in the curriculum.Tat is, it provides you with the material that is most likely needed to correctly re-spond to the CAIA® exam questions.

CAIA® Notes begins by listing the CAIA Association® Course Outline by opic and

Learning Objective. You can quickly reerence a particular Learning Objective by turning to the page number against each Learning Objective.

Each opic starts by listing the Main Points rom the CAIA® Study Guide. Withinthat opic, it then goes through the explanations or each Learning Objective andits sub-parts. Te Learning Objectives are summarized using various explanations,examples, and calculations, where appropriate. Keywords are highlighted withineach opic to remind you o these important terms as you read. Each opic alsolists the original source reerences.

Te Glossary aims to provide useul inormation directly related to the Keywords.Each entry in the Glossary reers back to its relevant opic. Te Index at the endalso highlights the Keywords. In the Index, the page numbers in bold are the pageson which a Keyword is ound in its respective opic.

Various icons are placed throughout the books to point out calculations, reerenc-es, and note-worthy items. We have also boxed out important equations or quick reerence (you can also fnd a separate Formula Sheet at www.iiexamprep.com). Allo these eatures should assist you with your navigation through the various opicsas you study.

For your convenience, we have produced both a digital and paper version o CAIA® Notes. Tis allows you to download CAIA® Notes onto your laptop, or bring the

book with you in your briecase, so that you can study anytime, anywhere.





Copyright © 2009 CAIA Association.® All Rights Reserved.

i

CAIA® Study Guide Learning Objectives

This CAIA Association® listing is by Topic and Learning Objective. Each Learning Objective within a Topic has a page number (in brackets) for this CAIA® Notes book.

PART 5: CURRENT AND INTEGRATED TOPICS

TOPIC 7: Structured Products, New Products and New Strategies7.1 Describe the characteristics of a special purpose vehicle (SPV) in the context of collateralized obligations. (1) 7.2 Describe the key characteristics of a collateralized fund obligation (CFO). (2) 7.3 Explain the benefits and risks of investing in CFOs. (2)7.4 Describe the structure of a collateralized commodity obligation (CCO). (3) 7.5 Describe the conceptual characteristics of infrastructure sectors. (3) 7.6 Compare infrastructure with other traditional and alternative assets. (4) 7.7 Critique the evidence on the performance history for infrastructure investments. (5) 7.8 Explain how the composition and construction of the following indices impact their relative performance: (5)

a. RREEF Hypothetical Infrastructure Index b. UBS Global Infrastructure & Utilities Indexc. Moody’s Economy.com Infrastructure Index

7.9 Identify risks involved with infrastructure investments.(6)7.10 Explain the economic implications of climate change in terms of its impacts on existing assets, future economic

activity, increased regulation, and consumer behavior. (7)7.11 Describe the role of financial markets in reducing the economic cost of climate change through (7)

a. markets for catastrophe and weather risks. b. emissions trading.c. climate-related investments.

7.12 Explain the economics rationale for using financial instruments to transfer risk. (9)7.13 Discuss the criteria that need to be fulfilled by instruments employed for risk transfer. (9) 7.14 Describe existing instruments that can be used to transfer risk and identify potential investors and sponsors of

these instruments. (10)7.15 Describe both exchange traded as well as over-the-counter weather derivatives. (11)7.16 Describe emissions trading, its project based mechanism, and its potential market participants. (11) 7.17 Compare the factor-based approach to hedge fund replication with the payoff distribution approach to hedge

fund replication, in terms of their: (12) a. goals.

b. methodology.c. ability to replicate hedge fund returns.d. benefits.

e. drawbacks.7.18 Discuss the term convergence as it is applied to the alternative investments industry. (14) 7.19 Compare and contrast the historical objectives of private equity funds with that of hedge funds. (14) 7.20 Contrast recent hedge fund participation in traditional private equity activities with recent private equity

participation in traditional hedge funds activities. (15) 7.21 Explain why the distressed investment space provides an excellent example of recent convergence of hedge fund

and private equity strategies. (16) 7.22 Describe the emergence of the hybrid hedge fund/private equity fund. (16) 7.23 Discuss the factors that contributed to the convergence of private equity and hedge fund strategies referencing

recent trends in the area. (17) 7.24 Discuss the concerns and risks related to the trend toward convergence of hedge fund and private equity fund

strategies. (18)

TOPIC 8: Asset Allocation

8.1 Calculate the portfolio’s asset values after a given change in the equity value, using: (19)

a. buy-and-hold. b. constant mix.c. constant-proportion portfolio insurance.

8.2 Compare the payoff and exposure diagrams of the buy-and-hold, constant mix, constant-proportion portfolioinsurance, and option-based portfolio insurance strategies. (23)

8.3 Determine the expected performance and cost of implementing strategies with concave payoff curves relative tothose with convex payoff curves under: (26) a. trending markets.

b. flat (but oscillating) markets.8.4 Discuss the motivations for and impact of resetting the parameters of dynamic strategies. (28)8.5 Describe examples of undiversified “strategies” that have allowed individuals to become wealthy. (28)



Topics and Learning Objectives


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8.6 Describe changes in the financial system have thrust more responsibility upon individuals with regard to wealthmanagement and asset allocation. (28)

8.7 Explain and apply the concept of personal risk and its various components to the asset allocation problem faced by individuals. (29)

8.8 Explain and apply the wealth allocation framework that accounts for various dimensions of risk and leads to anideal portfolio that provides: (29) a. the certainty of protection from anxiety.

b. the high probability of maintaining one’s standard of living.c. the possibility of substantially moving upward in the wealth spectrum.

8.9 Develop and justify an asset and risk allocation for an individual using the information provided to the candidateduring the examination. (30)

8.10 Understand the impact of alternative investments, including real estate, executive stock options and humancapital on asset allocation of individual investors. (31)

8.11 Describe and apply barbell and option based strategies in the context of asset allocation. (31) 8.12 Discuss reasons why the performance of rebalanced equally weighted commodity futures portfolio should not be

used to represent the return of commodity futures asset class. (32) 8.13 Explain why the three most commonly used commodity futures indices (GSCI, DJ-AIGCI, CRB) show different

levels of return and volatility over a common time period. (32) 8.14 Explain how the returns of a single cash-collateralized commodity futures and a portfolio of cash-collateralized

commodity futures can be decomposed into various sources of return. (33) 8.15 Discuss the four theoretical frameworks (CAPM, the insurance perspective, hedging pressure hypothesis, theory

of storage) used to explain the source of commodity futures excess returns. (34) 8.16 Explain the concepts of contango, normal backwardation and market backwardation. (35)

8.17 Calculate the roll yield of a commodity futures contract in backwardation or contango. (35) Note: The 12th linefrom bottom of the left column should read “if inventories are high, the convenience yield may be low.”

8.18 Discuss the importance of roll return in explaining the long-run cross-sectional variation of commodity futuresreturns and the implication for investors. (36)

8.19 Describe the relative importance of volatility of spot return and roll return in determining the volatility of futures returns. (36)

8.20 Describe the impact of inflation and unexpected changes in the rate of inflation on individual commoditycontracts, sectors, and diversified commodity portfolios and indices. (36)

8.21 Explain how rebalancing and diversification can impact the geometric rate of return of a portfolio in comparisonto its arithmetic rate of return. (38)

8.22 Discuss the effectiveness of tactical asset allocation in commodity portfolios using strategies based onmomentum and term structure of futures prices. (38)

8.23 Argue against the use of naïve extrapolation of past commodities returns to forecast future performance anddiscuss the importance of formulating forward-looking expectations. (39)

8.24 Discuss the role of global commercial real estate in a strategic asset allocation setting. (40)

8.25 Identify the components of the commercial real estate asset class and the relative advantages of direct real estateinvestment and real estate investment trusts (REITs). (41)

8.26 Explain the historical performance and diversification benefits of select asset classes. (41) 8.27 Compare the assumptions and results of the CAPM approach to the Black-Litterman approach when

determining forward-looking asset allocations. (42) 8.28 Explain the seven caveats identified by the author as considerations for strategic asset allocation to global

commercial real estate. (43)

TOPIC 9: Current Topics9.1 Understand what is meant by the “term structure of a commodity futures curve” and the terms “backwardation”

and “contango.” (47) 9.2 Understand the derivation of the futures curve for natural gas and the association between the curve and

potential determinants including anticipated production, consumption and seasonal factors. (47) 9.3 Explain a futures calendar-spread strategy and the sources of potential profits, potential losses and risk from this

type of strategy. (48)

9.4. Describe the type of calendar-spread strategy Amaranth employed and explain the rationale for this strategy as itrelates to natural gas pricing. (48) 9.5 Discuss the magnitude of Amaranth’s calendar-spread positions: explain how this hedge fund was able to

accumulate such large positions (including the role of position limits) and describe the effects of the magnitudeof the positions on daily profits and losses. (49)

9.6 Discuss the causes for increased volatility on the natural gas commodity futures market prior to Amaranth’sliquidation in September 2006. (52)

9.7 Discuss how sophisticated storage operators can manage their storage facilities as a set of options on calendar spreads. (52)

9.8 Describe how daily volatility as measured by standard deviation can underestimate potential risk (where risk isdefined as the likelihood of experiencing severe loss), and explain how scenario analysis can be used to better indicate the risk of a fund’s structural position in such circumstances. (53)





iii

9.9 Describe what is meant by “nodal” or “one-way” liquidity in the commodity markets and how the lack of “two-way” liquidity adversely affected Amaranth. (55)

9.10 Understand how forced liquidations can affect market prices and why changes in market prices can be correlatedwith the size and direction of the liquidation. (55)

9.11 Discuss eight hypotheses explaining the market events of August 2007. (56) 9.12 Illustrate an understanding of the terminology used to describe distinct categories of fund strategies that fall

under the broad heading of “long/short equity.” (57) 9.13 Describe the anatomy of the long/short equity strategy. Explain how it is simulated in the paper, how the

strategy provides liquidity to the market place, how leveraged portfolio returns are constructed, the relationship between market capitalization and the strategy’s profitability, and the practical implications of transactionscosts. (58)

9.14 Explain the return pattern of the main simulated strategy during the second week of August 2007. (60) 9.15 Compare and contrast market events in August 2007 with August 1998. (60) 9.16 Explain how the increase in total assets under management and the number of long/short funds over the 1998 to

2007 time period likely impacted expected returns and the use of leverage. (60) 9.17 Describe the set of hypotheses that are collectively referred to as the “unwind hypothesis.” (61) 9.18 Discuss one proposed measure of illiquidity of long/short equity funds and how the results have changed over

the past decade. (61) 9.19 Describe a method for approximating a network view of the hedge-fund industry and what such a view

indicates. (62) 9.20 Evaluate the statement: Quant failed in August 2007. (62) 9.21 Critique the methodology of the article. (63) 9.22 Evaluate the current outlook for systemic risk in the hedge fund industry. (64)

9.23 Describe a subprime loan and discuss the four principal reasons for the recent increase in sub-prime loandelinquencies. (64)

9.24 Explain the economic motivations that enabled the waterfall payment structure of an ABS trust or CDOstructure with a collateral pool consisting of high-yield securities to attain an investment grade rating for thesecurities they issued and the resulting contribution to the credit crisis. (65)

9.25 Explain the role of rating agencies in the credit crisis. (66) 9.26 Criticize the incentive compensation system for mortgage brokers and lenders and its adverse effect on the due-

diligence efforts at the firms. (66) 9.27 Explain the factors affecting the rating of a special investment vehicle (SIV). (66) 9.28 Describe the role of monolines. (67) 9.29 Explain the lack of incentives for banks to perform due diligence on the collateral pool. (67) 9.30 Explain the role and actions of central banks in 2007 and early 2008. (67) 9.31 Explain the role of valuation methods. (67) 9.32 Describe the lack of transparency in the credit markets. (68) 9.33 Describe how systemic risk arose in 2007. (68)

9.34 Argue how increased transparency in the rating process is necessary. (69) 9.35 Argue how standardization can simplify valuation issues. (69) 9.36 Assess the hidden risks of implicit and explicit off balance-sheet bank commitments and argue how increased

transparency can provide investors with information regarding financial institutions’ exposure. (69) 9.37 Describe how new product design can dampen market disruptions. (70) 9.38 Discuss possible regulatory responses. (70) 9.39 Describe sound risk management practices. (71) 9.40 Describe nonlinearities in the risk of subprime CDO tranches. (71)

TOPIC 10: Portfolio and Risk Management

10.1 Assess the long-run and short-run benefits of hedging the tail risk of a portfolio. (73) 10.2 Explain the relationship between systemic risk, liquidity risk, monetary policy and other macro events. (73) 10.3 Explain why increased correlation among various asset returns during periods of stress could provide

opportunities for free insurance against tail risk. (74) 10.4 Describe the four approaches to hedging or insuring a portfolio against tail risk. (74)

10.5

Explain why dynamic strategies such as portfolio insurance cannot be used to hedge against tail risk. (74) 10.6 Describe the three factors that impact the construction of a tail hedge. (75) 10.7 Explain why long-dated options may provide an inexpensive method for hedging tail risk. (75) 10.8 Evaluate the factors that lead to the underpricing of risk by investors. (75) 10.9 Explain the relationship between the real economy and capital markets and discuss the factors that have made

the real economy less volatile through time. (76) 10.10 Discuss why capital markets are complex and adaptive and explain the implications of these characteristics for

models of risk measurement. (76) 10.11 Compare and contrast the terms risk and uncertainty. (77) 10.12 Explain the role of shadow banking system as a source of liquidity and discuss why during periods of market

stress this source of liquidity may disappear. (77) 10.13 Demonstrate how cognitive biases can lead to errors in judgment by financial market participants. (78)





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10.14 Describe factors complicating the establishment and maintenance of target allocations to illiquid asset classes.(78)

10.15 Explain the role of Monte-Carlo simulation to achieve stable (steady-state) allocation in this study. (79) 10.16 Illustrate the total impact of several individual risk factors on private equity allocation drift. (79)

TOPIC 11: Research Issues in Alternative Investments

11.1 Illustrate how an investment in commodity futures can earn a positive return when spot commodity prices arefalling. (81)

11.2 Compare commodity spot returns and commodity futures returns. (82) 11.3 Compare commodity futures returns with stock returns and bond returns. (83) 11.4 Compare commodity futures risk with equity risk. (83) 11.5 Discuss the use of commodity futures as a hedge against inflation. (84) 11.6 Explain the diversification benefits of commodity futures. (84) 11.7 Describe the performance of commodity futures from a non-US investor’s perspective. (85)11.8 Describe the difference between normal backwardation and a market that is in backwardation. ( 85 ) 11.9 Describe a trading strategy that uses basis in futures markets as an indication of risk premium in futures

markets. (86) 11.10 Describe the factors that cause smoothing and how smoothing impacts asset allocation decisions. (86) 11.11 Compare the results of Stevenson (2004) with previous studies on the impact of smoothing models on

allocations to real estate. (87) 11.12 Compare four approaches to generating an unsmoothed total real estate return series. (87) 11.13 Describe the impact of varying smoothing parameters for UK real estate return data on the optimal allocations

to real estate. (92)

11.14 In the Marcato and Key (2007) study, compare and contrast the results of using UK data with those employingUS and Australia real estate return data. (92)

11.15 Argue the best method of adjusting a real estate return series when conducting an asset allocation study. (93) 11.16 Describe the hedge fund business model presented by the authors. (93) 11.17 Analyze the issues in measuring the growth of the hedge fund industry. (94) 11.18 Evaluate the potential biases in hedge fund databases. (95) 11.19 Review the approach and describe the main findings of bottom-up research on hedge fund risk factors. (96) 11.20 Describe and assess the adequacy of the asset-based style (ABS) risk factor model used by Fung and Hsieh to

analyze hedge fund returns. (98) 11.21 Discuss the broader risks associated with hedge funds and describe the regulatory concerns. (99) 11.22 Describe the role of manager selection in the experience of a private equity investor. (99) 11.23 Discuss the challenges that an investor would face in measuring the risk-adjusted performance of private

equity. (100) 11.24 Explain the implication of the observation that mean and median returns on private equity databases are

significantly different. (101)

11.25 Explain and identify the potential bias in using the performance of liquidated funds to represent the overall performance of private equity funds. (101)

11.26 Compare the performance of companies in which private equity firms invest with small cap firms listed on NASDAQ. (101)

11.27 Explain the liquidity characteristics of listed private equity securities. (102) 11.28 Discuss the impacts of adjustment for stale prices on risk, return, and diversification benefits of private equity

(candidates do need to memorize exact figures). (102) 11.29 Identify the impact of IPO under-pricing on the performance of the PVCI. (103) 11.30 Explain how the following issues pose a challenge to private equity investors: (103)

a. Illiquidity. b. Parameter uncertainty.c. Absence of an investible index.d. Cross-sectional differences in private equity managers.



Copyright © 2009 Institutional Investor, Inc. All Rights Reserved.

1

TOP I C 7

Structured Products, New Productsand New Strategies

M a i n P o i n t s Explaining the structure, characteristics, benefits and risks of investing in

CFOs

Explaining the characteristics of infrastructure investments and the practicaluse of infrastructure indices

Explaining the potential for financial markets and instruments to play a role inalleviating negative climate change consequences

Comparing factor-based and pay-off distribution approaches to hedge fundreplication

Describing factors that contributed to convergence between private equity andhedge fund strategies as well as concerns and risks regarding the past trend

1. Describe the characteristics of a special purpose vehicle (SPV) in thecontext of collateralized obligations.

Special purpose vehicles (SPVs) are used as the legal entities (e.g., trusts) that form thecenter of every collateralized obligation (CO) structure. “Collateralized obligations” isthe umbrella term for a spectrum of asset-backed structures (e.g., collateralized debtobligations (CDOs) and collateralized loan obligations (CLOs)) that hold debt obligationsas collateral and are financed with “tranches” or securities that typically have diverseseniority and/or longevity.

The SPV is the entity that legally owns (holds) the collateral (the underlying debt, credit,

or other instruments), and is the entity that issues the various tranches that have claims tothe cash flows (senior, mezzanine and equity).

The SPVs are usually Delaware based business trusts or special purpose corporations. Theyare referred to as “bankruptcy remote”. This means that a bankruptcy of the sponsoring

bank or the money manager will not affect the functioning of the CO structure.

The SPVs hold the collateral and distribute the cash flows from the collateral to thetranche holders.

SPVs typically hold asset backed securities (ABS), which are bonds that are securitizedor collateralized by the cash flows from an underlying pool of assets—such as credit

cards, home loans, auto loans, equipment leases, or other non-mortgage related assets.



Topic 7: Structured Products, New Products and New Strategies


2

2. Describe the key characteristics of a collateralized fund obligation(CFO).

Collateralized fund obligations (CFOs) are the application of the collateralizedobligation (CO) concept to investing in hedge funds and started in 2002. CFOs hold portfolios of hedge funds and “repackage” the ownership of the portfolio into securities

or tranches with different levels of seniority and/or longevity.

CFOs allow investors to participate in alternative investment opportunities (typicallywith diversification) through a spectrum of CFO tranches with various maturities and risk levels that are potentially more tailored to the preferences of the investor and more easilyunderstood due to standardization and comparability to other similar programs. The debttranches may offer credit ratings and the equity tranches offer leverage.

For example, a CFO might be formed that requires that the portfolio of hedge fundsowned inside the CFO meet a number of minimum diversification requirements (e.g., 25or more funds, 20 or more managers, no more than 10% with one fund, no more than15% with one manager, etc.). The level of diversification is an important issue in

determining the relative value (and credit ratings) of the tranches or securities that haveclaim to the cash flows generated by the portfolio.

Tranches are securities sold to investors that represent claims to the cash flows from the portfolio. The tranches are usually denoted with letter names and vary in seniority fromvery low risk senior tranches to an equity tranche with high risk. For example, tranche

“A” might represent half of the value of the CFO, might offer a low coupon (e.g., LIBOR plus 60 basis points), have semi-annual coupon payments and have first priority to thecash flows from the portfolio of hedge funds. Given this high priority and substantialdiversification of the portfolio's holdings, the tranche might receive a credit rating from amajor agency of AAA.

Less senior tranches would have higher coupons and lower credit ratings. Finally, theequity tranche would be unrated and would receive the residual cash flows, if any, after the debt tranches have been satisfied. Certain requirements such as a total net value might be imposed which, if not met, trigger a liquidation (along with potential diversificationand liquidity requirements). These liquidation triggers are designed, along with thediversification, to provide protection to the senior tranches so that they can be sold withhigh credit ratings.

3. Explain the benefits and risks of investing in CFOs.

Benefits to CFOs (collateralized fund obligation) to investment company managers that

manage the CFOs can include management fees, incentive fees and gains throughownership of the equity tranche.

Benefits to CFOs (collateralized fund obligation) to hedge fund managers who view the

CFOs as investors in their hedge fund are that the money is less likely to be withdrawn.Hedge fund managers prefer investors that are relatively unlikely to withdraw funds(“sticky” money rather than “hot” money). Using the CFO structure, investors in atranche wishing to liquidate can sell their tranche without it affecting the CFO portfolioand requiring a liquidation of a portfolio holding.





3

Benefits of CFOs (collateralized fund obligations) to investors include the ability of institutions such as pension funds, insurance companies and others to diversify into the

hedge fund arena through ownership of tranches rated by a major rating agency. Often,such institutions are prohibited from direct ownership of unrated investments such as ahedge fund. Further, CFOs have been shown to have lower systematic risk exposures

(due to their absolute return strategies) than other similarly rated investments (such as pools of corporate bonds) and so are less subject to general credit market events or other market wide events. Anson reports the correlation of fund of funds returns with leveraged

loans and high yield returns of only .35 and .43. Thus, investors can receive the benefitsof somewhat diversified risk exposures that contain less systematic risk (and perhapsmore idiosyncratic risk related to funds manager skill).

Risks of CFO investing are generated from the risks of the assets (hedge funds) thatcomprise the portfolio. Anson analyzes historic returns to funds of funds and comparesthe return distributions to the distributions of high yield portfolios and leveraged loan portfolios. Anson concludes that the past return distributions have been roughly similar.The hedge fund of fund returns have moderate volatility and a good Sharpe ratio but have

a slight negative skew and slightly high kurtosis. Therefore, CFO investors are exposedto a relatively substantial risk of large negative returns. However, as noted in the previous paragraph, the correlations of the returns with large credit market events may bereasonably low and therefore CFOs provide diversification benefits.

4. Describe the structure of a collateralized commodity obligation (CCO).

The concept of collateralized obligations (COs) has been extended into commodities witha collateralized commodity obligation (CCO) being issued with rated tranches in 2005.The idea is to utilize a CO structure to facilitate exposure to commodity price risk.

The commodity price risk is accomplished in the CCO using commodity trigger swaps (CTSs). A commodity trigger swap is similar to a credit default swap except that the risk to the principal is generated by falling commodity prices (rather than a credit event). The

CCO receives fixed coupons (much like insurance premiums) up to the maturity of the

CTS at which time the CCO either receives the full principal of the CTS (if the

triggering event has not occurred) or nothing from that CTS if the triggering event hasoccurred. The triggering event is prespecified. For example, a triggering event might be if a ten day average of a particular commodity price has declined more than 35% from thecommodity price when the swap is set.

The CCO contains a diversified portfolio of CTSs and must adhere to prespecifieddiversification standards. The result is a set of tranches that offer a spectrum of

probabilities for full payment and an exposure to various commodity prices such thatsevere declines in one or more commodity prices could cause tranches to lose principal(starting with the least senior tranches).

5. Describe the conceptual characteristics of infrastructure sectors.

Mansour and Nadji describe six conceptual characteristics of infrastructure sectors. Notethat the set of characteristics of infrastructure investments is a main point of CAIA’s







5

any infrastructure investment depends on the individual asset and the stage of the asset’smaturity. See the exhibit below.

Value-Added

Core Real Estate/Fixed

Income

Opportunistic/Private

Equity

Risk

E x p e c t e d R e t u r n

Core Real Estate/Fixed Income Value-Added Opportunistic/Private Equity

Gas/Electricity/Transmission (Mature) Airports Greenfield Toll Roads

Mature Toll Roads Seaports New Telecommunications

Mature Telecom Mature Toll Roads (with Expansion) Power Generation/Transmission

Water

Source: Mansour and Nadji (2007)

7. Critique the evidence on the performance history for infrastructureinvestments.

The performance history for infrastructure investments has several limitations. These

limitations include:1. Limited Performance History.

2. Expensive and often proprietary data collection.

3. Lack of Appropriate Benchmarks.

4. Significant variation within infrastructure investments given its hybrid nature.

8. Explain how the composition and construction of the following indicesimpact their relative performance:

Note that the bulk of the material focuses on the UBS Index and Moody’sEconomy.com Infrastructure Index. Also note that benchmarking infrastructure

investments is listed as a main point for this Learning Objective, and such investmentscan be: listed infrastructure investments and unlisted infrastructure investments.The major indexes use listed companies in their construction. The article also notes astudy by Peng and Graeme (2007) that examined the performance of 19 major unlistedAustralian funds.





6

a. RREEF Hypothetical Infrastructure Index

The RREEF was constructed based on the UBS index because the UBS index was notwidely available on a global or U.S.-basis. RREEF used the UBS-Europe Index as the base, stripping out companies that did not focus on direct infrastructure such as airlinesand logistics. Hence, the RREEF index has focused primarily on pure infrastructure plays

or “infrastructure operating companies.”

Since only listed companies were used, the volatility of this series was increased butcould be directly compared to publicly-traded assets such as equities and securitized realestate. It returned 12.5% per year with a 13.2% volatility, placing it between European bonds and equities.

b. UBS Global Infrastructure & Utilities Index

The UBS indices exist for the global and major regions of the world, including the U.S.The Global series is based on a group of 85 companies.

The UBS index is about 4.6% of the S&P Global Universe. Integrated Utilities make up52% of the index, Integrated Regulated Utilities make up 25% and Energy Transmissionand Distribution make up 13%. The remaining subsets are Power Generation (5%), Water (1%) and Other Infrastructure (including communication and transport) that make up 4%.

On a 10-year basis, the UBS index averaged 12.7% less than private equity and publicreal estate but more than hedge funds, public equity, and fixed income returns. Thevolatility at 18.3% has exceeded fixed income and hedge funds but trails public real

estate and public equity.

The index has showed low correlations with traditional and alternative assets.

c. Moody’s Economy.com Infrastructure Index

Five infrastructure sectors are included: Electricity (Distribution and Generation), Water (Treatment and Distribution), Communications, Transport, and Gas (storage anddistribution).

The Electricity, Water, and Gas sectors are under Energy and Utility.

Companies are market-cap weighted. The Economy.com Index has a lower return thanthe UBS Index since its inception (5.3% for Economy.com versus 9.4% for UBS). It also

has a lower volatility than the UBS Index (13.1% for Economy.com versus 19.3% for UBS).

9. Identify risks involved with infrastructure investments.

Mansour and Nadji find six types of risks associated with infrastructure projects,including:

1. Construction Risk: Construction may be delayed or abandoned due to unforeseen risks

related to weather.





7

2. Operational Risk: Infrastructure projects may fail operationally if a chain of commanddoes not exist and is not properly supervised.

3. Leverage/Interest Rate Risk: Infrastructure investments may require additional borrowing of capital which subjects itself to interest rate risk.

4. Regulatory Risk: Infrastructure projects may be exposed to regulatory risk if new lawsinadvertently create new restrictions.

5. Legal Risk: Infrastructure investments may be exposed to legal risk if, for example,they are to require land not yet acquired.

6. Political Risk: International infrastructure investments may be exposed to the whimsof the government.

Other considerations include liquidity, pricing and benchmarking.

10. Explain the economic implications of climate change in terms of itsimpacts on existing assets, future economic activity, increased

regulation, and consumer behavior.

According to the latest Intergovernmental Panel on Climate Change (IPCC), globalwarming is a reality, and there will be an increase in the severity of weather around theglobe. Emerging and developing economies will be hit hard. Initially, the human toll will probably be highest in countries such as India, Bangladesh, South and Central America.In developed countries, meanwhile, the economic toll will be higher. The changes inclimate will have many impacts: higher cost of capital, higher insurance costs, higher

regulatory costs, and changes in consumer behavior that may have positive or negativeeffects.

The increased uncertainty associated with the weather changes will affect the planning of

future economic activity. This will increase the risk premiums from the investments.Companies that plan to build and invest in an area where the weather effects are higher can expect a higher cost of capital. Furthermore, people planning to move to such areascan expect higher insurance premiums for homes and property.

Regulations associated with the weather will increase costs. However, certain industriesare likely to benefit: e.g. construction, renewable energies, and mechanical and electric

engineering as companies attempt to meet the new regulations.

Climate change can also have an economic impact on consumer behavior. Evidencesuggests that consumers are conserving energy and cutting back on climate harmingactivities and supporting compensation measures.

11. Describe the role of financial markets in reducing the economic cost of climate change through:

There are two basic approaches for dealing with climate change: abatement strategies and adjustment strategies. Abatement strategies attempt to prevent climate change.Adjustment strategies react rationally to the unavoidable consequences of climatechange.





8

Financial markets and suitable financial instruments can help finance climate-relatedtechnology and distribute weather risks efficiently. The following list summarizes the

roles of financial markets.

a. markets for catastrophe and weather risks.

The market for catastrophe risks and weather risks offers adjustment strategies. Theseare instruments that provide compensation for certain events. Such instruments include

catastrophe risk transfer instruments such as catastrophe bonds and weather derivatives, which involve a cash flow when a certain event occurs. There are also risk-

sharing arrangements for unavoidable natural catastrophes and weather risk, which canreduce the individual cost of coverage.

The effect of these instruments is an efficient sharing of the risks that come fromunavoidable natural catastrophe and weather risks. This lowers the cost of coveringindividuals. The markets also provide information such as price signals concerningenvironmental threats.

b. emissions trading.

Emissions trading is part of an abatement strategy. The strategy sets a limit on theamount of pollution across the economy by issuing a limited supply of emissioncertificates. These certificates can be traded, which gives each corporation an incentive to produce less pollution because it can sell unused emission certificates.

Additional benefits may result from derivatives on those certificates and the existence of funds and other investment vehicles that invest in emission certificates.

The goal of emission trading is to minimize the costs associated with greenhouse gas

emission reduction. However, there is an ongoing debate concerning the potential benefits and functioning of the emissions trading market.

c. climate-related investments.

Climate-related investments are part of both abatement strategies and adjustment

strategies. Climate-related investments include public investment funds and privateequity funds that invest in assets that could profit from climate change. Such investmentswould include simply investing in the equity of companies that are developing

environmentally friendly products. Making loans to such companies would also be a partof this strategy.

There are a wide variety of companies in which to invest, e.g., those in the energyindustry, those that are developing and producing climate protection-relevanttechnologies, those that are applying climate protection-relevant technologies, and thosethat offer solutions for adapting to climate change. This strategy would be enhanced by a

political and regulatory framework that reduces the cost of debt and equity financing for the companies. Increased investor awareness would lower perceived risk and the cost of capital.





9

12. Explain the economic rationale for using financial instruments totransfer risk.

The general economic rationale for having financial instruments to transfer risk is thatrisk sharing by agents in the economy can reduce shocks to the overall economy. For onething, a decline in business for any sector will reduce tax revenues, and there will be a

drain on government funds in repairing the infrastructure. The following list providesmore details.

● Coverage of large volumes: Natural catastrophes and extreme weather events cancover large areas so that the potential losses are above the levels that a single firm or even government can afford. The financial instruments would provide payoffs to helpsupply capital to cover losses.

● Efficiency and transparency: The market can break down the risk into small piecesand distribute them among qualified investors. This would reduce the concentrationof risk among a few insurers, and the risk levels would be more transparent. Market participants would price risk to include all relevant information, which would

increase efficiency.

● Uncorrelated asset class: The new instruments would provide a new tool for increasing diversification of investment portfolios. This is especially true becauseweather-related events typically have a low correlation with market returns.

● Macroeconomic benefits: Firms can hedge risks and increase output, which helps the

overall economy. Market efficiencies should lower the cost of hedging and increasemacroeconomic benefits further.

13. Discuss the criteria that need to be fulfilled by instruments employedfor risk transfer.

There are five basic criteria for instruments to be effective in transferring risk.

1. Measurable and calculable risk: There must be estimates of both losses and probabilities in order to price the instruments, which would most likely come fromhistorical data.

2. Affordable risk premium: For the party seeking protection, the risk premium must be

affordable while still covering the potential losses.

3. Reliable payment trigger: To minimize conflicts of interest, there should be a precisedefinition of the event that triggers payment. The payment trigger must be transparent,reliable, and difficult to manipulate.

4. Avoidance of moral hazard and adverse selection: There should be informationsymmetry. This would lower the adverse selection problem where high-risk firms seek coverage at the average market price. Information symmetry would lower the potentialfor the moral hazard problem in that the covered firm would want to inflate losses.

5. Development of adequate pricing models: Traditional pricing models would require

modification to price catastrophe risks and weather risks. Weather data is verydifferent from traditional market data, for e.g., weather data has a seasonal element.





10

Furthermore, catastrophe data is sparse, and all the outcomes are not known. Theseand other considerations would have to be included in the pricing models.

14. Describe existing instruments that can be used to transfer risk andidentify potential investors and sponsors of these instruments.

The following list describes the existing instruments that can be used to transfer catastrophe and weather risks. Catastrophe bonds and exchange-traded contracts arefairly liquid, and the other instruments are tailored to meet the needs of certain entitiesand are usually held until maturity.

Catastrophe bonds (cat bonds): The coupons are usually based on LIBOR plus anappropriate risk premium, and when a predefined loss occurs, the investor forfeits thecapital invested. Special purpose vehicles (SPVs) usually issue the bonds and invest the proceeds in traditional fixed income securities to cover contingent claims by the sponsor.

Cat-risk CDO (Collateralized Debt Obligation): The various catastrophe risks are bundled and sold in individual risk tranches. One or more events must occur before theinvestor suffers a loss.

Capital market-financed quota share reinsurance, known as sidecars: In this contract, theinvestors share proportionally in a loss according to a predetermined quota. This uses

tranches as well, and there is at least one debt and equity tranche.

Industry loss warrants (ILW): This market, that has been around for a while, is usually inthe form of private placements. It is a type of capital market-financed loss (re-)insurance,

which is linked to an industry loss index.

Event loss swaps (ELS): These are a variant of conventional ILWs. They are more tradable because they are more highly standardized.

Catastrophe swaps (cat swaps): These are contracts where two insurers can swap generallyuncorrelated risks, such as those between different regions or industries.

Contingent capital arrangements: This category is composed of types of put options.The option buyer has the right to raise debt or equity capital or sell assets under specificterms if a given loss occurs. One use of this would be by a firm that would want to makesure it has adequate capital in the event of a loss. They were popular in the 1990s, but

because they are difficult to price, they are not used much today.

Exchange-traded contracts in catastrophe risks: Some cat futures and cat options tradeon an exchange. They started in the early 1990s, but turnover was small. There have been moves to modify the contracts to generate more interest. Since they trade on an

exchange, they offer more liquidity, and they would be used by entities where liquidity isimportant.

The investors in these instruments must be knowledgeable, which limits potentialinvestors to insurers and reinsurers, institutional investors, and hedge funds. Insurers andreinsurers use the contracts as part of their overall portfolio strategy. Hedge funds make

investments to earn the premiums. Mutual funds using these instruments are beingdeveloped so that more investors can participate.





11

The “sponsors” are those that issue the contracts for protection. The largest sponsorsare insurers and reinsurers, corporations with a high exposure, and government

insurance and development funds. Insurance companies that have taken on the risks of companies with insurance contracts are a large group of sponsors that use thecontracts to manage their risk. Corporations with high-risk exposure naturally hedge

risk so they can focus on their business. There are government insurance anddevelopment funds in many countries that are sponsors, e.g., Mexico’s naturalcatastrophe fund (FONDEN) recently transferred large amounts of earthquake risks to

the capital markets using catastrophe bonds.

15. Describe both exchange traded as well as over-the-counter weatherderivatives.

The market for weather derivatives is concerned with relatively low-cost high probabilityevents. This is in contrast to the market for catastrophe risks that covers high-cost low probability events. Weather derivatives pay off when there are unusually low or high

temperatures. A natural gas company may wish to hedge against a warm winter, for example, which lowers the quantity demanded of natural gas used for heating.

In the OTC market, the contracts are negotiated individually and with properties specified by the counterparties. These contracts began in the mid-1990s. Tradable weather-relatedfutures and options have been on the Chicago Mercantile Exchange (CME) since 1998.Other exchanges have offered these products, but have discontinued them for now,

through some are planning to introduce new products. As of now, however, the CME isthe only exchange where weather-related futures and options trade, and the exchange plans to expand its offering. Market participants find that the exchange-traded productshave a lower cost and higher liquidity. There has been an increase in the turnover inexchange-traded contracts at the CME relative to that of the OTC market.

16. Describe emissions trading, its project-based mechanism, and itspotential market participants.

The biggest market for greenhouse gas emissions is the EU Emission Trading System (EU-ETS). The EU-ETS uses targets proposed by the Kyoto Protocol, which defines anumber of different emission certificates. There is a distinction, for example, between

emission rights and emission credits. There are a limited number of emission rights for all companies, and these rights can be traded among companies that emit the greenhouse

gases. This is referred to as cap and trade, in that there is a limit or cap to the emissionsand the right to emit can be traded. The limited number includes the EU Allowances

(EUAs), traded in the EU-ETS, and includes the assigned amount units (AAUs) thattrade internationally.

With respect to emission credits, on the other hand, investors can have credits fromadditional climate protection projects that are in other countries credited to their ownreduction target (baseline and credit). Also, with respect to emission credits, there is adifference when the reductions take place in an industrial country or in an emergingmarket. For the industrial country, the resulting certificates are called emission reduction





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units (ERUs). For emerging markets, they are called certified emission reductions(CERs).

The Kyoto Protocol also allows for the realization of carbon-sink projects at home suchas afforestation. This is done with the use of removal units (RMUs). Also, it is possibleto generate tradable project-based credits called verified emission reductions (VERs).

They differ from CERs and ERUs in that VERs can only be used for voluntary CO2compensation.

The CERs and ERUs are useful for the exceptions the Kyoto Protocol extends toemerging markets. This is because emerging markets are exempt from greenhouse gasesquantitative reduction commitments. The Kyoto Protocol’s project-based mechanismsallow the CERs and ERUs from additional climate protection projects in third countries

to be credited to the owner’s reduction target within certain limits, e.g., down to 50% of the initial target.

The Clean Development Mechanism (CDM) of the Kyoto Protocol allows for investment to be made in a project that promises to yield future income in the form of

CERs. A wide variety of projects qualify, and the CERs can be generated from a portfolio of projects. The rights to future CERs are traded at a discount, which is afunction of the project’s stage of progress. A less advanced project would have a higher discount.

Potential market participants include carbon funds, which include government

purchasing programs and private commercial funds. The Prototype Carbon Fund (PCF),launched by the World Bank, was one of the earliest funds. It gathered experience withthe new emissions trading instruments and prepared the market for later funds. Anincreasing number of investment banks, brokers and institutional investors are buying andselling certificates for their own or third-party accounts; however, companies that have to

meet reduction commitments within the EU-ETS framework are still the biggest group of end buyers (compliance buyers).

Investors who have no direct involvement with emissions can attempt to earn a positivereturn in the market for these types of instruments. Carbon funds offer advantages over adirect investment because the funds have an expertise in the area, offer diversification,and can allow the investors to take on a particular level of risk via the number of shares purchased in the fund.

There has been increasing product differentiation, and new possibilities are opening upfor investors. Investors can place bets on rising prices through derivative instruments onemission certificates or participate in the realization of CDM projects.

17. Compare the factor-based approach to hedge fund replication withthe payoff distribution approach to hedge fund replication, in terms of their:

a. goals.

The ultimate goal of both the factor-replication approach (or the factor-based

approach) and the payoff distribution approach is to create a portfolio with





13

characteristics similar to a particular hedge fund, for e.g., to have the same risk profile, and earn returns similar to those of the hedge fund at a lower cost. To

achieve that ultimate goal, the factor-based approach has the goal of replicatingthe hedge fund’s returns using hedge fund risk factors. The payoff distribution

approach attempts to replicate the hedge fund’s returns by matching the

unconditional higher moments, which should then give the same first moment, i.e.,the same average return.

b. methodology.

The factor-based approach focuses on the conditional distribution to earn theconditional mean of a hedge fund, given values of the underlying risk factors. Thisrequires a two-step approach.

Step 1 requires the calibration of a satisfactory factor model for hedge fund returns. Thisis essentially estimating a factor model:

Hedge Fund “k” Returnt = B0 + B1F1,t + B2F2,t + . . . + B NF N,t + et where each Fi,t is the value of factor “i” at time “t”, and each Bi is the correspondingfactor sensitivity.

A stepwise regression is often used in this process, but it does not allow for the researcher to make inferences. A stepwise regression is a regression technique that allows for

forward selection of relevant factors or backward elimination of irrelevant factors.Forward selection starts with no factors and, at each step, the most significant factor isadded to the model. Backward elimination starts with a set of factors and, at each stage,the least significant factor is removed.

Another approach is to use a conditional factor model which allows the coefficients, Bi,

to be time varying, too. The goal is to capture time varying factor exposures. Another approach is to use non-linear factor models that may also be able to better capture therelationship and make better out-of-sample forecasts.

There is also return-based style (RBS) analysis, which examines the exposure of hedgefunds to certain style factors. While this approach allows for lower specification risk, thekey issue is the efficacy of the factors in building mimicking portfolios.

Once having chosen a particular methodology in Step 1, Step 2 requires the identification

of the replicating factor strategy (RFS), which is creating a clone of the hedge fund returnusing the estimated coefficients and the out-of-sample values of the factors.

The payoff distribution approach focuses on creating a clone portfolio where, for all x,

Pr(Clone return<x) = Pr(Hedge Fund return < x).

This also requires a two-step process: Step 1 consists of estimating a payoff function that

maps an index return onto a hedge fund return. Step 2 consists of pricing the payoffs andderiving the replicating factor strategy, which is done using the Merton (1973) replicating portfolio interpretation of the Black and Scholes (1973) formula.





14

c. ability to replicate hedge fund returns.

The factor-based approach, while the most natural and straightforward way to approachthe replication problem, has failed in thorough empirical tests to produce satisfactoryresults on an out-of-sample basis. The payoff distribution approach produces satisfyingresults for long-run out-of-sample returns but cannot capture short-run time-series

properties. Also, the approach does not attempt to match the first moment (the mean),which is crucial to any investment analysis.

d. benefits.

The factor-based model addresses the essence of the problem, which is to find details of the risk exposures. The payoff distribution approach has the benefit of doing a better job of replicating return, at least, in the long run.

e. drawbacks.

Neither approach has produced satisfactory results. With the factor-based approach,there is a difficulty in identifying the correct factors and replicating the dynamic exposureto the factors. We should recall that simple regression techniques only capture the pastaverage exposures of the managers.

Also, any factor analysis can suffer from specification risk. This is the result of not usingan accurate mix of factors. Either the omission of factors or including too many factorscan lower the accuracy of the model.

The payoff distribution property has some success in replicating long-run returns.However, it fails to replicate the short-run time-series characteristics.

18. Discuss the term convergence as it is applied to the alternativeinvestments industry.

The term convergence is used to define the blurring of the lines between hedge fund and private equity investing. In this context, the authors refer to actual transactions pursed by both hedge fund managers as well as private equity managers. The objective of bothhedge fund and private equity investors is to pursue “manager skill” or “alpha” rather than market exposure.

19. Compare and contrast the historical objectives of private equity fundswith that of hedge funds.

There are several distinctions in the historical objectives of hedge fund managers versus private equity managers, as noted by the authors. The exhibit below classifies theseobjectives into three categories (Securities employed, Strategy pursued and Sources of returns) and compares these objectives.





15

Distinction Hedge Fund Private Equity

Securities Listed Unregistered, Private

Strategy Long or Short Ownership to obtain voting control

Sources of Return

1. Arbitrage Opportunities,

2. Superior Security Selection

3. Provision of Liquidity notgenerally available to theMarket

1. Control of the Underlying Business

Strategy and ManagementComposition

2. Alignment of Economic Interestsof Management and Shareholdersand

3. Access to types of Non-publicinformation that listed shareinvestments cannot provide.

Source: Gonzalez-Heres and Beinkampen (2006)

20. Contrast recent hedge fund participation in traditional private equityactivities with recent private equity participation in traditional hedgefunds activities.

Gonzalez-Heres and Beinkampen note that hedge funds use side pockets within existinghedge fund vehicles to participate in private equity activities. Hedge funds also establish

direct lending businesses that function much like a bank or mezzanine fund.

In contrast, private equity funds set up units under the same roof to pursue hedge fundinvesting. The authors mention five examples. Some have detailed information on their

websites. These include:

1. Blackstone Group (http://www.blackstone.com/company/index.html): A private equityspecialist, with five general private equity funds and one specialized fund focusing onmedia and communications-related investments. Blackstone also manages hedge fundssuch as Blackstone Kailix Advisors that invests primarily in equity investments on along and short basis.

2. Texas Pacific Group (http://www.texaspacificgroup.com/): A global privateinvestment firm with over $30 billion of capital under management. It manages afamily of funds including private equity, venture capital and public equity and debt

investing.

3. Fortress Investment Group (http://www.fortressinv.com/): A private equity specialistthat makes significant, control-oriented investments in North America and WesternEurope, with a focus on acquiring and building asset-based businesses with significantcash flows. It also runs hedge funds: hybrid hedge funds and liquid hedge funds.

4. Bain Capital (http://www.baincapital.com): A private investment firm whose affiliatesmanage over $50 billion in assets. It has investments in private equity and venturecapital as well as long/short public equity, credit products, and global macro hedgefunds.





16

5. H.I.G. (http://www.higcapital.com/): A private investment specialist that manages private equity, venture capital, distressed debt, and public equities products.

21. Explain why the distressed investment space provides an excellentexample of recent convergence of hedge fund and private equity

strategies.

The distressed space provides an excellent example of recent convergence because bothhedge fund investors and private equity investors have expanded their mandates.

A typical distressed hedge fund manager evaluated investment opportunities more like adebt investor and invested in publicly traded securities. He/she may or may not have played a significant role in negotiating a restructuring of the issuing company. The

objective was to identify securities of companies at various stages of the bankruptcy process (including companies on the verge of filing for bankruptcy or just emerging from bankruptcy), that are publicly traded at a discount to their intrinsic values. Exit generallycame through selling the appreciated security in the public market or to a strategic

acquirer. Capital was in lock-up status for one or two years.

Private equity managers, on the other hand, are “active” investors that typically acquire amajority interest in a company in order to get operating control and run the business. Thekey difference is that they play a significant role in the operational turnaround andrestructuring of the issuing company. They often sell the company at a much later date for

a profit via an initial public offering (IPO) or sale to strategic investors.

However in recent times, hedge fund managers have expanded from “passive investing”to “buy-to-own” investing. They are now acquiring sizable stakes with the mindset of owning the business rather than trading the securities. They have also taken a “lend-to-

own” debt financing approach. This entails providing debt financing, usually to highly

levered companies and in situations where the fund is indifferent about whether return isgenerated from interest or principal repayments or from a hands-on operationalturnaround if the company defaults.

Private equity fund managers, on the other hand, are increasingly taking “toehold

positions” in order to identify opportunities. Even if they are ultimately unsuccessful ingaining control, they recognize that material gains can be realized from these toehold

positions. In other words, they are poaching each other’s strategies.

22. Describe the emergence of the hybrid hedge fund/private equity fund.

While Gonzalez-Heres and Beinkampen offer some general observations, theyspecifically describe a multi-billion-dollar hedge fund manager that their team has had alongstanding relationship with since the mid-1990s. This manager is a multi-strategymanager. The manager started out as a convertible arbitrage manager but transformed

into a passive distressed manager by the end of 2001 and then morphed into a hybrid

fund over the last 18 months. The authors note that this was done by investing away fromliquid distressed debt situations and toward more illiquid private assets, ranging fromsmall- and mid-sized companies to physical aircrafts. Currently, the fund isapproximately one-third private equity and two-thirds hedge fund.





17

During the 2001-2002 period, there was a large wave of corporate bankruptcies. Themanager took advantage of these opportunities by focusing on passive (non-operational)

hedge fund-style distressed investing. By mid-2004, when these opportunities vanished,this manager felt that there were “gaps” that were overlooked by both hedge fund and private equity managers. These situations, however, required three to five years to

produce attractive returns. After conferring with investors and getting their consent, themanager shifted towards a more private equity-oriented approach.

In the situations above, the ability of the manager to adapt and migrate quickly to wherethe opportunities lay, led to strong returns. The ability to offer both hedge fund and private equity products are referred to as hybrid funds. The authors note that in late 2005and early 2006, several private equity managers that they had longstanding relationships

with indicated a desire to launch “sister” hedge fund products. Research conducted by private equity managers as they seek opportunities often led them to uncover opportunities in public markets. Alternatively, large positions in private companies oftenmay lead managers to discover unique insights into the health and stability of other companies or industries.

23. Discuss the factors that contributed to the convergence of privateequity and hedge fund strategies referencing recent trends in the area.

There are several factors that have contributed to the convergence of private equity andhedge fund strategies.

First, the surge of capital that has flowed into hedge funds and away from private equityhas put downward pressure on returns. It has also forced managers to look into privateequity opportunities where venture capital slowed significantly after the Internet bubble burst in 2001.

Second, because corporate defaults since 2004 have been at near historical lows,opportunities for distressed and private equity managers have been limited.

Third, the limited opportunities have led distressed hedge fund managers to pursue other opportunities such as leveraged buyouts. A record $149 billion was raised for leveraged

buyouts in 2005. Hedge fund managers watching these capital flows have beenencouraged by some of the enthusiasm in this space.

Fourth, private equity funds have been able to persuade corporate boards to back their transactions using the allure of stable capital, significant savings in time and money, andthe avoidance of scrutiny that comes with going private. This is because the corporate board of a private company is no longer compelled to comply with the Sarbanes-Oxley

Act, nor does it have to answer to a multitude of shareholder constituencies.

Fifth, the compensation structure of many hedge funds provides incentives for the fund toinvest in higher yielding, illiquid securities traditionally purchased by private equity firms

over the short term. Unlike private equity firms where the manager is typically paid a performance fee only after all invested capital is returned to investors, hedge funds havetraditionally been compensated on an annual basis.






19

TOP I C 8

Asset Allocation

Main Po in t s Comparing and contrasting buy-and-hold, constant mix, and constant-

proportion portfolio insurance strategies

Applying a wealth allocation framework that accounts for various dimensionsof risk and deriving an ideal asset allocation for an individual

Interpreting the term structure of futures prices, the components of futuresreturns for individual contracts, returns for portfolios constructed andrebalanced with various methods, and the implications of tactical assetallocation strategies using commodity futures contracts

Critically examining the methods of including global commercial real estate ina strategic asset allocation

1. Calculate the portfolio’s asset values after a given change in theequity value, using:

a. buy-and-hold.

Perold and Sharpe consider various rebalancing strategies between a risk free bond and

the stock market (with interest rates set to zero for simplicity). There are three primarystrategies discussed as summarized below:

Strategy Name Rebalancing inUp Market

Rebalancing inDown Market

Shape of Payoff v. Stock Market

Buy-and-hold None None Linear

Constant Mix Sell Stock Buy Stock Concave

Constant Proportion

Portfolio Insurance Buy Stock Sell Stock Convex

Consider for example an investor with $100 starting value allocating all of the funds between two choices: risk free bonds (interest rate equals zero for simplicity) and a single

risky portfolio (the stock market). Assume that the investor’s initial allocation is to put$70 in stock and $30 in bonds. The stock market is indexed to 100.0

Under a buy-and-hold strategy there is no rebalancing. The “Up” panel below shows thevalue of the portfolio in an up market with the market index rising



Topic 8: Asset Allocation


20

10 points each period for three consecutive periods. The “Down” panel below shows thevalue of the portfolio in an analogous down market. Any funds used to purchase stocks

come from bonds and vice versa.

“Up” Market Panel: Buy-and-hold:

Before Balance After BalanceTime

Stock Level

Bonds StocksStock Bought(+)

or Sold (-)Bonds Stocks Total

0 100.0 30 70 0 30 70 100

1 110.0 30 77 0 30 77 107

2 120.0 30 84 0 30 84 114

3 130.0 30 91 0 30 91 121

“Down” Market Panel: Buy-and-Hold:

Before Balance After Balance

TimeStock

LevelBonds Stocks

Stock Bought(+)


0 100.0 30 70 0 30 70 100

1 90.0 30 63 0 30 63 93

2 80.0 30 56 0 30 56 86

3 70.0 30 49 0 30 49 79

Note that there are no transactions since the strategy is buy and hold. Further note that thetotal value is linear – it changes by the same dollar amount for each equal dollar movement in the stock market. The value of the portfolio (last column on right) changes

$7 for each 10 point change in the stock market index when it is 70% initially invested inthe stock market and there is no rebalancing.

b. constant mix.

Perold and Sharpe consider various rebalancing strategies between a risk free bond andthe stock market (with interest rates set to zero for simplicity). There are three primarystrategies discussed as summarized below:






Constant Proportion

Portfolio Insurance Buy Stock Sell Stock Convex

Under a “Constant Mix” strategy there is periodic rebalancing such that the portfolio is

returned to being, in this case, 70% stocks and 30% bonds. The “Up” panel below showsthe value of the portfolio in an up market with the market index rising 10 points each period for three consecutive periods. The “Down” panel below shows the value of the portfolio in an analogous down market. Any funds used to purchase stocks come from bonds and vice versa.





21

“Up” Market Panel: Constant Mix:


TimeStock Level



0 100.0 30 70 0 30 70 100

1 110.0 30 77 -2.10 32.10 74.90 107.002 120.0 32.10 81.71 -2.04 34.14 79.67 113.81

3 130.0 34.14 86.31 -1.99 36.13 84.31 120.45

“Down” Market Panel: Constant Mix:


TimeStock Level



0 100.0 30 70 0 30 70 100

1 90.0 30 63 +2.10 27.90 65.10 93.00

2 80.0 27.90 57.87 +2.17 25.73 60.04 85.77

3 70.0 25.73 52.54 +2.25 23.48 54.78 78.26

The $2.10 sale of stocks (and purchase of bonds) in time period 1 of the “Up panel” is arebalancing such that the new value of the portfolio ($107) remains 70% allocated tostocks. Stocks are sold as the stock market trends up to prevent “underweighting” in bonds. In the downward panel, stocks are purchased as the stock market trends down to prevent “underweighting” of stock.

Very importantly, note that the total value is non-linear – it changes by smaller dollar amounts for each equal upward dollar movement in the stock market. The value of the portfolio rises $7 for the first upward 10-point change in the stock market index but rises byonly $6.81 for the second 10-point change (the rebalanced stock holding does not rise by $7 because the 10-point stock rise is a smaller percentage stock price rise than it was when the

stock level was lower). Conversely, rebalancing to the stock market while it is falling produces larger losses than in previous periods or in the buy-and-hold strategy (note that each10-point decline in the market index represents a higher percentage decline).

Viewed on a graph with total portfolio value on the vertical axis and stock market indexvalues on the horizontal level, the Constant Mix strategy forms a concave shape. The

buy-and-hold strategy forms a straight line.

c. constant-proportion portfolio insurance.

Perold and Sharpe consider various rebalancing strategies between a risk free bond andthe stock market (with interest rates set to zero for simplicity). There are three primary

strategies discussed as summarized below:






Constant ProportionPortfolio Insurance Buy Stock Sell Stock Convex





22

Under a “Constant-Proportion Portfolio Insurance” (CPPI) strategy the investor sets a

floor value at which all risky investing terminates. Further, the investor increases risky

assets holding when the market rises and decreases risky asset holdings when the marketfalls.

For example, consider that the investor sets a floor value of $50 but invests 150% of the total

portfolio value in excess of this floor in stock. Thus, the investor starts with a total value of $100 allocated $25 to bonds and $75 to stock. At the end of each period, the investor resetsthe stock allocation so that it is 150% of the excess, if any, by which the total portfolioexceeds the floor ($50). Any funds used to purchase stocks come from bonds and vice versa.

“Up” Market Panel: CPPI:


TimeStock Level



0 100.0 25 75 0 25 75 100

1 110.0 25 82.50 +3.75 21.25 86.25 107.50

2 120.0 21.25 94.09 +3.92 17.33 98.01 115.343 130.0 17.33 106.18 +4.08 13.25 110.26 123.51

“Down” Market Panel: CPPI:


TimeStock Level



0 100.0 25 75 0 25 75 100

1 90.0 25 67.50 -3.75 28.75 63.75 92.50

2 80.0 28.75 56.67 -3.54 32.29 53.13 85.42

3 70.0 32.29 46.49 -3.32 35.61 43.16 78.78

The $3.75 purchase of stocks (and sale of bonds) in time period 1 of the “Up panel” is arebalancing such that the new allocation increases its “bet” on stocks. Stocks are boughtas the stock market trends up to try to achieve massive gains. In the downward panel,

stocks are aggressively sold as the stock market trends down to prevent larger losses andto insure that the floor value ($75) is protected.

Very importantly, note that the total value is non-linear – it changes by larger dollar amounts for each equal upward dollar movement in the stock market. The value of the portfolio rises $7.50 for the first upward 10-point change in the stock market index andrises $7.84 for the second 10-point change (since the strategy placed 150% of “profits” in

stock). Conversely, rebalancing away from the stock market while it is falling producessmaller losses than in previous periods. Over the long-term, the floor should increase, so

that the relationship between the initial floor and a floor at time “t” is

Ft =F0ert

where Ft, F0, r, and t are are the floor value of the portfolio at time t, the floor value atinitiation of the strategy (t=0), the risk-free rate, and a time index.

At a given point in time, viewed on a graph with total portfolio value on the vertical axisand stock market index values on the horizontal level, the CPPI strategy forms a convex





23

shape. The buy-and-hold strategy forms a straight line and the constant mix strategyforms a concave shape.

2. Compare the payoff and exposure diagrams of the buy-and-hold,constant mix, constant-proportion portfolio insurance, and option-based portfolio insurance strategies.

A payoff diagram in the context of the article by Period and Sharpe, is a graph of therelationship between total portfolio (asset) value on the vertical axis and stock marketindex value (performance of the risky asset class) on the horizontal axis. Simply put, ittells the investor the profit and loss of his or her entire portfolio in relationship to

movement in the stock market.

For example, consider a buy-and-hold strategy that purchases a particular combination(e.g., 50%/50%) mix of a risky asset (stocks) and a risk free asset (bonds). The bond

values are assumed constant and for simplicity do not even pay interest. The buy-and-

hold strategy does not rebalance, so the stock position simply grows and shrinks linearlywith the stock market as depicted below. The slope of the line depends on the originalmix, but the relationship is linear regardless of initial mix

Buy & Hold

|

| X

| X

| X

| X

| X

TotalPortfolioValue | X

| X

| X|_X_____________________________________________________

Value of the Stock Market

Now consider a constant proportion strategy that sells stock in a rising market to maintainthe desired mix and buys stock in a declining market similarly to maintain a desired mix.The payoff diagram will demonstrate a concave relationship as indicated below:

Constant Mix

|| X| X

| X| X| X

TotalPortfolioValue | X

| X| X|__ _____________________________________________________






24

Now consider a Constant-Proportion Portfolio Insurance (CPPI) strategy that buysstock in a rising market and sells stock in a declining market (to protect a floor value).

The payoff diagram will demonstrate a convex relationship as indicated below:

CPPI strategy

| X

|

| X

| X

| X

| X

TotalPortfolio

Value | X

| X

| X

X

|_ ______________________________________________________


Finally, consider an option-based portfolio insurance strategy that owns a constantstock position in a rising market and sells all stock if a lower floor is reached in adeclining market (to protect a floor value). The payoff diagram will demonstrate a kinked but otherwise linear relationship similar to the traditional diagram of a call options and as

indicated below:

Option-based portfolio insurance strategy that owns a constantstock position in a rising market and sells all stock if a lower floor isreached in a declining market|| X

| X| X| X|X X X X

TotalPortfolioValue |

|||__ _____________________________________________________


In summary, the key to the payoff diagrams is that they show the linearity, concavity,convexity and call-option-like relationships of the four strategies as reviewed above. Theseshapes are important in understanding behavior in trending versus reverting markets.

An exposure diagram in the context of the article by Period and Sharpe, is a graph of the relationship between desired stock position (amount of risk) on the vertical axisand total portfolio value on the horizontal axis. Simply put, it tells the investor therisk exposure of the portfolio in relationship to the total portfolio’s cumulative performance.





25

For example, consider a buy-and-hold strategy that purchases a particular combination(e.g., 50%/50%) mix of a risky asset (stocks) and a risk free asset (bonds). The bond

values are assumes constant and for simplicity do not even pay interest. The buy-and-hold strategy does not rebalance, so the stock position simply grows and shrinks linearlywith the stock market with a lower bound equal to the bond position as depicted below.

The location of the line on the horizontal axis depends on the original mix and bond position but is linear regardless of initial mix. The key to the diagram is that it has amoderate slope.

Buy & Hold

| X

| X

| X

| X

| X

| X

Portfolio’sStock

Value | X| X

| X

|_____________________________________________________

Value of the Total Portfolio

Now consider a constant proportion strategy that sells stock in a rising market to maintainthe desired mix and buys stock in a declining market similarly to maintain a desired mix.

The key to the diagram is that it has a moderate slope.

Constant proportion strategy

||

|

|

| X

| X

Portfolio’sStock Value | X

| X

| X

|_X___________________________________________________






26

Now consider a Constant-Proportion Portfolio Insurance (CPPI) strategy that buysstock in a rising market and sells stock in a declining market (to protect a floor

value). The exposure diagram will demonstrate a steeply sloped relationship asindicated below:

CPPI strategy

| X

| X

| X

| X

| X

| X


| X

| X

|______________ X __________________________________

Value of the Total PortfolioFinally, consider an option-based portfolio insurance strategy that owns a constantstock position in a rising market and sells all stock if a lower floor is reached in adeclining market (to protect a floor value). The exposure diagram will demonstrate a

steep and curved relationship as indicated below:

Option-based portfolio insurance strategy that owns a constant stock position in a rising market and sells all stock if a lower floor is reachedin a declining market

|

|

| X| X

| X

| X


| X

| X

|__________________ __________X________________________


3. Determine the expected performance and cost of implementing

strategies with concave payoff curves relative to those with convexpayoff curves under: a. trending markets b. flat (but oscillating)markets.

Concavity in the context of the Perold and Sharpe study refers to the tendency of astrategy to decrease equity exposure (risk) as the equity market rises and to increaseequity exposure as the equity market falls. An example is a constant mix strategy thatsells stock in a rising stock market to keep the stock’s value a constant proportion of

the portfolio. Convexity refers to the tendency of a strategy to increase equity





27

exposure (risk) as the equity market rises and to decrease equity exposure as theequity market falls. An example is a CPPI (Constant-Proportion Portfolio

Insurance strategy) that is highly aggressive with profits, but is quick to reduce stock exposure when nearing a lower bound “floor” value.

The expected performance of a strategy is directly associated with the strategies’ payoff

curves. A linear (buy-and-hold) strategy simply makes or loses money based on the terminal performance of the underlying stocks. However, concave strategies perform relatively well inflat (but oscillating) markets while convex strategies perform well in trending markets.

a. trending markets.

A trending market works in favor of a convex strategy because as markets rise the stock

exposure is greatly increase, producing high relative profits if the up trend is substantial andsustained. Conversely, in a downtrend the stocks are sold off, mitigating the losses. A concavestrategy’s relative performance is the opposite. A concave strategy such as a constant mix strategy liquidates stocks into a rally and buys additional stock throughout a long decline.

b. flat (but oscillating) markets.

A flat but oscillating market favors concave strategies and hurts convex strategies. A concavestrategy will buy after a decline (e.g., to keep a target mix) and then profit in a reversal. A

convex strategy will get whipsawed – selling after the decline and buying after the rise. Thus,the curvature explains the expected performance and costs of the strategy due to thesubsequent nature of the market in the sense of whether the market will tend to trendmore, or oscillate more.

The following equations can help in describing the payoffs and equity weights for thevarious strategies. The role of the variance in the constant proportion strategy is evidentfrom the payoff equation for that strategy:

Vt = Ft + (V0 - F0) [(It/I0)m]e(1-m)(r+0.5*m*variance)t

where Vt, Ft, It, m and r are are the value of the portfolio, the floor value of the portfolio,the value of the market index, the multiplier, and the risk-free rate, respectively. Thesubscripts 0 and t stand for the beginning value and the value at time t respectively.

The amount of equity (risky assets) held in the CPPI strategy is:

Equity = m (At – Ft),

or

Equity = MIN(A, m( At – Ft)) if no leverage is allowed.

For the buy-and-hold strategy, the payoff equation reduces to

Vt = Ft + (V0 – F0)(It/I0),

and the amount of the equity held is

Equity = (At – Ft).





28

4. Discuss the motivations for and impact of resetting the parameters of dynamic strategies.

Parameters of dynamic asset allocation strategies can adjust exposures to risky assets atvarious levels, can adjust minimum desired asset levels (cushions and floor values) andso forth. Some dynamic strategies, such as option-based portfolio insurance, require

resetting of parameters at horizon points. Other dynamic strategies such as constant mix require transactions but not resetting of parameters.

By adjusting the parameters, the risk exposures and payoffs of the strategies can bealtered. For example, the option based portfolio insurance strategy may be viewed as aspecial case of the Constant-Proportion Portfolio Insurance in which parameterschange with levels of the cushion. Further, the CPPI strategy can be transformed into a

constant mix strategy by constantly adjusting the floor to a specified percentage of theasset values.

Resetting of parameters can allow the portfolio allocator to make substantial changes inthe exposures and payoffs of the strategy – potentially changing the entire character of

the strategy as illustrated above by the ability to transform one strategy into another through constant parameter adjustment. In particular, parameter resetting can beappropriate for horizon points or after major market movements.

5. Describe examples of undiversified “strategies” that have allowedindividuals to become wealthy.

Undiversified strategies that have worked for a minority of investors include:

1. using leverage in real estate by assuming a mortgage;

2. accumulation of low-basis stock and stock options and not diversifying; and

3. starting a small business.

In each case, there is the potential for large returns if the conditions are favorable, for e.g., a 20% down payment on a home provides a 50% return if the home increases invalue by only 10%. It should be noted that a large portion of those that have becomewealthy have used these strategies, but this can be misleading, because there are manyinvestors that used these strategies and did very poorly.

6. Describe changes in the financial system have thrust more responsibilityupon individuals with regard to wealth management and asset allocation.

The primary change that has thrust more responsibility upon individuals is the movement

from defined benefit to defined contribution plans. This means that market risk hasmoved from the sponsoring firm to the individual. The following factors have alsoincreased the level of responsibility that individuals have for their own retirement:

1. estimating life expectancy and the general increase in life expectancy (with a defined benefit plan, the sponsor pays as long as the individual is alive);

2. achieving diversification from funds whose returns tend to have higher levels of correlation; and





29

3. choosing among a wider range of possible products, for e.g., derivatives, structured products.

7. Explain and apply the concept of personal risk and its various componentsto the asset allocation problem faced by individuals.

Personal risk comes from the unique characteristics of the investor that can amplify the levelof risk well above that implied by the basic market statistics. For example, the historicalmarket standard deviation can imply a much higher level of risk for an older individual or a person with less job security. The components of personal risk are listed below.

1. Cash flows: the estimated net inflows and savings and the variability of those cash flows.

2. Lifecycle stage: the stage of earning power and the desire to leave a legacy.

3. Ability to weather shortfalls: ability to adjust to unexpected declines in assets and/or

living longer than expected.

4. Event risk: ability to adjust to events like the loss of a job, health problems, market

crashes, etc.One way to think of personal risk in contrast to a traditional risk measure is to consider how well an individual can endure under extreme shocks, e.g., a move three or morestandard deviations from the mean, which could reduce a portfolio to zero. Moregenerally, the investor might have a target minimum level of wealth, and a shock couldlower the portfolio’s value below that number, which could be devastating to the investor on either a real or psychological level or both. With respect to such a shock, one

application of the personal risk approach is to use Roy’s Safety First criterion whenaddressing the ability to weather shortfalls.

With respect to cash flows, an application of the personal risk approach is to allow the

outflows of cash to vary directly with the changing value of the portfolio. That is, theinvestor should not take the same amount out each period, especially in a down-trendingmarket.

Taking out insurance policies is an application of the personal risk approach to addressevent risk.

8. Explain and apply the wealth allocation framework that accounts forvarious dimensions of risk and leads to an ideal portfolio that provides:

Note: By analyzing actual human behavior, Kahneman and Tversky (1979) found thatinvestors attempt to compose a portfolio that protects from anxiety, that has a high

probability of maintaining one’s standard of living, and provides for the possibility of increasing wealth. The three dimensions of risk that the ideal portfolio must address are:one, personal risk that can lower the level of lifestyle; two, market risk to increasereturns, and three, aspirational risk associated with enhancing one’s lifestyle.

a. the certainty of protection from anxiety.

Personal risk refers to the possibility of a fall in the investor’s lifestyle and the resultinganxiety associated with that possibility. The investor can address this risk by applying a





30

variety of strategies. Those strategies include incurring expenses that limit downside risk,e.g., purchasing puts. Another type of strategy is to simply make an allocation of wealth

to a very safe asset such as Treasury Bills. Such portfolio decisions are said to be makingan allocation to the investor’s personal risk bucket.

b. the high probability of maintaining one’s standard of living.Market risk refers to the possibility of the investor being able to at least maintain acertain standard of living or even improve it moderately with the market returns.Addressing this issue is referred to as making an allocation to the investor’s market risk bucket. In most cases, this would be the largest allocation and would be made to standardmarket assets such as stocks and bonds.

c. the possibility of substantially moving upward in the wealth spectrum.

Aspirational risk is associated with the possibility of significantly enhancing one’slifestyle. Allocations in this area are called allocations to the aspirational risk bucket.They would include purchasing call options on market assets or purchasing assets withlarge risk but also the high possibility of returns such as small capitalization stocks.Allocations to the aspirational risk bucket would be relatively small compared to the

market risk bucket.

In summary, a wealth allocation framework that accounts for these three dimensionswould provide a safety net on the downside, a large exposure to market risk with itscommensurate returns, and an investment in a high-risk asset that can provide very highreturns in a positive market environment.

9. Develop and justify an asset and risk allocation for an individual usinginformation provided to the candidate during the examination.

The following are examples of the types of investments that an individual would make toaddress the three dimensions of risk.

1. Personal risk bucket: make an allocation to the risk free asset or buy put options.Both of these will lower return under average market conditions, but will provide benefits if the market falls below certain thresholds.

2. Market risk bucket: after making allocations to the personal and aspirational risk

bucket, investing the remainder in a well diversified portfolio of conventional assetsthat provide a return commensurate with market risk and low idiosyncratic risk.

3. Aspirational risk bucket: investing in high risk/return assets such as hedge funds

and/or private equity, or buying call options that will pay off if the market takes off. A representative allocation might be to invest 2% in protective puts and 3% in long call positions, with the rest in a diversified portfolio of market assets. The justification is thatthe 2% allocation to protective puts lowers the risk of extreme losses, and the 3%allocation to long calls can achieve high returns if the market increases. An alternativemight be to invest 30% in the risk free asset and 5% in a high-risk alternative asset such

as a hedge fund and the rest in a portfolio of market assets. Once again, the 30% in the





31

risk-free asset will minimize losses, and the 5% in the hedge fund provides high upside potential under the right conditions.

10. Understand the impact of alternative investments, including real estate,executive stock options and human capital, on the asset allocation of

individual investors.Alternative investments, real estate, executive stock options and human capital will havean impact on the asset allocation of individual investors.

Alternative investments such as private equity, hedge funds, futures, commodities, and

foreign exchange have become a basic ingredient in the portfolios of wealthy individuals.Each has a particular set of impacts on the portfolio, and some can fit into more than onerisk bucket.

1. Private equity and hedge funds would be allocations to the aspirational risk bucket.These investments have idiosyncratic risk, high fees, and low transparency.

2. Commodities would best fit into the personal risk bucket as a protection againstinflation.

3. Futures could fit into all three buckets as a means to reduce risk, diversify withmanaged futures, or enhance risk.

4. Foreign exchange would fit into the personal risk budget as a protection against thedeclines in currency and as a diversifier. It could be a means to address aspirational

risk if there is the expectation of an increase in a given currency.

Real estate can also fit into various risk buckets. A sensible home for personal use would be an allocation to the personal risk bucket. Modest rental units would be an allocationto the market risk bucket and serve as a good diversifier. Speculative real estate

purchased for resale at a higher price would be an allocation to the aspirational risk bucket.

Executive stock options would fit into the aspirational risk bucket. This is because theyare high risk and undiversified assets. Furthermore, they would be highly correlated withthe human capital of the individual.

Human capital would generally fit into the aspirational risk bucket. As education and

skills become more specialized, and therefore undiversified, investments in this area aredesigned to increase the standard of living of the individual.

11. Describe and apply barbell and option based strategies in the context of

asset allocation.The barbell strategy is one where an investor allocates a given amount to a safe positionthat provides a cushion. The rest would be allocated to a risky portion known as theaspirational part. Thus, the portfolio has two extreme positions to meet the desired risk allocations.

An application of the barbell strategy would be to have a relatively high weight in the

personal risk bucket, a relatively low weight in the market risk bucket, and a relativelyhigh weight in the aspirational risk bucket. ‘Relatively high’ would be in comparison to





32

a more standard allocation. A more standard application might be 40% in the personal

risk bucket, 55% in the market risk bucket, and 5% in the aspirational risk bucket.

The application of the barbell strategy would produce an allocation more like 65%,10%, and 25% respectively. Various tools could be used to achieve this for the three buckets. The personal risk bucket could have bonds, the market risk bucket could have

a broad-based equity market portfolio, and the aspirational risk bucket could be a hedgefund; i.e., a barbell portfolio would be 65% in high-grade bonds, 10% in a market ETF,and 25% in a hedge fund.

One particular barbell strategy is an option-based strategy that invests in TIPS for thesafe portion and then purchasing call options for the aspirational part. If the market performs poorly, the investor has some credit and inflation risk. If the market performs

well, the calls will provide a high level of return.

Option-based investing strategies can include certain dynamic asset allocation strategiesthat can be approximated using simple buy-and-hold strategies that include options.Incorporating options may help the sustainability of pension funds.

An application of this strategy would be 90% in TIPS and 10% in a call option on amarket ETF. There is no need to make an allocation to the market risk bucket becausethe option on the ETF has exposure to the market.

12. Discuss reasons why the performance of a rebalanced equally weightedcommodity futures portfolio should not be used to represent the returnof the commodity futures asset class.

There has been a significant difference in the average returns of individual futurescontracts and the returns of a rebalanced equally weighted commodity futures portfolio.

The Goldman Sachs Commodity Index (GSCI) earned an average annual return equal to

12.2% over the time period 1969-2004, with a standard deviation of 18.35%, but theannual return of individual commodity futures has been close to zero. Of 36 commoditycontracts, for the period 1959-2004, only one had a positive return statistically differentfrom zero.

These differences are the obvious reason that the performance of a rebalanced equallyweighted commodity futures portfolio should not be used to represent the return of thecommodity futures asset class. The underlying reason comes from the fact that a portfolioof uncorrelated securities with high standard deviations that is rebalanced can have a

higher return than the individual assets in the portfolio.

13. Explain why the three most commonly used commodity futures indices

(GSCI, DJ-AIGCI, CRB) show different levels of return and volatility overa common time period.

The three most commonly used commodity futures indices are the Goldman SachsCommodity Index (GSCI), traded on the Chicago Mercantile Exchange; the Dow Jones-AIG Commodity Index (DJ-AIGCI), traded on the Chicago Board of Trade; and whatused to be the Reuters-CRB Futures Price Index (CRB), traded on the New York Boardof Trade.





33

Each index is designed to be a broad representation of investment opportunities in theaggregate commodity futures market. The GSCI is the most representative with 86% of

open interest, the DJ-AIGCI accounted for 10%, and the CRB made up the remaining 4%of open interest (figures as of 2004). Over the period 1991-2004, the three commodityindices experienced different levels of return and volatility. The GSCI had twice the

volatility of the CRB during the period. The DJ-AIGCI and the GSCI had average returnssimilar to that of the Lehman Aggregate, and the CRB had a return about equal to that of the T-bill return.

The return of an index is a function of two factors: the returns of the components of theindex and the weights of the components. In the case of the indices, the most importantfactor that can explain the differences is the differing weights of individual commodity

futures contracts in the indices. The use of different portfolio weights implies that eachindex defines the aggregate commodity futures market differently.

● The GSCI uses weights based on the level of worldwide production for eachcommodity, and it has a high weight in energy commodities.

● The annually rebalanced DJ-AIGCI uses weights based on contract liquidity and production data.

● The monthly rebalanced CRB had been a geometrically averaged and equallyweighted index; however, it has now changed to be similar to the DJ-AIGCI.

14. Explain how the returns of a single cash-collateralized commodity futuresand a portfolio of cash-collateralized commodity futures can bedecomposed into various sources of return.

Two components make up the annualized total return of a cash-collateralized commodity

futures contract: the return on the cash position used as collateral and the change in the

futures price. The equation is

Individual cash-collateralized commodity futures return

= Cash return + Excess return

The excess return is simply the percent change in the price of a futures contract. If, for instance, an investor purchases a corn futures contract for $2 a bushel and later sells thecontract for $2.1 a bushel, the excess return on this position is 5%.

Example: An investor goes long a gold contract at $800/oz. If the cash return over thehorizon is 2% and the price of the contract changes to $820/oz., what is the total cash-collateralized commodity futures return?

Return = 2% + ($820-$800)/$800 = 2% + 2.5% = 4.5%

The annualized total return of a diversified cash-collateralized commodity futures

portfolio can be decomposed into three components:

Cash-collateralized commodity futures portfolio total return =

Cash return + weighted-average excess return + diversification return





34

The diversification return is usually a benefit from the synergies of combining two or more assets and rebalancing the portfolio. The geometric average return of a portfolio

will be positively affected by the reduction in variance. A positive diversification returnmeans that the compound return of the portfolio will be greater than the weighted-averagecompound return of the individual portfolio constituents. The diversification return is

enhanced by rebalancing but will usually be lower if the portfolio is not rebalanced.

15. Discuss the four theoretical frameworks (CAPM, the insuranceperspective, hedging pressure hypothesis, theory of storage) used toexplain the source of commodity futures excess returns.

The four theoretical frameworks for understanding the source of commodity futuresexcess returns are the capital asset pricing model (CAPM), the insurance perspective, the

hedging pressure hypothesis, and the theory of storage. None of these is the definitivemodel, but they all represent work being done to understand commodity returns.

The CAPM would predict that commodities futures would have a zero excess return. This

is because commodities futures are uncorrelated with equities and have a zero beta. Thereare at least two faults with this argument. First, the CAPM is for capital markets, andcommodities are not included in the market index. Second, the CAPM has lowexplanatory power even for equities; therefore, it would not be surprising that it wouldnot have much explanatory power for non-equity assets.

The insurance perspective proposes a return is earned by speculators who take long

positions from “normal backwardation.” If today’s futures price is below the spot pricein the future, then as the futures price converges toward the spot price at maturity, excessreturns should be positive. The excess return from a long commodity futures investmentshould be viewed as an insurance risk premium. Under normal backwardation,investors who go long commodity futures should receive a positive risk premium;

therefore, normal backwardation provides a rationale that a long-only portfolio of commodity futures can represent an effective allocation of capital.

The hedging pressure hypothesis says that commodities can produce positive returns for either normal backwardation markets or contango markets. In normal backwardation

markets, the agents who are long the commodity itself, for e.g., oil producers, are willingto short futures at a lower price, and the speculators can be long and earn a profit as thefutures price rises. In contango markets, the agents who are short the commodity, for e.g., airlines needing fuel, are willing to go long futures at a higher price, and thespeculators can be short and earn a profit as the futures price rises. The bottom line is thatreturns can be positive for either the long or short position, and it depends upon whether

there is more hedging pressure on the long or short side.The theory of storage focuses on the role that inventories of commodities play in thedetermination of commodity futures prices. The theory is that inventories allow producers toavoid shortages and production disruptions. The more plentiful inventories are, the less thelikelihood is that a production disruption will affect prices. The less plentiful inventories are,the more likely it is that a production disruption will affect prices. As a result, having a level

of inventories that will reduce the impact of production disruptions is beneficial. Theconvenience yield is a type of risk premium that is determined by inventory levels.





35

Inventories may be low for difficult-to-store commodities; as a result, those commoditiesmay have high convenience yields. Conversely, for easy-to-store commodities, inventories

should be plentiful and they should have low convenience yields.

In the theory of storage, storage costs determine the price of a commodity futurescontract. We should recall the basic futures pricing formula when there are not storage

costs and a convenience yield: the futures price equals the spot price times the futurevalue interest factor.

For example, if the spot price = $800 and the interest rate = 5%, for a one-year contract,the futures price = $840. There could be a convenience yield, however, if inventories arelow. In the above example, if the convenience yield is 1%, the futures price = $832.

Example: A commodity has a convenience yield of 10%, and the interest rate is 6%. If

the spot price is $50, compute the futures price for a six-month contract. Answer: Thenet effect will be a futures price that is 2% lower than the spot price: for an annualcontract, the computation is 6% - 10% = -4%, but for a six-month contract, the effect isone half of this. The futures price = $50*(1 - 4%/2) = $49.

16. Explain the concepts of contango, normal backwardation and marketbackwardation.

Contango is the condition of the futures price being above the expected future spot price.The term “contangoed commodities” refers to contracts where the futures price is greater than the spot price. One explanation for contango is that hedgers are net long futures.

Normal backwardation describes the case where the futures price for a commodity isless than the expected spot price in the future. If today’s futures price is below the spot price in the future, then as the futures price converges toward the spot price at maturity,

excess returns should be positive. Normal backwardation cannot be observed, because

the expected spot price is not truly known. Market backwardation can be observed, andits two components are the market consensus expected future spot price and a possiblerisk premium.

17. Calculate the roll yield of a commodity futures contract in backwardationor contango.

Roll yield or roll return generated in a backwardated futures market is achieved byrolling a short-term contract into a longer-term contract and profiting from theconvergence toward a higher spot price. The short-term contract will have a higher priceand the return will be positive:

Roll yield = (Fshort-term/Flong-term) -1.

When backwardation exits, the roll yield is positive. For example, if the futures price for a May 2009 contract is $45 and the futures price is $40 for the June 2010 contract, and

assuming the term structure remains the same,

Roll yield = ($45/$40) -1 = 12.5%





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When contango exits, the roll yield is negative. If the futures price for a May 2009contract is $48 and the futures price is $50 for the June 2010 contract, and assuming the

term structure remains the same,

Roll yield = ($48/$50)-1 = -4%

Example: If the futures price for a January 2010 contract is $100 and the futures price is$80 for the January 2011 contract, and assuming the term structure remains the same,

calculate the roll yield and comment on whether the market is in backwardation or contango.

Answer: Roll yield = ($100/$80)-1 = 25%. Since the roll yield is positive, the market isin backwardation.

18. Discuss the importance of roll return in explaining the long-run cross-sectional variation of commodity futures returns and the implication forinvestors.

Roll returns are important in explaining the cross-section of individual commodityfutures’ excess returns from December 1982 through May 2004. In a regression, thecoefficient of determination (R 2) indicates that the roll returns explained 91.6% of thelong-run cross-sectional variation of commodity futures returns over the period. Therewas a lot of variation ranging from positive roll returns to negative roll returns.

The implications are that investing in commodity futures with relatively high roll may berewarding, but it is not a guarantee. Potential investors should be cautious. The results donot suggest that roll returns will explain 91.6% of the cross-sectional variation of commodity futures returns over any particular future time horizon. It is convenient toextrapolate, but it is not scientific. For a broadly diversified portfolio of commodity

futures, it may be the best for a risk averse investor to assume a future roll return of zeroor even below zero.

19. Describe the relative importance of the volatility of spot return and rollreturn in determining the volatility of futures returns.

For the period December 1982 through May 2004, roll returns have been highlycorrelated with excess returns. However, there was not a significant average excess or spot return for any given individual commodity futures or commodity futures sector.The importance of the high volatility of spot returns is evident because it made even thehighest roll return an insignificant excess return. Given the conflicting results, the bottom line is that it is not clear if any of the average spot and excess returns were

statistically different from zero in this time period.

20. Describe the impact of inflation and unexpected changes in the rate of inflation on individual commodity contracts, sectors, and diversifiedcommodity portfolios and indices.

Inflation is usually considered an important factor in determining commodity prices and,therefore, would probably play a role in determining return volatility. Two studies are

representative of research in this area.





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● Greer (2000): Over the 1970–99 period, the Chase Physical Commodity Index had atime-series correlation of 0.25 with the annual rate of inflation and a time series

correlation of 0.59 with the change in the annual rate of inflation.

● Strongin and Petsch (1996): The GSCI performs well relative to stocks and nominal bonds during periods of rising inflation.

We must be careful about how we define inflation, however, because commodities haveonly about a 40% weight in the U.S. Consumer Price Index (CPI), and a broad-basedcommodity futures index excludes many items measured in the CPI. One notableexample is the housing component of the CPI, which is the biggest single CPI componentand is not in a commodity futures index.

Inflation consists of two components: expected inflation and unexpected changes in

inflation. Unexpected inflation appears to be correlated with excess returns of the GSCI.Assuming changes in inflation cannot be forecast means that changes in inflation canserve as a proxy for unexpected inflation. Using this proxy, it is found that for the period1969 to 2003, unexpected inflation explained 43% of the time-series variation in the

GSCI’s annual excess returns. The GSCI has a positive (but statistically insignificant)actual inflation beta and a positive (and significant) unexpected inflation beta.

This result requires a closer look for two reasons: first, the GSCI’s composition haschanged over time and, second, the returns of many commodity futures seem to beuncorrelated with one another. We should recall that the inflation beta of a commodity

futures portfolio is simply a weighted average of the portfolio’s constituent inflation betas. Thus, it is important to understand the behavior of a broad-based commodityfutures investment by looking at the inflation sensitivity of individual commodity futures.

The following is a summary of statistical tests.

● The sectors energy, livestock, and industrial metals have significant unexpectedinflation betas.

● The individual commodity futures heating oil, cattle, and copper have significantunexpected inflation betas.

● The precious metals sector has a statistically significant negative inflation beta.

● No other sectors or individual commodities have significant inflation betas.

Average roll returns explain 67% of the cross-sectional variation of commodity futuresunexpected inflation betas. Some commodities (e.g., copper, heating oil, and live cattle)had positive roll returns for the period and high unexpected inflation betas. Other commodities (e.g., wheat) had negative roll returns and negative unexpected inflation

betas. Thus, one explanation for why some commodity futures might be better inflationhedges than others is that average roll returns are highly correlated with unexpectedinflation betas.

The wide dispersion of relationships probably explains why the equally weighted averageof the 12 commodities has a positive (but insignificant) inflation beta. Finally, neither themagnitude nor the sign of the inflation coefficients is guaranteed to remain constant in thefuture.





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21. Explain how rebalancing and diversification can impact the geometric rateof return of a portfolio in comparison to its arithmetic rate of return.

The diversification return is the difference between a portfolio’s geometric return and theweighted-average geometric return of the portfolio’s constituents. The diversificationreturn can, under certain circumstances, appreciably raise the geometric return of a fixed-

weight, rebalanced, commodity futures portfolio.

The arithmetic return is the simple average of period-to-period returns. The geometric

return is the return that takes into account the compounding across periods. The

geometric return is always less than the arithmetic return, but there can be a positiveincremental return from variance reduction. Illustrating this begins with theapproximation:

geometric return = (arithmetic return) – (asset or portfolio’s variance)/2.

By reducing the variance, the geometric return increases. A fairly simple formuladefines the diversification return of an equally weighted, rebalanced portfolio:

portfolio diversification return =

(average variance)*(1-average correlation)*(N-1)/(2*N),

where N is the number of assets in the portfolio. This equation illustrates that there is a positive relationship between the diversification return and the average variance of the

securities in the portfolio, a negative return between the diversification return and theaverage correlation and the number of securities in the portfolio.

For example, for an equally weighted portfolio of 30 securities with average individualsecurity standard deviations of 30% a year and average security correlations ranging

from 0.0 to 0.3, the diversification return ranges from 3.05% to 4.35%. This indicateshow investors can boost a geometric return with some certainty by rebalancing a portfolio. The important point to remember is that when asset variances are high andcorrelations are low (as they are with commodities), the diversification return can behigh.

22. Discuss the effectiveness of tactical asset allocation in commodityportfolios using strategies based on momentum and the term structure of futures prices.

Many investors find the possibility of earning returns from both time-series and cross-sectional analysis attractive. The two basic strategies are:

● Momentum or trend following. Fung and Hsieh (2001) found that most activemanagers of commodity futures portfolios are trend followers who rely on theassumption that past price moves predict future price moves; and

● Term structure or cross-sectional analysis focuses on the idea that the term structureof commodity futures explains a significant portion of the long-run cross-section of commodity futures returns.





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Momentum/Trend following

Evidence suggests that commodity futures returns are predictable. Jensen et al. (2000,2002) found that the GSCI outperformed stocks and bonds when their measure of U.S.Federal Reserve monetary policy rose. Strongin and Petsch (1996) found that GSCIreturns were tied to current economic conditions. Nijman and Swinkels (2003) found that

nominal and real portfolio efficient frontiers can be improved by timing allocation to theGSCI in response changes in some macroeconomic variables (bond yield, the rate of inflation, the term spread, and the default spread). Vrugt, Bauer, Molenaar, andSteenkamp (2004) found that GSCI return variation is affected by measures of the business cycle, the monetary environment, and market sentiment.

Furthermore, for the period 1969-2004 and subperiods within that sample, Erb and

Harvey (2006) found a positive and significant return from a simple trend followingstrategy of going long the GSCI for one month when the previous month had had positivereturns. Erb and Harvey also found the returns increased by a long-the-winners and ashort-the-loser strategy, and the Sharpe ratio was more than twice that of a long-only

strategy.

Term structure or Cross-sectional analysis

When the GSCI is backwardated, i.e., the price of the nearby GSCI futures contract isgreater than the price of the next-nearby futures contract, this could provide long-only positive excess returns. Nash and Smyk (2003) found that GSCI total returns are positive

when the GSCI is backwardated. Based upon this, it would be natural to extrapolate thatwhen the GSCI is in contango, i.e., the price of the nearby GSCI futures contract is lessthan the price of the next nearby futures contract, the long-only strategy would producenegative returns.

The historical evidence suggests that the term structure seems to have been an effectivetactical indicator of when to go long or go short a broadly diversified commodity futures portfolio. Erb and Harvey measured the return to be 8.2% for a strategy of going long theGSCI when backwardated and short when contangoed. That return is more than twicethat of a long-only strategy. They also found positive results for similar strategies on

individual futures contracts.

23. Argue against the use of naive extrapolation of past commodityreturns to forecast future performance and discuss the importance of formulating forward-looking expectations.

Naïve extrapolation is the practice of using past performance as a forecast of future

performance. Research suggests this can be hazardous. Arnott and Bernstein (2002)found that past high excess returns for U.S. equities do not mean that forwardlooking equity risk premiums are high. They argued that forward-looking returnsshould be based on an understanding of the fundamental drivers of equity returns,

such as earnings growth, dividend yield, and the change in valuation levels. Only if the future return drivers are the same as in the past will past returns be a guide to thefuture.





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In commodity futures, historically, the long-only GSCI has had an excess return of about6% a year; however, it has been declining in recent years. One explanation is that

increased institutional investment in commodity futures has driven up prices and drivendown prospective returns, and another explanation is that the decline is from changes inthe composition of the GSCI. Furthermore, there is no guarantee that the yield curve in

the future will be such that positive roll returns are possible.It is also important to note that, historically, individual commodity futures’ excessand spot returns have not been statistically significant. This would infer that any long-term forecast of positive excess return or positive spot return for an individualcommodity futures contract is unlikely to be statistically supported by historicalexperience. In conclusion, there is no one best estimate of the expected return of a

commodity futures portfolio, although the diversification return is the easiest returndriver to estimate.

24. Discuss the role of global commercial real estate in a strategic assetallocation setting.

Modern portfolio theory suggests that assets such as commercial real estate, which havelow correlations with the current opportunity set of traditional asset classes (stocks, bonds, and cash), tend to provide the greatest diversification benefits. Unfortunately,there exists ample disagreement on the role of these other asset classes (e.g. commercialreal estate) in a strategic asset allocation, even though commercial real estate represents a

large portion of the investable universe and should be included in all investors’opportunity sets. Hudson-Wilson, et al., who are cited by Idzorek et al., argue that realestate and the other assets should be incorporated in the portfolio at their market weightsand that, as a second step, these weights should be adjusted in order to best attaininvestment objectives.

Idzorek et al. argue that the largest investors’ target allocations will be more heavilyweighted in direct commercial real estate investments (acquiring and managing actual physical properties), while smaller investors will likely do it more with real estateinvestment trusts (REITs) and stocks of listed companies that belong to the real estateindustry, as well as direct investments in commercial real estate, as they work towardsa strategic asset allocation to global commercial real estate (for more on thecomponents of the commercial real estate asset class, see Learning Objective 25).

Relative weightings should be very close to market capitalization-based weights. For the average investor, however, REITs and real estate stocks are the only practical andefficient means to obtain exposure to the commercial real estate equity asset class.

Idzorek et al. document that a shift is in progress within global commercial real estate asthe advantages of REITs and stocks of listed real estate companies (for more on this, seeLearning Objective 25) produce a likely inclination for these securities among investors,and eventually a considerable portion of direct real estate is prone to be securitized. They predict that the size of REITs and stocks of listed real estate companies and their proportion in the total commercial real estate market will continue to grow in the coming

years.





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25. Identify the components of the commercial real estate asset class and therelative advantages of direct real estate investment and real estateinvestment trusts (REITs).

The four components (or segments) of the commercial real estate asset class are:

1) Private (direct) commercial real estate: debt. This segment is accessible only tolarge investors, which can purchase issued whole loans directly. Small investors maygain exposure to this component of commercial real estate through the purchase of stocks of commercial banks, stocks of mortgage REITs, and other specialty financecompanies.

2) Public (indirect) commercial real estate: debt. This component is constituted primarily by commercial mortgage-backed securities (CMBS).

3) Private (direct) commercial real estate: equity. It involves the acquisition andmanagement of actual physical properties.

4) Public (indirect) commercial real estate: equity. This segment involves buying

shares of real estate investment companies (REITs) and other listed real estatecompanies.

The relative advantages of direct real estate as an investment include:

1) direct control,

2) the ability to choose specific properties,

3) greater capacity, and

4) potential tax-timing benefits.

The relative advantages of REITs include:

1) liquidity,

2) investor access,

3) lower costs,

4) potential for better corporate governance structures,

5) independent analysis, and

6) pricing in public capital markets.

26. Explain the historical performance and diversification benefits of selectasset classes.

Instructor note: There are ten different asset classes to compare in this section. The

learning objective does not specify on which assets the student should concentrate. We

believe focus should be mainly on real estate assets.

Idzorek et al. analyze the historical performance of the following four geographicallysegmented indirect real estate investments (REITs): Global, North America, Europe and

Asia, and also compare them to the performance of traditional assets (cash, U.S. and Non-U.S. bonds, U.S. large and U.S. small caps, and Non-U.S. stocks).





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During the period 1990-2005, North American real estate was the highest-returning assetclass (with an average annual arithmetic return of around 17%). Furthermore, North

American real estate, U.S. bonds, and U.S. large-cap stocks showed the highest Sharperatios, while non-U.S. stocks, European real estate, and Asian real estate exhibited thelowest Sharpe ratios. Asian real estate was the most volatile asset class, reflecting the fact

that Asian currency markets were highly volatile over the period. Idzorek, et al. alsoconclude that, at least over short periods of time, it is almost impossible to predict whichasset class (among the traditional assets and the previous four real estate investments)

will be the best performer.

The data illustrates that global real estate has generally had low, or negative, correlationcoefficients with traditional assets (U.S. large-cap stocks, U.S. small-cap stocks, and U.S.

bonds). European real estate has had negative, or very low, correlations with all U.S.asset classes. These weak to negative correlations between real estate and traditional assetclasses suggest that additional diversification benefits can be attained by including realestate investments in a portfolio. Also, the three sub-asset classes of global real estateconsidered (American, European, and Asian) exhibit relatively high intra-equity

correlations.

Finally, global real estate tends to have lower correlations with the four traditional U.S.assets when compared to North American real estate. On the other hand, North Americanreal estate has lower correlations with non-U.S. bonds and stocks when compared toglobal, European, or Asian real estate.

27. Compare the assumptions and results of the CAPM approach to theBlack-Litterman approach when determining forward-looking assetallocations.

Two forward-looking asset allocations models are analyzed in Idzorek, et al.: the CAPM

approach and the Bayesian Black-Litterman asset allocation approach. In a forward-looking context, as opposed to the traditional mean-variance optimization approach, thecapital market assumptions are forecasts and, therefore, they are not known withcomplete certainty.

Idzorek, et al. estimate forward-looking efficient asset allocations based on expectedreturn estimates drawn from the CAPM using a reverse optimization procedure. Theyfind a large difference between the CAPM return and the historical arithmetic return for North American real estate and offer two possible explanations. First, the historicalaverage annual return may be the result of an abnormally optimistic but temporary periodfor North American real estate. Second, past research has suggested that several return

anomalies can not be explained by the CAPM (e.g. small firm effect, momentum, etc.). Itmay be that North American real estate represents an anomaly analogous to these.

Idzorek et al. then also use the Black-Litterman model to produce a set of forward-looking expected returns that blends expected returns arising from the CAPM with thehistorical returns. The Black-Litterman asset allocation gives a greater weight toworldwide commercial real estate equities and smaller weights to non-U.S. stocks than

the CAPM. Furthermore, these forward-looking efficient asset allocations support North





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American and European real estate more than the CAPM-based allocations (see theexhibit).

The Bayesian Black-Litterman asset allocation procedure provides market-basedasset allocations that are improved with information available from the historicalreturns. As in the CAPM-based allocations, another description of the market

portfolio will generate a different set of asset allocations. The Black-Litterman-basedapproach is largely influenced by the short-term returns that are mixed with theCAPM returns, although the resulting suggested asset allocations are influenced far less than most other models.

Exhibit: CAPM and Black-Litterman Forward-Looking Asset Allocations for Real EstateClasses

CAPM Forward-Looking A. A. Black-Litterman Forward-Looking A. A.

Asset Class Conservative Moderate Aggressive Conservative Moderate Aggressive

North Am. RE 2.9% 5.0% 4.8% 5.9% 12.1% 15.4%

European RE 3.9% 6.7% 8.5% 4.5% 7.8% 11.1%

Asian RE 1.7% 4.1% 8.6% 1.1% 3.4% 8.2%

Source: Idzorek, et al. (2007)

28. Explain the seven caveats identified by the author as considerations forstrategic asset allocation to global commercial real estate.

Idzorek et al.’s first caveat is that the two forward-looking asset allocations sets they present represent only two of the many possible asset allocations that can be derived fromanalytically based forward-looking expected returns.

Second, it may questionable to use global REITs and stocks of real estate companies toembody the long-term performance of the universe of commercial real estate equity

investments. However, considering REITs and listed real estate stock returns as a proxyfor all commercial real estate investments has become more suitable in the last few yearsas these investments have been growing fast and represent a larger proportion of themarket. In the end, we are confronted with an empirical question.

Third, investors who have a separate strategic asset allocation to REITs and listed realestate stocks may not need to own these indirect real estate investments. However, allother investors should own REITs and listed real estate stocks. Idzorek et al. note that thiscaveat is relevant only to a small group of the largest investors.

Fourth, the CAPM-based asset allocations presented are market-based and assume thatinvestors do not suffer from a U.S. or home bias (i.e. the tendency to invest in a large proportion of domestic assets, despite the supposed benefits of diversifying into foreignassets). Another definition of the market portfolio (one that includes, for example,commercial real estate) will produce another asset allocation. More work on this topic isneeded.

Fifth, if investors would be able to increase their opportunity set so as to include all themost important assets that are part of the – unobservable – market portfolio (assets such





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as TIPS, convertible bonds, commodities, emerging market bonds, high-yield bonds andemerging market stocks), the end result will be a reduction in the allocation to the asset

classes considered in Idzorek et al.’s study.

The sixth caveat, which is related to the fifth, is that a different composition of the market portfolio will yield a different set of Black-Litterman-based asset allocations.

Finally, actual asset allocations should be tailored to the investor’s unique situation.

Example:

Let us use these insights on real estate asset allocation in a hypothetical example. Peter Gray, an investment analyst at YHG Investments, is analyzing whether to include realestate as an asset class in a client portfolio that is currently constituted only in U.S. stocksand U.S. bonds. To that end, he collects the following historical information on U.S. realestate, U.S. stocks, U.S. bonds and U.S. inflation.

YEAR REITS Stocks Bonds CPI

1991 35.68% 30.47% 16.00% 2.98%1992 12.18% 7.62% 7.40% 2.97%1993 18.55% 10.08% 9.75% 2.81%1994 0.81% 1.32% -2.92% 2.60%1995 18.31% 37.58% 18.47% 2.53%1996 35.75% 22.96% 3.63% 3.38%1997 18.86% 33.37% 9.65% 1.70%1998 -18.82% 28.58% 8.69% 1.61%1999 -6.48% 21.04% -0.82% 2.68%2000 25.89% -9.10% 11.63% 3.44%2001 15.50% -11.89% 8.44% 1.60%

2002 5.22% -22.10% 10.26% 2.48%2003 38.47% 28.68% 4.10% 1.87%2004 30.41% 10.88% 4.34% 3.29%2005 8.29% 4.91% 2.43% 3.40%2006 34.35% 15.79% 4.33% 2.53%

Average 17.06% 13.14% 7.21% 2.62%St. Deviation 16.46% 17.45% 5.64% 0.64%

CORRELATION MATRIX

REITS Stocks Bonds CPI

REITS 1.00 0.17 0.22 0.31

Stocks 1.00 0.18 -0.19Bonds 1.00 -0.09

CPI 1.00

Note: “REITS” returns were measured from the total return index REITs calculated by

NAREIT, “Stock” returns were measured from the Standard and Poor’s 500 total return index,

“Bonds” returns where measured from the Lehman Aggregate U.S. Bond total return index,and “CPI” is the percentage changes in the U.S. Consumer Price Index.





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Part A: Peter notices that real estate provided the highest return of the three asset classes, but he is concerned that the standard deviation of real estate is almost as high as the

standard deviation of stocks. This leads him to question whether to add real estate to thetraditional portfolio at all. Do you agree with Peter?

Modern portfolio theory suggests that asset classes such as real estate, which have low

correlations with the current opportunity set of traditional asset classes (we can see in thetable that it has correlations of 0.17 and 0.22 with stocks and bonds, respectively), maywell provide substantial diversification benefits. Therefore, even though real estateexhibited one of the highest standard deviations, its low correlation to traditional assets,combined with the high returns it offered, makes this alternative investment a good potential candidate to be added to a traditional portfolio.

Part B: Based on the information presented in the tables, Peter is assessing whether realestate offers a better hedge than stocks or bonds against the risk of inflation. What wouldyou say to him?

You could say that real estate investments exhibited a positive correlation with inflation

(0.31), as opposed to stocks and bonds, which showed a slightly negative correlation withinflation (-0.19 and -0.09, respectively). Therefore, it appears that real estate investmentsoffered a better hedge against the risk of inflation than stocks or bonds did during the period 1991-2006. A more rigorous study would attempt to see the correlation of realestate returns with inflation using a measure of unexpected inflation (what is shown in thetable, CPI inflation, is simply the observed inflation, whether it was expected or not).

References

Perold, A. F., and W.F. Sharpe. “Dynamic Strategies for Asset Allocation.” Financial Analysts

Journal . January/February 1988.

Chhabra, A. “Beyond Markowitz: A Comprehensive Wealth Allocation Framework for Individual Investors.” The Journal of Wealth Management , Spring 2005.

Erb. C., and C. Harvey. “The Strategic and Tactical Value of Commodity Futures.” Financial

Analysts Journal . Vol. 62, No. 2, March/April 2006.

Idzorek, T.M., M. Barad, and S.L. Meier. “Global Commercial Real Estate.” The Journal of

Portfolio Management . Special Issue, 2007






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TOP I C 9

Current Topics

M a i n P o i n t s Explaining what happened in the Amaranth debacle, how it happened and how

it could have been avoided

Applying the market events of August 2007 to the concept of systemic risk

Describing factors contributing to the subprime credit crisis andrecommending policies and practices to address them

1. Understand what is meant by the “term structure of a commodityfutures curve” and the terms “backwardation” and “contango”.

The term structure of a commodity futures market is a curve that is constructed by plotting each delivery-month contract on the x-axis and its respective price on the y-axis.

As noted on page 326, the author says: “When the near-month futures contracts trade at adiscount to further-delivery contracts, one says that the futures curve is in contango.

When the near-month futures contracts instead trade at a premium to further-deliverycontracts, one says the futures curve is in backwardation.”

2. Understand the derivation of the futures curve for natural gas and theassociation between the curve and potential determinants includinganticipated production, consumption and seasonal factors. The prices of summer and fall natural gas contracts typically trade at a discount to thewinter contracts. The markets, therefore, provide a return for storing natural gas. An



Topic 9: Current Topics


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owner of a storage facility can buy summer natural gas contracts and, simultaneously, sellwinter natural gas contracts. The difference will be the operator’s return for storage.

When the summer contracts expire, the owner takes delivery and injects it into storage.When the winter contracts expire, the owner makes delivery of the natural gas.

U.S. natural gas storage capacity has actually declined since 1989 and domestic

production has not kept up with demand. If there are hurricanes during the summer in theU.S., concerns arise as to whether natural gas production will be sufficient for winter consumption needs. In such scenarios, the front month’s contract price can increasedramatically to discourage current demand and the futures curve would trade in steeper contango to provide a further enhanced return for storage. This occurred after HurricaneKatrina in 2005.

3. Explain a futures calendar-spread strategy and the sources of potentialprofits, potential losses and risk from this type of strategy.

Trades that exploit the spreads between two delivery months are referred to as calendar-

spread trading strategies. As noted by the author, sophisticated storage operators can potentially value their storage facilities as a set of complex options on calendar spreads.

Potential profits:

In general, if the calendar spreads are volatile, storage is worth more.

The strategy is profitable as long as the operator’s financing and physical outlay costs areunder the spread locked in through the futures market. If spreads tighten, traders(operators) can trade out of the spread at a profit and reinitiate a trade later. If traders

(operators) cannot trade out of the spread, they can take physical delivery and realize thevalue of their storage facility.

Potential losses and risk:

If the winter months are unexpectedly mild and there are massive amounts of natural gasin storage, the near month contract’s price plummets to encourage its current use.

4. Describe the type of calendar-spread strategy Amaranth employed andexplain the rationale for this strategy as it relates to natural gaspricing.

As described by Till, Amaranth’s spread trading strategy involved taking long positionsin winter contract deliveries and short positions in non-winter contract deliveries. These positions would have been profitable if adverse weather events such as hurricanes andcold shocks occurred during the period 2006-2010.

However, by August 2006, Amaranth faced a major dilemma – how to trade out of itslarge, high-priced spread positions without causing the price of those spreads to fall. In prior months, Amaranth had rolled its short positions into the next month, hoping that

market conditions would change and enable it to unload its positions. As summer wasnow ending, there were no more summer months into which it could roll these positions.By late August, with the hurricane season almost over and with natural gas suppliesremaining plentiful, it appeared likely there would be adequate supplies for the winter.The market fundamentals were strongly indicating that the winter/summer price spreads





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should fall. This would be particularly disastrous for Amaranth, which was still holdinglarge positions that it had obtained when these spread prices were high (See the US

Senate PSI Staff Report “Excessive Speculation in the Natural Gas Market,” that issummarized in Till’s article.)

The key economic function for natural gas is to provide for heating demand during the

winter in the northern states in the U.S. and air-conditioning demand during the summer months. There is a long injection season during spring through fall when natural gas isstored in caverns for later use during the long winter season. Till makes several pointsregarding the natural gas market:

Domestic production has not kept up with demand.

U.S. natural gas markets are largely insulated, in the short-term, from global energyfactors.

There is insufficient storage capacity, particularly if there is a severe winter.

Inventories need to be recycled to maintain integrity.

All these factors have a major impact on the pricing relationships for different deliverymonths.

5. Discuss the magnitude of Amaranth’s calendar-spread positions: explainhow this hedge fund was able to accumulate such large positions(including the role of position limits) and describe the effects of themagnitude of the positions on daily profits and losses.

Given below is an excerpt from the US Senate Permanent Subcommittee onInvestigations (PSI) Report that sheds some light on position limits and actions by NYMEX. The key item to consider here is that Amaranth was easily able to move to ICE

from NYMEX because ICE did not have the same constraints as NYMEX.

By the end of July, Amaranth was short nearly 60,000 contracts for September, 42,000contracts for October, and 80,000 contracts for April 2007; it was long 80,000 contractsfor January 2007, 60,000 contracts for March 2007, and 29,000 contracts for December 2007. Amaranth held about 40% of the total open interest in the NYMEX natural gasmarket for all of the winter months (October 2006 through March 2007).

During 2006, NYMEX repeatedly reviewed Amaranth’s natural gas holdings todetermine whether they exceeded NYMEX’s established position limits or accountabilitylevels. On several occasions, Amaranth traded large numbers of contracts near their expiration date, triggering NYMEX notices that the firm had violated NYMEX position

limits; a CFTC investigation of one of these instances is still ongoing.

In August 2006, NYMEX took more forceful action to limit Amaranth’s trading,directing Amaranth to reduce its positions in the NYMEX futures contracts not just for the September contracts that were about to expire, but also for its contracts in thefollowing month of October. In response, Amaranth reduced its positions in those

contracts on NYMEX, but at the same time, it increased its positions in the correspondingcontracts on ICE. The end result was that Amaranth maintained and even increased its positions in contracts for September and October, and it preserved its ability to engage in





50

large-scale trading as the September contract neared expiration. In fact, Amaranth’s moveenhanced its ability to conduct large-scale trading near the contract expiration because,

under current law, no market surveillance or position limits apply to trading on ICE.

The US Senate PSI Report found, for example, in late July 2006, that Amaranth’s naturalgas positions for delivery in January 2007 represented a volume of natural gas that

equaled the entire amount of natural gas eventually used in that month by U.S. residentialconsumers nationwide. The scale of Amaranth’s trades is shown in Exhibit 2, reproducedfrom Till’s article, below. Exhibit 3 shows graphically Amaranth’s forward curve. Thekey again is the magnitude of the positions when NYMEX and ICE positions arecombined.

Source: Till (2007)





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Till’s Exhibits 4 and 5 indicate that the changes for various spread relationships acrossthe natural gas curve were extremely large on September 15 and September 18 which

signaled, at the time to market participants, that a distressed liquidation was occurringgiven that these moves were very large standard deviation moves compared to recenthistory.

Exhibits 4 and 5 also show that the prices of the winter-month contracts fell dramaticallyas compared to the non-winter-month contracts in the near-month through 2011 forwardmaturities for natural gas. While NYMEX had position limits, ICE did not at the time.(The US Senate PSI Report provides details.)

Source: Till (2007)





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Source: Till (2007)

6. Discuss the causes for increased volatility on the natural gas commodityfutures market prior to Amaranth’s liquidation in September 2006.

As noted by Till, if the winter is unexpectedly mild and there are still massive amounts of

natural gas in storage, the near month price of natural gas plummets in order to encourageits current use and the curve trades in contango in order to provide a return to any storage

operator who can still store gas. This scenario occurred during the end of the winter inearly 2006. As indicated in Learning Objective 4 above (See the US Senate PSI Report),with hurricane season almost over and with natural gas supplies remaining plentiful, itappeared likely there would be adequate supplies for the winter. The marketfundamentals were strongly indicating that the winter/summer price spreads should fall.

7. Discuss how sophisticated storage operators can manage their storagefacilities as a set of options on calendar spreads.

Storage is worth more if the calendar spreads in natural gas are volatile. As a calendar spread trades in steep contango, storage operators can buy the near month contract and

sell the far month contract, knowing that they can ultimately realize the value of thisspread through storage.

Another preferable scenario would occur if the spread tightens. Under this scenario, thetrader could trade out of the spread at a profit. If the spreads were to be in wide contango again, the position could be reinitiated. The key consideration here again is the volatilityof the spreads. If the volatility of the spreads remained high, the storage operator/trader





53

can continually lock in profits. If conditions are such that the operator/trader cannot tradeout of the spread with a profit, they can take delivery of the natural gas in anticipation of

making delivery at a later date. The storage operator/trader is thus realizing the value of the storage facility.

Alternatively, if the winter is unexpectedly mild with an abundance of natural gas in

storage, the near month price plummets and current consumption is encouraged. If storage operators can still store gas, they can realize a return.

8. Describe how daily volatility as measured by standard deviation canunderestimate potential risk (where risk is defined as the likelihood of experiencing severe loss), and explain how scenario analysis can be usedto better indicate the risk of a fund’s structural position in suchcircumstances.

The daily P/L of Amaranth’s August 31 positions is shown in Exhibit 12 from Till’sarticle. The daily standard deviation based on three months of data was about $105million – far lower than the actual losses that occurred in September. See below anexcerpt from the Senate Report on the losses suffered by Amaranth. If scenario analysishad been conducted, the riskiness of the fund’s structural position would have been moreevident. As noted by Till, as of August 31, 2006, winter natural gas futures prices were

trading at an extreme relative to non-winter-month contracts. A scenario analysis couldhave examined over the past six years what the level of the fund’s spreads had been.

Using the Senate report, Till finds two spreads that were 93% correlated to Amaranth’snatural gas book: the November 2006 versus October 2006 (NGX-V6) spread and theMarch 2007 versus April 2007 (NGH-J7) spread. Exhibit 14 shows that if these twospreads had reverted to the levels that had prevailed at the end of August during the

previous six years, up to -36% could have been lost under normal conditions.

Source: Till (2007)







55

has been in free fall. In my opinion, fundamentally, that spread is still a long way fromfundamental value.

Over the past couple years the market has put a big risk premium into that spread yet ithas paid out on expiry once in ten years. We’ll be at all time high storage levels withmediocre s/d [supply and demand] and an el nino. Even though that spread has collapsed

over the past 2 weeks, the only reason it’s still $1 is because of your position.Historically, that spread would be well below $1 at this point given the scenario.’

Mr. Arnold gave Mr. Hunter two price quotes for the March/April spread: 45-60 cents for the March/April 2007 spread which had closed the previous trading day at $1.15; and$1.00-$1.20 for the March/April spread in 2008 and beyond, which had closed the previous day at between $2.10 and $2.20. Mr. Hunter declined Mr. Arnold’s offer. Mr.

Arnold’s prediction of the behavior of these spreads, however, turned out to beremarkably accurate. On September 21, the last day of Amaranth’s trading in the naturalgas market, the March/April 2007 spread stood at 58 cents, and the March/April spreadsfor 2008 and beyond ranged from $1.18 to $1.25.

After several days of frantic negotiations with several brokerages and banks, onSeptember 20th, Amaranth formally sold its energy book to its clearing firm, JPMorganChase, and Citadel, another hedge fund. To meet its margin calls and satisfy clientrequests, Amaranth liquidated the remainder of its $8 billion portfolio.”

9. Describe what is meant by “nodal” or “one-way” liquidity in thecommodity markets and how the lack of “two-way” liquidity adverselyaffected Amaranth.

Commodity markets have “nodal liquidity”. This implies that as a commercial market

participant needs to initiate or lift hedges, there will be flow but these transactions do notoccur on demand. That is, there is no ready and willing counterparty available to take theopposite positions. Hence a key focus of experienced commodity traders is an exitstrategy. What flow or catalyst will allow a trader to exit a position?

In the case of Amaranth, there was no natural (financial) counterparty that could take onits positions in under a week (or, specifically, during the weekend of September 16-17,

2006). The natural counterparties to Amaranth’s trades were the physical market participants who had already locked in the value of forward production or storage, so theywould not be interested in taking counterparty positions to Amaranth.

10. Understand how forced liquidations can affect market prices and whychanges in market prices can be correlated with the size and direction

of the liquidation.Exhibit 15 shows the critical liquidation cycle.

By Friday September 15, 2006, Amaranth had lost more than $2 billion of its $8 billion portfolio. According to Davis et al. (2007), at this point, the fund was bleeding cash andfacing demands from its prime broker for additional funds that it did not have.

The critical liquidation cycle began at this point. Once a threshold of losses is crossed, thecycle of investor redemptions occurs and prime brokers demand a reduction of leverage.





56

The fund’s NAV declines precipitously as the fund sells holdings in a distressed fashion.The distressed sale of assets negatively impacts market prices.

Source: Till (2007)

11. Discuss eight hypotheses explaining the market events of August 2007.

After reviewing a number of empirical results, Khandani and Lo developed the followingeight tentative hypotheses (based exclusively on indirect evidence) about August 2007:

1. The losses to quant funds in the second week of August 2007 were started by a

short-term price impact that was the consequence of a rapid “unwinding" of one or more large quantitative equity market-neutral portfolios.

2. The price impact of the “unwind” between Tuesday, August 7, and Wednesday,August 8, forced a number of other types of equity funds to drastically reduce their risk exposures, thus aggravating the losses of the majority of these funds on August

8-9.

3. Most of the unwinding and “de-leveraging” took place on August 7-9. The lossesstopped after Thursday, August 9, and a significant turnaround took place on Friday,

August 10.

4. This price-impact pattern led the authors to hint that the losses were the short-term

side-effects of an abrupt liquidation on August 7-8. In other words, the losses did notarise as a result of a hypothetical fundamental collapse in the primary economicforces that affect long/short equity strategies.

5. The authors point to the following likely factors as contributors to the scale of thelosses resulting from the unwind: (a) the massive recent growth in assets dedicatedto long/short equity strategies; (b) the trending decline in the effectiveness of quantitative equity market-neutral strategies; (c) the bigger leverage required to





57

preserve expected returns levels demanded by hedge fund investors; (d) the lack of realization about how popular the long/short equity category had become; and (e)

the unknown magnitude and timing of the mortgage-related problems surfacing inthe credit markets.

6. The presumed unexpected liquidation of one or more large quantitative equity

market-neutral portfolios occurred. Given that these portfolios are composed mainlyof exchange-traded instruments, the price shock of the quick unwind was swiftlyspread to other funds.

7. The authors find it reasonable to deduce that the systemic risk in the hedge fundindustry may have increased in recent years because of the following: thedifferences between the behavior of their test strategies in August 2007 and August

1998, the increase in average absolute correlations among hedge fund indices, the boost in the number of funds and the average assets under management per fund,and the expansion of credit-related styles among hedge funds.

8. The authors suggest that the continuing troubles in the subprime mortgage market

may activate more liquidity shocks in the more liquid hedge fundstyles (e.g. long/short equity, managed futures, and global macro). However, theyhypothesize that the harshness of the shock to the long/short equity style is likely to be subdued in the short-term because market participants now possess moreinformation regarding the magnitude of this sector and the possible price-impact of another potential liquidation of a long/short equity portfolio.

12. Illustrate an understanding of the terminology used to describe distinctcategories of fund strategies that fall under the broad heading of “long/short equity.”

Khandani and Lo refer to the following four seemingly dissimilar categories of fundstrategies as “long/short equity”:

1. Statistical arbitrage. This refers to “highly technical short-term mean-reversionstrategies” concerning large numbers of securities. They use very short holding periods and substantial computational, trading, and IT infrastructure.

2. Quantitative equity market-neutral. This is a more general category, concerning broader types of quantitative models, some with lower turnover, fewer securities,and inputs other than past prices such as accounting variables, earnings forecasts,

and economic indicators.

3. Long/short equity strategies. The third category is the broadest. Khandani and Lo

characterize long/short equity strategies as those that comprise any equity portfoliosthat employ short-selling, that may or may not be quantitative, that may or may not be market-neutral (for e.g., many long/short equity funds are net long), and wheretechnology does not necessarily play a significant role. Long/short equity strategies

usually represent the single largest category in most hedge fund databases in termsof number of funds and assets.

4. 130/30. This group, also called “active extension” strategy, is a relatively new

category in which a fund or managed account of, say $10MM, invests $13MM in





58

long positions in one group of securities and $3MM in short positions in another group of securities. Khandani and Lo argue that this strategy can be considered a

logical addition of a long-only fund, assuming that the long-only constraint has beenrelaxed to a limited extent.

13. Describe the anatomy of the long/short equity strategy. Explain how itis simulated in the paper, how the strategy provides liquidity to themarket place, how leveraged portfolio returns are constructed, therelationship between market capitalization and the strategy’sprofitability, and the practical implications of transactions costs.

We explain this learning objective by dissecting its parts.

Anatomy of the long/short equity strategy and how it is simulated:

Khandani and Lo estimate the effects of the events of August 6-10, 2007, on long/short

equity portfolios simulating a specific strategy originally proposed by Lehmann (1990)and Lo and MacKinlay (1990).

The strategy works as follows. Suppose there are N securities, and consider a long/shortmarket-neutral equity strategy that consists of allocating an equal dollar amount to longand short positions. At each rebalancing date, the long positions are made up of what

might be called the “losers” (stocks that have been underperforming relative to a givenmarket average m), and the short positions are invested in what might be called the“winners” (stocks that have been outperforming relative to the same market average m).In terms of an equation, if we denote ωit as the portfolio weight of security i at date t , thenwe have:

for some k > 0.

Notice that the portfolio weights are equal to the negative of the degree of outperformance k periods ago. Therefore, each value of k generates, to some extent, aspecial strategy. Khandani and Lo set k =1 to represent a day.

By buying yesterday's “losers” and selling yesterday's “winners” at each date, the proposed strategy is dynamically betting on the existence of a mean reversion across all N securities, thus profiting from turnarounds that take place within the rebalancing time

interval. As a result, equation (1) can be considered a “contrarian” trading strategy that iscapable of profiting from a market “overreaction”, that is, when underperformance(outperformance) is followed by positive (negative) returns.

− −

− −

=

= − −

≡ ∑1

1( )

1(1)

it it k mt k

N

mt k it k

i

R R

N

R R N

ω





59

How the strategy provides liquidity to the market place:

Contrarian trading strategies increase the demand for losers (by buying losers) and addto the supply of winners (by selling winners), thus providing liquidity to the marketplaceand helping stabilize any imbalances that may exist between supply and demand. Inoffering liquidity to the marketplace, contrarian trading strategies also achieve a

reduction in market volatility as they help mitigate price fluctuations by buying stocks for which there is excess supply and selling stocks for which there is excess demand.

How leveraged portfolio returns are constructed:

The portfolio’s return of an “arbitrage” or “market-neutral” portfolio (where long andshort positions exactly offset each other) cannot be calculated in the usual way becausethere is no net investment. In practice, however, the return of these strategies can becalculated as the profit-and-loss of that strategy’s positions divided by the initialinvestment that was required to support those positions. More specifically, the grossdollar investment I t of the portfolio presented in equation (1) and its unleveraged portfolio

return R pt can be computed by the following equation:

(2)

Then, to construct leveraged portfolio returns L pt (θ ) employing a regulatory leveragefactor of θ :1, Khandani and Lo multiply equation (2) by θ /2:

(3)

Relationship between market capitalization and the strategy’s profitability:

Khandani and Lo find that the average daily return of the strategy in the smallest decile isconsiderably larger than the corresponding return for the largest decile. This finding isconsistent with the smaller-cap pattern that has been long known by long/short equity

managers. The reason behind this finding may lie in the fact that smaller-cap stocksgenerally exhibit more “inefficiencies” and, hence, the profitability of the contrarianstrategy in the smaller deciles is significantly higher than in the larger-cap portfolios.

Practical implications of transactions costs:

A caveat of these findings relating to smaller-cap stocks is that the transaction costsinherent to trading smaller-cap stocks are typically very high. Therefore, in the realworld, the previous results may not be as attractive as the data might imply. This trade-off

=

=

≡

≡

∑

∑

1

1

1| |,

2

N

t it

i

N

it it i

pt

t

I

R R

I

ω

ω

=≡∑ 1

( / 2)( )

N

it it i pt

t

R L

I

θ ω θ





60

between apparent profitability and transaction costs suggests that the intermediate decilesmay be the most suitable from a realistic point of view.

14. Explain the return pattern of the main simulated strategy during thesecond week of August 2007.

Khandani and Lo simulate an unlevered contrarian return strategy applied on August 7,8, and 9, 2007, and find that the strategy yielded a cumulative three-day loss of -6.85%,which represents a staggering loss of 7.6 standard deviations, assuming independentlyand identically distributed daily returns. Actual losses were probably magnified several-fold as many long/short equity managers were employing leverage. Interestingly, a good portion of the losses was reversed on Friday, August 10, when the contrarian strategyyielded a return of 5.92%.

Khandani and Lo argue that this dramatic reversal is a revealing sign of a liquidity trade.The extraordinary return patterns observed in the second week of August 2007 can beexplained as the result of broad-based momentum induced by a major strategy

liquidation. Once the liquidation had ended, the liquidation-based momentum reversedinto a strong mean reversion that caused Friday’s extraordinarily positive returns.

15. Compare and contrast market events in August 2007 with August 1998.

Khandani and Lo argue that there exists one important difference between August 1998(around the time of the Long Term Capital Management (LTCM) debacle), and August

2007. They say: “ In contrast to August 2007, where an apparent demand for liquidity

caused a re-sale liquidation that is easily observed in the contrarian strategy's daily

returns, the well-documented demand for liquidity in the fixed-income arbitrage space of

August 1998 had no discernible impact on the very same strategy.” This is a verysignificant difference that is consistent with a greater degree of financial integration,

including the possibility of contagion among markets, in 2007 than in 1998.

According to Khandani and Lo, the differences between August 1998 and August 2007are the result of several possible interpretations. One explanation is that in 1998, therewere less multistrategy funds and proprietary trading desks participating in both fixed

income arbitrage and long/short equity. As a result, the demand for liquidity caused byfailing fixed income arbitrage strategies did not spread out as willingly to long/short

equity portfolios. A second possibility is that the magnitude of the capital invested inlong/short equity strategies was not sufficiently large to cause any major disruption,even if such strategies were unwound rapidly in August 1998. Finally, a third possibleexplanation is that in 1998, long/short equity funds were not as leveraged as it appears

they were in 2007. The authors argue that all three of these explanations may be at least partially valid.

16. Explain how the increase in total assets under management and thenumber of long/short funds over the 1998 to 2007 time period likelyimpacted expected returns and the use of leverage.

Khandani and Lo estimate that, excluding strategies such as 130/30 funds, the totalnumber of long/short funds and the average assets per fund grew exponentially since





61

1994 (beginning at $62MM in January 1994 and ending at $229MM in July 2007). Thisimplies greater competition and, consequently, a diminishing profitability arising from

the strategies used by such funds.

The authors speculate that, as these strategies began to yield decreasing returns, hedgefund managers started to increase leverage in order to preserve the expected returns that

investors anticipate. However, even though leverage offers the potential to magnify small profit opportunities into larger profits, it also has the potential risk of escalating smalllosses into larger ones. More leverage also implies that the magnitude of the positions isoften significantly larger than the size of collateral placed to hold up those positions. Thissituation can be very precarious because when unfavorable market fluctuations shrink themarket value of collateral, credit is withdrawn rapidly. This can lead to an abrupt

liquidation of large positions over very short time periods and can wreak havoc infinancial markets, in a way similar to what happened in August 1998 and in August 2007.

17. Describe the set of hypotheses that are collectively referred to as the“unwind hypothesis.”

Khandani and Lo argue that the fact that the leveraged contrarian strategy that theysimulate lost -4.64% on Tuesday, August 7, and then -11.33% on Wednesday, August 8, points to an abrupt liquidation by one or more equity market-neutral portfolios of largesize. They also note that the timing of these losses was just a few days after the end of July, which was a very difficult month for many hedge funds, and propose that the events

of August 7-9 may well had been the first time that hedge funds were strained to dealwith the astonishing credit-related losses they had experienced in July. This realizationmay, in turn, have activated the first unwind of their more liquid positions (basically their equity portfolios) during this period.

Another significant pattern that the authors uncovered is the fact that the losses on August

7 and 8 were most dramatic for some of the intermediate-decile portfolios, a finding thatis consistent with a statistical arbitrage unwind.

Confronted with the large losses of August 7-8, most of the affected hedge funds would probably have been forced to cut their risk prior to Thursday’s market open by “de-

leveraging” (either on a voluntary basis or because they had surpassed borrowing limitsestablished by their creditors). As the authors point out, this was a sensible practice that,unfortunately, also proved to be disastrous.

Friday’s enormous turnaround seemed to confirm that the losses of the preceding threedays were caused by an abrupt liquidation, and not by any structural change in theequilibrium returns of long/short equity strategies. Structural changes would probably

have had a more enduring effect on prices.

18. Discuss one proposed measure of illiquidity of long/short equity fundsand how the results have changed over the past decade.

Khandani and Lo argue that a significant decrease in liquidity of long/short equity strategies over the past decade has probably occurred because the number of long/short

equity funds, the level of assets per fund, and the leverage that each fund uses have allincreased throughout that period. They then propose to measure the illiquidity of





62

long/short equity funds using the first-order autocorrelation coefficient of their monthlyreturns, as originally put forward by Lo (1999) and Getmansky, Lo, and Makarov (2004).

Results using the proposed measure of illiquidity for the period from December 1994to June 2007 imply that, apart from for a short-lived increase in late 2004, asignificant decline in the liquidity of long/short equity funds occurred over the past

six years as suggested by an ever increasing autocorrelation (i.e. the correlation between the return for a hedge fund and its lagged return from the previous month)since 2000. More importantly, they argue that the fact that the autocorrelations haverisen at all in the most crowded, and among the most liquid, of all hedge fund sectorsrepresents yet another clue that systemic risk in the hedge fund industry has risen inrecent years.

19. Describe a method for approximating a network view of the hedge fundindustry and what such a view indicates.

Although credit and liquidity are separate sources of risk exposure for hedge funds and

their investors, the two have been generally perceived as being inevitably entangled sincethe LTCM collapse in August 1998. In spite of recent progress achieved in modelingcredit and liquidity risk, the intricate network of creditor/obligor interactions still needs to be correctly mapped. In this regard, Khandani and Lo present an approximation of the useof some of the techniques developed in the theory of networks to design systemicmeasures for liquidity and credit risk exposures and improve the strength of the world’s

financial system to idiosyncratic shocks.

Khandani and Lo caution that the opaqueness of the hedge fund industry does not allowthem to collect the data needed to approximate the “network topology” that is the initial point of these procedures. In spite of this, they proceed to calculate the changes in theabsolute values of correlations between hedge fund indices over time as an indirect and

rudimentary estimation of the change in the “degree of connectedness” in the hedge fundindustry. Khandani and Lo find that the hedge fund industry has undoubtedly becomemore closely connected. This is because the multi-strategy category exhibits now a higher correlation with almost every other index. This higher correlation also yields support tothe hypothesis that factors outside the long/short equity segment may have produced anunwinding of arbitrage strategies in August of 2007.

The authors caution that it is possible that the variation in correlations may have been dueto volatility shifts and not due to changes in the covariances of returns. They also warnthat it may not be possible to gather the required data to draw the network topologywithout resorting to additional regulatory supervision, given the fact that the hedge fund

industry protects its intellectual property by being secretive about its trades.

20. Evaluate the statement: Quant failed in August 2007.

Khandani and Lo comment that it would be easy to conclude that the losses resultingfrom the events of August 2007 were the result of a “fire-sale liquidation” of quantitatively constructed portfolios, rather than the particular limitations inherent to the

quantitative methods they use. If this was the case, the fire sale would have come about asa result of an underestimation of risk on the part of hedge funds.





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The authors also contend that while market participants will most likely advance their strategies and risk management practices as a result of the events of August 2007, it is

implausible that the likelihood of potential disruptions can be entirely eradicated by suchimprovements. This is because events such as this may merely be an inevitable aspect of equity market-neutral strategies. Furthermore, the risk/reward profiles of these strategies

can be thought of as providing small but stable positive returns nearly all of the time, tiedwith sporadic short-lived considerable losses. Such profit-and-loss pattern can be fairlyattractive to those investors that are aware of the meaning of “tail risk” and whose

individual risk preferences allow them to endure the unavoidable “rare event”.

Khandani and Lo leave open the answer to the question “Did ‘Quant’ fail?” when theyconclude that: “Quantitative models may have failed in August 2007 by not adequately

capturing the endogeneity of their risk exposures. Given the size and interconnectedness

of the hedge fund industry, we may require more sophisticated analytics to model the

feedback implicit in current market dynamics... (However) If all three sets of stakeholders

- managers, investors, and creditors - were aware of the risks and willing to bear them,

then August 2007 is merely the cost of doing business. If not, then August 2007 signaled

another kind of failure in this industry.”

21. Critique the methodology of the article.

Khandani and Lo caution that, even though their unwind hypothesis is consistent with theevents that occurred during the week of August 6-10, 2007, all of their inferences should

be taken as tentative, indirect, and too recent to be pondered correctly. This is because of the following five reasons:

1. The authors did not have access to inside information about the hedge funds thatwere impacted by the crisis in August 2007, nor to information on prime trading or brokerage records, or industry leverage statistics.

2. Their empirical results were based on only one simple trading strategy applied toU.S. stocks. This strategy may or may not be representative of certain short-termmarket-neutral mean reversion strategies.

3. The “outsider’s perspective” of the methodology used by Khandani and Lo does not

allow them to determine whether or not the early losses on August 7 were the resultof a forced liquidation or of a deliberate risk reduction by hedge funds.

4. Results were based on information that was available in the TASS hedge fund

database, which only has data for hedge funds that have voluntarily decided to beincluded in the database. The authors admit that they had no means to ensure that thefunds in the database were representative of the industry or of a particular style. Infact, many of the hedge funds that made headlines in August 2007 were notavailable in the TASS database.

5. Finally, the authors were not able to test the hypothesis that liquidations of various

investment strategies and asset classes may have started before the week of August6-10, 2007.





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22. Evaluate the current outlook for systemic risk in the hedge fundindustry.

Khandani and Lo argue that by providing liquidity, facilitating the transfer of risk,engaging in price discovery, and discerning new sources of returns, the hedge fundindustry has, on the aggregate, facilitated economic growth and provided social benefits.

They also argue that hedge funds have evolved to become more similar to banks, anindustry that is vastly regulated due to the negative externalities banks cause when theyfail, and in spite of the positive externalities banks generate when they do well. However,unlike banks, hedge funds can withdraw liquidity at any time, and a synchronizedliquidity withdrawal among a large group or a whole segment of funds could have potentially devastating effects on the basic functioning and viability of the global

financial system.

If hedge funds have amplified systemic risk, then Khandani and Lo argue that we wouldneed to know “by how much?” and “do the benefits outweigh the risks?” Althoughnobody would argue that the systemic risk for the financial system would completely

disappear, we still need to know the optimal or acceptable level. Khandani and Losuggest that the first step needed to tackle this problem consists in making efforts to

understand the probability and causes of systemic risk in the financial system and how tomeasure it.

One possibility is the suggestion made by Getmansky, Lo, and Mei (2004) to create a National Transportation Safety Board-like organization for capital markets to supervise

different features of systemic risk, and by creating procedures for improving models andmethods that are typically used. A “Capital Markets Safety Board” may be a more directway to deal with the systemic risks inherent to hedge funds than registration, as somehave suggested, because the latter would not tackle the systemic risks that the hedge fundindustry may create in the financial system.

23. Describe a subprime loan and discuss the four principal reasons for therecent increase in subprime loan delinquencies.

Subprime mortgages typically have a 200- to 300-basis point interest premium above

prevailing conventional or prime mortgage rates. Alt-A mortgages are issued to borrowers who have better credit scores than subprime borrowers but fail to providesufficient documentation with respect to all sources of income and/or assets. In terms of credit risk, an Alt-A mortgage falls between prime and subprime borrowers.

Four principal reasons for the recent increase in subprime loan delinquencies are:

1. The subprime borrowers were not creditworthy. They were highly levered withhigh debt-to-income ratios. The loans had high loan-to-value ratios. Furthermore,many of the mortgages allowed the borrower to borrow the down payment for thehome.

2. The use of short reset loans. These would have low teaser rates for the first two or three years, referred to as 2/28 and 3/27 hybrid subprime ARMs. The rates wouldincrease after the initial period.





65

3. The slowing in the rise in home values, which made it more difficult for subprime borrowers to refinance.

4. A decline in credit standards by mortgage originators in underwriting to meet thedemand for high-yield assets.

24. Explain the economic motivations that enabled the waterfall paymentstructure of an ABS trust or CDO structure with a collateral poolconsisting of high-yield securities to attain an investment grade ratingfor the securities they issued and the resulting contribution to thecredit crisis.

Asset backed security (ABS) trusts became the owners of the loans securitized by theoriginators. They generated net present value from the repackaging of the cash flows andcould absorb some losses. The managers would perform simulations to obtain a lossdistribution that allowed the determination of the credit enhancement (CE), which is theamount of loss on the underlying collateral that can be absorbed before the tranche

absorbs any loss.The ABS trust or CDO would run the collateral’s cash flows through a waterfall

payment structure. In a waterfall payment structure, the cash flows are assigned to arange of low grade to high grade tranches. The high grade or “senior bonds” are paidfirst, and the junior tranches do not get paid if the collateral pool becomes stressed incertain ways, e.g., a change in the collateral/liability or cash-flow/bond-payment ratios.

Furthermore, insurance purchased from a monoline insurer called a “surety wrap” couldincrease the credit status of the senior tranches, despite that fact that the underlyingcollateral was subprime mortgages.

There were many economic motivations that enabled the waterfall payment structure of an ABS trust or CDO structure with a collateral pool consisting of high-yield securitiesto attain an investment grade rating. Those economic motivations include:

1. the desire to lower costs and rely on the ratings so that pension funds and insurancecompanies had a disincentive to perform their own due diligence;

2. rating agencies would get paid fees for monitoring assets that were given aninvestment grade rating, and this would incent the agency to issue more investmentgrade ratings;

3. the CDO trusts being rated knew the procedures and had a fixed target to meet to get

an investment grade rating,

4. mortgage originators had no incentive to perform due diligence and monitor

borrower’s creditworthiness,

5. special investment vehicles (SIVs) had to achieve investment grade status in order to survive and did whatever it took to meet that goal,

6. monolines were perceived to have low risk and therefore had an incentive toincrease leverage, and

7. Basel II allowed banks to hold AAA assets as collateral so there was an increaseddemand for such assets.





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25. Explain the role of rating agencies in the credit crisis.

The actions of ratings agencies played a huge role in the credit crisis. Many investorsrelied on the ratings for a diverse set of products such as asset-backed commercial

paper (ABCP), mortgage bonds, derivative product companies (DPCs), and monolines which insure municipal bonds, and structured credit products such as tranches of CDOs.

Many institutional investors are restricted to only investing in assets with a high creditrating. These investors rely heavily on the risk assessment of credit agencies. As long asthe asset has an adequate credit rating, the other characteristics and complexities would be overlooked by these institutions. The institutions overlooked the fact that these assets

rated AAA had higher returns than comparable corporate assets, which should have beena signal that there was an error in the rating.

Finally, the rating agencies had an incentive to issue AAA ratings because they were paidfor the rating, and they were also paid fees for monitoring the entities to which they hadgiven such ratings.

26. Criticize the incentive compensation system for mortgage brokers andlenders and its adverse effect on the due-diligence efforts at the firms.

The basic problem with the system was that brokers and lenders had little or no incentive

to perform due diligence and monitor borrowers’ creditworthiness. This was becausemost of the subprime loans originated by brokers were securitized after the origination of the loan. Securitization is the packaging of loans and selling them to other firms to getthem off the loan originator’s balance sheet. The loan originator, e.g., a bank, got paidfrom the origination of the loan and suffered few, if any, negative consequences if the

loan defaulted.

27. Explain the factors affecting the rating of a special investment vehicle(SIV).

The acronym SIV refers to either a special investment vehicle or structured investment

vehicle. An SIV is a limited-purpose, bankruptcy-remote company that purchases mainlyhigh-rated medium- and long-term assets from its parent company. The SIV funds these purchases with short-term asset-backed commercial paper (ABCP), medium-term notes(MTNs), and subordinated debt capital.

The factors that affect the rating of SIVs are the usual set of risks: credit, liquidity,market, interest rate, foreign currency, and managerial and operational risk.

i) Credit risk addresses the creditworthiness of each obligor and the risk during what is

called the wind down period associated with credit deterioration.ii) Liquidity risk is the result of the need to refinance because of a maturity mismatch

between assets and liabilities.

iii) Market risk is the change in prices from changes in the market, which managersmust address by marking-to-market and marking-to-model the liquid and illiquidassets respectively.





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iv) Interest rate risk is the risk the present value will change from a change in interestrates.

v) Foreign currency risk is the risk of a change in value from changes in the value of currencies in which investments have been made.

vi) Managerial and operational risks refer to losses from errors and/or criminal acts of employees.

28. Describe the role of monolines.

Monoline insurers guarantee the payments from certain types of assets. They are highly

leveraged, and yet they carried a AAA rating which, in turn, gave a AAA rating to theassets they backed. They began in the 1970s as entities to insure the debt of hospitals andnonprofit groups. In recent years, much of their growth has been from backing structuredABS and CDOs.

The monolines contributed to the financial crisis because the when they weredowngraded, the assets they backed were downgraded.

29. Explain the lack of incentives for banks to perform due diligence on thecollateral pool.

The banks invested in AAA rated securities, as the Basel II regulations allowed suchassets to serve as bank regulatory capital. Structured securities that had AAA ratedtranches were thought to be acceptable. The rating lowered the incentive of the bank to perform due diligence.

Another issue was managers’ short-term horizons. They generally focused on their year-on-year bonuses, and this further lowered the incentives to do due diligence. The worsethat could happen to managers is that they could lose their jobs. This was not a very big

disincentive because the market environment was such that there were plenty of other jobs to be had, and even a failed manager could get rehired at another institution. Thus,the managers had little incentive to consider the risk exposure from the possible writedown of the monolines which would, in turn, lower the rating of the assets they held.

30. Explain the role and actions of central banks in 2007 and early 2008.

In mid 2007, the Federal Reserve indicated that the spillovers from the subprime marketshould not affect the overall economy. Central banks around the world provided capital to banks. For example, the Russian Central Bank injected the ruble equivalent of $1.7 billion into the banking system. Also, the European Central Bank pumped money into

Europe’s money markets, and the Fed did the same in the U.S.In the spring of 2008, the Fed introduced a new lending facility: the Primary Dealer Credit Facility (PDCF). The PDCF allowed investment banks and securities dealers touse a wide range of securities as collateral for loans from the Fed.

31. Explain the role of valuation methods.

The fair value accounting framework has three levels of evaluation: 1) market prices, 2)

prices of related assets, and 3) model valuation for illiquid assets. The valuation methods





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allow for the estimation of fair value of a wide range of assets from liquid assets toilliquid assets.

The valuation methods contributed to the crisis because the models had uncertain parameters, especially in turbulent markets. Furthermore, the uncertainty about whichvaluation methods to use could lead to disagreements between borrowers and lenders,

which in turn could lead to the selling of assets and cause funds to close. This wouldincrease market turbulence further.

32. Describe the lack of transparency in the credit markets.

Lack of transparency has been endemic on many levels. Here is a list of reasons for thelow level of transparency.

1. Methods of valuation are often not known, which leads to price uncertainty.

2. The basic information concerning the percentage of CDOs and subprime mortgagesin a fund is rarely readily available.

3. For institutions, there is lack of transparency as to the total magnitude of commitments given in terms of backstop lines of credit or loan commitments to private equity buyouts.

4. Banks may hold or “warehouse” sub-prime assets with the intention of securitizingand selling them, and the extent of these holdings are unknown to outside investors.

5. The general level of complexity of the assets leads to a low level of transparency.

33. Describe how systemic risk arose in 2007.

Systemic risk is the degree to which events in one market will affect other markets. Itarose in 2007 when money market managers who normally purchased Asset Backed

Commercial Paper (ABCP) switched to Treasury bills and drove up the Treasury yields.This increased the perceived risk of subprime mortgages and the general level of risk aversion. The ABCP market basically closed. This in turn led to some borrowers not being able to roll over debt even when they were not involved in the sub-prime market.Hedge funds had to sell assets to raise cash, and this depressed prices of various types of assets.

Many SIVs have backstop lines of credit from banks. The uncertainty led to bankshoarding cash and restricting loans to other banks. The LIBOR rate increased. Thereluctance to lend and the tightening of credit standards affected hedge funds, theavailability of residential and commercial mortgages, bond auction markets, and lending

to businesses.Regulators failed to recognize the existence of positive feedback mechanisms and tounderstand their implications for the financial system. One reason for the systemic natureof the crisis was from the widespread ownership of structures containing subprimemortgages and the circular dependence between refinancing and collateral valuation. If asset values decline, ability to refinance declines, valuation of counterparty collateral

declines, value of monoline assets declines, and the value of the guarantees given bymonolines declines.





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34. Argue how increased transparency in the rating process is necessary.

Most of the recommendations for dampening the effect of future crises require anincrease in transparency. It is necessary to know, or have, transparency with respect towhat a rating means, how the rating was calculated, and the nature of the data used in thecalculation. This is the only way investors can have confidence in the ratings.

Some of the needs for transparency are specific for certain assets. For collateralizedstructures, there is the need for more transparency concerning the types of models, theassumptions used to rate a particular structure, and the accuracy and robustness of themethodologies and assumptions. For SIVs, there also needs to be transparency withrespect to the backstop lines of support in the case of disruptions so investors know therisks.

There are four basic recommendations that could dampen the impact of future crises:

1) There should be a clear definition of the meaning of a rating. It should be knownwhat a rating implies with respect to the probability of timely payment and expected

loss. If a rating is through-the-cycle, then the length of the cycle should be known.2) The method of calculating a rating should be known and reproducible by an

independent third party.

3) The government should create an agency that collects and makes availableinformation on collateralized pools.

4) There should be transparency with respect to the source and nature of the data usedin the calculations.

35. Argue how standardization can simplify valuation issues.

The standardization of instruments has already proved valuable in the market for swaps.

There are standardized maturities of 1, 3, 5, 7, and 10 years. This standardization meansthat models have very reliable prices as inputs. This has simplified valuation in thismarket because the models can be calibrated to match these current prices. Such moves

towards standardizing issues in other markets would likewise improve the valuationmethodologies in those markets.

36. Assess the hidden risks of implicit and explicit off balance-sheet bank commitments and argue how increased transparency can provide

investors with information regarding financial institutions’ exposure.

Banks had a variety of hidden risks on their balance sheet. Current 10-K statements of financial institutions offer little information about the level of the total off balance sheet

commitments of banks, and an increase in transparency would provide investors withinformation regarding financial institutions’ exposure.

One type of explicit commitment occurs when providing financing for a levered buyout

without the protection of an “adverse market,” which can provide an escape from a baddeal. Another explicit commitment comes about when banks gave backstop lines of creditto their sponsored SIVs without specifying the total level of these commitments.Increased transparency would alert the investors of the level of risk concerning thesecommitments.





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Another type of implicit commitment arose when banks would bring back assets fromtheir SIVs to protect the reputation of the SIV. Another implicit commitment is in

enhanced money market funds that invested in subprime mortgages, and banks wouldstep in to support such funds to keep the value from falling below zero, i.e., to prevent thefunds from breaking the buck. Increased transparency would alert investors that these

commitments exist and the type of events that banks have committed themselves tocovering.

An increased level of transparency can provide investors with information concerningthese exposures. Any increase would be an improvement, and 10-K statements shouldoffer information about assets being warehoused and the level of the total off balancesheet commitments of banks.

37. Describe how new product design can dampen market disruptions.

New indices, like the CDS indices introduced in 2002, can improve transparency. Theseindices increase the availability of information on bid-ask spreads and pricing. To

improve transparency, sub-indices in some areas are recommended.The development of new options can allow institutions to protect against risk.Commercial paper can be designed, for example, to allow an SIV to convert it into a one-or two-year note if certain market conditions exist. This would make SIVs less sensitive

to market disruptions, albeit at the cost of the option.

38. Discuss possible regulatory responses.

The Group of 7 finance ministers and Central Bank governors has been presented with 67recommendations by the Basel-based Financial Stability Forum. Many of therecommendations address improvements in transparency and an increase in regulatoryoversight. In general, the recommendations advise increased capital requirements for structured products, transparency of the risk exposures on the trading book, faster disclosure of losses by banks, clearing houses for OTC trades, and increased cross-border

monitoring. The following list provides more details:

1) There should be consistent regulations and oversight and a lower degree of fragmentation of regulations.

2) At the Federal level, there should be minimal lending standards to avoid locallobbyists from lowering standards in certain areas.

3) There should be random sampling of loan applications for approved loans to makesure standards are met.

4) Banks must hold a randomly selected number of mortgages and a specified portionof the equity tranche of ABS composed of their mortgages.

5) Regulators need to address the effects of wrong-way counterparty credit exposure indetermining capital requirements and the effects of procyclicality in stress testing.

6) There should be regulations against cherry-picking the placement of assets on either the bank book or the trading book at the time of purchase.





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7) Rating agency methods need to be improved and made more transparent. Also, thereneeds to be a lowering of the conflict of interest of these agencies.

8) Centralized clearing houses (CCHs) for OTC transactions should be establishedthat would monitor the risk exposure of participants, to make sure they havesufficient collateral, and stand ready to back defaults.

39. Describe sound risk management practices.

According to Crouhy, Jarrow, and Turnbull (2008), a proven set of risk management practices includes:

1) adopting a comprehensive view of exposures with the sharing of quantitative andqualitative information across the organization,

2) establishing processes to value complex and illiquid securities,

3) enforcing active controls over the consolidated organizations balance sheet, liquidityand capital positions, and

4) relying on a wide range of risk measures.

Other practices would include integrating liquidity, credit market, and finance controlstructures. Managers should take a more active role in balancing the need to develop new business and the level of risk the firm can assume. This would also mean better aligningthe compensation of managers to the quality, and not just the quantity, of loans issued.

40. Describe nonlinearities in the risk of subprime CDO tranches.

Limited liquidity and other complexities introduce nonlinearities in the risk of the

subprime CDO tranches. One reason is that a typical subprime CDO has a pool of assets composed of MBS bonds, rated BB to AA, with an average BBB rating, but the

BBB group is relatively small. A relatively small default rate could hit and wipe outthe BBB group fairly quickly. This would mean the super senior tranches would soon be hit. There is essentially a binary situation where either the cumulative default rateof the sub-prime mortgages remains below the threshold that keeps the MBS bonds

untouched and the super senior tranches do not incur any losses, or the cumulativedefault rate exceeds the threshold and the senior tranches are dramatically affected or even wiped out.

References

Till, H. “Amaranth Lessons Thus Far.” The Journal of Alternative Investments. Spring 2008.

Khandani, A.E., and A.W. Lo. “What Happened to the Quants in August 2007?” Journal of Investment Management. Vol. 5, No. 4, 2007.

Crouhy, M., R. Jarrow and S. Turnbull, “The Subprime Credit Crisis of 2007.” The Journal of

Derivatives, Fall 2008.






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TOP I C 10

Portfolio and Risk Management

M a i n P o i n t s Comparing methods of hedging against tail risk and their relative merits

Explaining the implications of complex and adaptive capital markets for risk management methods

Evaluating factors leading to portfolio allocation drift

1. Assess the long-run and short-run benefits of hedging the tail risk of aportfolio.

Tail risk refers to extreme and rare events that can produce large losses. Hedging tail

risk is defensive in the short term, but is offensive in the long term. In the short-term,

it lowers risk, which increases the chance of survival. In the long term, it allows

those that were defensive to be offensive because they can take advantage of the

reduced liquidity that accompanies tail events and position themselves for attractive

prospective returns.

2. Explain the relationship between systemic risk, liquidity risk, monetarypolicy and other macro events.

Systemic risks are characterized by periods where there is an increase in the demand for

liquidity, but few, if any, entities are willing to provide it. It puts pressure on the ability

to fund levered holdings. Liquidity is a macro risk, which must be measured using macro

models.

Tail risk is macro risk, which includes risks associated with monetary policy. Early and

late periods of Federal Reserve monetary easing and tightening are correlated with early

and late expansions. Macro instruments that respond to Fed activity can hedge the tail

events in these periods. Deleveraging risk, for example, is a monetary policy risk, i.e., it

is a tail event that is the result of macro events.

Modeling this type of macro risk involves addressing improbable, high-severity

scenarios. However, relating tail risk to macro risk simplifies the construction of the

hedges. Macro markets are the bond, stock, foreign exchange, credit, commodity markets.

Since these markets are typically liquid and deep, some forms of insurance is usually

available at an attractive price.



Topic 10: Portfolio and Risk Management


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3. Explain why increased correlation among various asset returns duringperiods of stress could provide opportunities for free insurance againsttail risk.

When systemic risk increases, the macro markets become more correlated. Although this

is bad in general, it is beneficial from a hedging strategy perspective. It means that a

hedge in one market, e.g., the bond market, can be a hedge for all the others as thecorrelation increases in all the markets.

4. Describe the four approaches to hedging or insuring a portfolio againsttail risk.

The four approaches to hedging or insuring a portfolio against tail risk are buying insurance

securities, buying options, investing in assets negatively correlated with tail risk, and moving

the portfolio off the optimal frontier. Each is described in more detail below.

1. Buy high quality “insurance securities”. The best example of these are short-term U.S.

Treasury instruments. In times of crisis, the flight to quality will increase the value of

these assets. The hedger must be careful to make sure the assets are not already

overpriced.

2. Buy contingent claims, or “option-like” securities. In some cases, there are mispricings

that allow the portfolio manager to get a significant amount of protection against

systemic risk at a very low cost.

3. Invest in strategies that are negatively correlated to tail risk . One such strategy is the

trend-following managed futures strategy, which has exhibited positive correlation to

tail risk indicators, e.g., the CBOE Volatility Index (VIX). As an added benefit, as a

strategy, managed futures are uncorrelated with the stock market.

4. Move the portfolio off the optimal frontier. Although this seems counterintuitive, thereasoning is that the optimal frontier is only optimal with respect to the second

moment, i.e., the variance. Also, the efficient frontier assumes the manager has perfect

forecasting ability concerning the mean and variance, which is hardly true. Some

assets such as spread products that have large higher moments can move a portfolio

out into the optimal frontier while adding tail risk . Such spread products include

corporate bonds and low-quality mortgages, which have higher yields because of

embedded default and illiquidity options.

Although each of these strategies insure the portfolio against tail risk to some level, they

are not what would be termed portfolio insurance strategies that the manager would

dynamically adjust in response to changing market conditions.

5. Explain why dynamic strategies such as portfolio insurance cannot beused to hedge against tail risk.

Dynamic strategies cannot be used to hedge tail risk because they require the ability to trade

the assets used to hedge, and the liquidity of these assets typically declines in a crisis. Thus,

the ability to engage in a dynamic strategy when a tail event or crisis occurs will be greatly

reduced. That is why the four strategies mentioned above (buying insurance securities,





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buying options, investing in assets negatively correlated with tail risk, and moving the

portfolio off the efficient frontier) have advantages in hedging tail risk .

6. Describe the three factors that impact the construction of a tail hedge.

The three factors that affect the construction of a tail hedge are:

1. the scenario behavior of the portfolio;

2. the scenario behavior of the hedges after deducting costs; and

3. the probability of the scenario occurring.

In order to find the best combination of the three factors, the manager must know how the

portfolio behaves under certain extreme scenarios where shocks occur. The manager

would then examine the stress scenarios where the shocks occur and find the combination

of hedges that controls the risk of the factors that produced the stress scenario. By having

a hedged portfolio when stress scenarios occur, the manager will have liquidity relative to

other portfolios and survive the stressful period, which allows the manager to plan over

multiple periods.

7. Explain why long-dated options may provide an inexpensive method forhedging tail risk.

Mispricing can exist for long-dated options, which is why they can provide an

inexpensive method for hedging. One reason for this is that there is also a natural habitat

formation of option market participants. Another reason is that models used by

participants generally focus only on the short term. The model must compute the various

“Greeks”, e.g., the deltas, gammas, vegas, etc. Without the available reference

information, the calculations of these will have higher error, which leads to a greater

amount of mispricing.

Another source of mispricing is the error in estimating the probabilities. Simulations

based upon past observations are generally unsatisfactory in estimating probabilities for

the pricing of tail options. Furthermore, the probability calculation is less important than

the knowledge that the potential hedges exist at the right price. Thus, tail hedges are

usually cheap for long-lived portfolios.

8. Evaluate the factors that lead to the underpricing of risk by investors.

The main factor that leads to the underpricing of risk is the focus on getting higher

returns. By focusing only on how to get higher returns, two other factors begin to play a

role: i) investors find themselves more accepting of new and riskier product structures,and ii) there is a tendency not to recognize the uncertainty of outcomes. Examining the

recent credit crises provides better insight into these two factors.

i) Investors find themselves more accepting of new and riskier product structures: As yields

on high-yield bonds fell in the years leading up to 2007, there were developments such as

more questionable loan guarantees and fewer loan convents. There was also an increase in

the use of leverage to try to enhance already falling returns.





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ii) A tendency not to recognize the uncertainty of outcomes: The pursuit of higher

returns also makes investors more open to investing in risky products. Many financial

innovations were constantly introduced in the financial market, but the complexities of

these products were not fully understood. Investors did not know or fully understand

the outcomes of these products.

9. Explain the relationship between the real economy and capital marketsand discuss the factors that have made the real economy less volatilethrough time.

According to the Capital Asset Pricing Model (CAPM), assets only earn a return based upon

their exposure to systematic risk. Systematic risk is caused by undiversified movements in the

macro “real economy,” and ultimately, it is the real economy that determines the return and

risk that cannot be diversified away. Over the long term, sustainable aggregate GDP growth

sets an upper limit on the growth of earnings in the corporate sector.

Although there is a link between the levels of growth of GDP and earnings, there is a

weak link between the volatility of corporate earnings growth and the volatility of thegrowth of the overall economy. Corporate earnings growth and stock market prices have

been much more volatile than the growth of the overall economy. Since 1947, for

example, the standard deviation of real equity returns and earnings growth have been

13.6% and 10.6%, respectively. The standard deviation of GDP growth has only been

2.3%. Furthermore, the variability in stock returns and variability in earnings growth have

remained constant, while the variability in the growth of the real economy has declined.

Four factors are behind the downward trend in the volatility of growth rates in the real

economy:

i) increased diversification of industries with the development of new technologies;

ii) increased importance of the emerging markets, which has also had a diversificationeffect, as does the increased number of industries;

iii) increased understanding of the workings of the economy by the central banks aroundthe world; and

iv) safety-net government programs.

In summary, there has been an increase in the diversification of the real economy from

the emergence of new industries and new markets. Furthermore, there has been an

increase in the effectiveness of monetary authorities to stabilize growth, and of

governments providing programs to prevent the economy from declining too quickly.

10. Discuss why capital markets are complex and adaptive and explain theimplications of these characteristics for models of risk measurement.

Capital markets are complex adaptive systems. They are complex because the economic

system is made up of many interactive agents, and their decisions relate to and influence

each other in nonlinear and unanticipated ways.

Capital markets are adaptive because the agents in the markets can change their behavior

when confronted with new situations and allow the system to evolve and benefit from the





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changing environment. Furthermore, the agents are not purely quantitative and

emotionless entities. Although rational to some extent, there are behavioral aspects to

markets, and they essentially evolve similar to living creatures.

The behavioral and evolutionary aspects of markets are why standard, traditional statistics

with smooth quantitative functions fail to describe financial activity. New methods such

as jump diffusion, GARCH, copulas, and other advances have led to the development of sophisticated models that can better predict the risk-return tradeoff in markets. However,

even these models cannot predict all outcomes, and the models may make unrealistic

assumptions concerning the way individuals and markets act.

11. Compare and contrast the terms risk and uncertainty.

Frank Knight ( Risk, Uncertainty, and Profit , 1921) cautioned that there is a difference

between risk and uncertainty. Specifically, we are able to quantify risk because there are

distinct outcomes, and the probabilities of risk are also distinct. A bet or “investment”

based upon the flip of a fair coin or the roll of a fair die would be based on risk. The

outcomes are known and so are the probabilities.Uncertainty, however, is not deterministic. Uncertainty means that the outcomes and

probabilities are not known. To the extent that some outcomes and probabilities are

known but many are not, financial markets have both risk and uncertainty. Exposure is

another factor when dealing with risk and uncertainty, and it is the degree to which an

investor should be concerned about possible outcomes.

12. Explain the role of the shadow banking system as a source of liquidityand discuss why during periods of market stress this source of liquiditymay disappear.

The shadow banking system consists of levered intermediaries that are largelyunregulated. They seek profit, which comes with risk. The shadow banking system

consists of intermediaries that provide liquidity and includes hedge funds and structured

conduits provided by SIVs (special investment vehicles). The structured conduits are

collateralized loan obligations (CLOs) and collateralized debt obligations (CDOs).

This shadow banking system is important for the financial markets as it provides

liquidity by taking on risk. The pool of capital is directly linked to the changing level of

liquidity. Thus, a decrease in risk aversion (an increase in the desire for risk and its

corresponding profit) will increase the availability of liquidity. In other words, an

increase in the desire for profit and the willingness to take on more risk increases

leverage and liquidity through vehicles like CDOs and SIVs.

The shadow banking system is not backed by a central bank and is largely unregulated. This

is the reason why liquidity can dry up when the public’s risk tolerance declines. Liquidity can

literally disappear from the shadow banking system in a very short period of time simply

from a change in the mood of investors. The liquidity withdrawal can lead to the forced

selling of assets, which increases systemic risk and creates a downward Minsky-type spiral

of de-levering and collateral-related liquidity-seeking by levered investors. This increases risk

aversion and reduces liquidity further, producing a liquidity conundrum.





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In summary, liquidity is dependent upon the risk aversion (appetite) of investors. If risk

aversion increases (appetite for risk decreases), liquidity will decline. This will create

market stress from the forced sale of assets, which increases risk aversion further, lowers

liquidity and asset prices further and continues the decline. This relationship between

liquidity and risk, and that liquidity is not only determined by monetary factors, is the

liquidity conundrum.

13. Demonstrate how cognitive biases can lead to errors in judgment byfinancial market participants.

Investors can suffer from several cognitive biases: confirmation bias, overconfidence in

models, group think, short-term thinking bias, and hindsight and recent memory bias.

● Confirmation bias: the investor seeks evidence that his theories are correct, ignores

evidence that contradicts his/her beliefs, and continues patterns of behavior that may

be detrimental.

● Overconfidence in models: investors rely too heavily on models to measure risk and

predict the future, and they confuse measurable risk with uncertainty, which cannot be measured.

● Group think: investors look to what others are doing to form opinions and ignore the

evidence.

● Short-term thinking or short-termism: related to group think, the investor gets caught

up in the current market mood and ignores the long-term view.

● Hindsight and recent memory bias: forecasting the future based upon the recent past.

All of these biases can lead to errors in judgment. In cognitive bias, the investor will

continue to engage in the same activity despite it repeatedly failing because the investor

thinks that sooner or later it has to work. One example would be buying more and morestock of a failing company. In overconfidence in models, the investor will make a

prediction based upon a model. The investor may make an over-allocation to the

investment thinking that all the risks are known. Group think can lead to errors as an

investor “joins the herd” in either buying or selling stocks. As all investors buy, for

example, the prices continue to rise. This increases the cognitive bias and investors buy

more. Short-term thinking and hindsight bias also come into play here as investors only

see the recent past increases in value and only look ahead to the short-term possible gains.

14. Describe factors complicating the establishment and maintenance of target allocations to illiquid asset classes.

Non-traded assets, such as private equity and real estate, have traditionally exhibited

attractive performance. However, establishing and preserving a target allocation of illiquid

asset classes is a complex task. More specifically, Meredith, et al. describe the following four

reasons why it is difficult to establish and maintain target allocations to illiquid assets:

1. Illiquidity: An asset is considered illiquid when it can not be sold, either quickly, with

insignificant loss of value, or at anytime. Secondary markets for illiquid investments





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are limited. As a result, investors cannot easily rebalance their asset allocation when

they deviate from their target allocation.

2. Uncertainty concerning the timing and size of capital calls: When the general partner of a

private equity fund identifies an appropriate investment opportunity, he/she can “call” the

required equity capital, at which time each partner will typically support a pro rata share of

its commitment (i.e. the pledge made by an investor to a private equity fund).Unfortunately, it is impossible to know exactly what investment opportunities will arise

when investing in a private equity or private real estate fund. As a result, there exists

considerable uncertainty regarding the timing and size of capital calls made by the fund’s

general partner. This point highlights the importance of having a commitment strategy

because an investor may eventually invest less than was expected or committed if a

private equity fund cannot find appropriate investment prospects.

3. Uncertainty about the timing and size of distributions: Since the timing of investment

realizations cannot be predicted with complete certainty, the timing and size of fund

distributions (i.e. the cash payments investors receive as compensation for investing

in private equity) are also uncertain.4. Valuation subjectivity: It is very difficult to value a private equity or a private real estate

fund at any point in time. This is because: a) these investments trade infrequently, b)

accounting rules tend to push general partners to account these assets at book value, and c)

there is always uncertainty regarding the precision of asset valuations.

15. Explain the role of Monte-Carlo simulation to achieve stable (steady-state) allocation in this study.

Meredith, et al. create a model portfolio formed by the following assets and allocations:

private equity (0%), public equity (65%), and fixed income (35%), which they seek to

migrate to a “steady state” (or stable) allocation of private equity (25%), public equity(50%), and fixed income (25%) over time. They then make assumptions regarding the

performance of their private equity, public equity, and fixed-income investments. They

use Monte-Carlo analysis to simulate the performance of this portfolio for thousands of

scenarios entailing various levels of asset class returns and cash flow patterns. Monte-

Carlo analysis consists of a class of computational algorithms that relies on repeated

random sampling to calculate its results.

Meredith, et al. found that they could not reach their 25% private equity allocation target

until the 5th year, and that it took 25 years to achieve a steady-state 25% allocation. They

acknowledged that it may be possible to arrive at the steady-state faster if the magnitude

of the transition had been lower (instead of the 0% to 25% shift that they used) or if the

asset would have produced cash flows, such as in the case of private real estate.

16. Illustrate the total impact of several individual risk factors on privateequity allocation drift.

Instructor note: Allocation “drift” occurs when one asset class becomes overweighted while

another becomes underweighted in the portfolio. In the short run, allocation drift may not be

an important problem. However, in the long run, allocation drift can change the portfolio’s

risk level to a point where it might become misaligned with the investor’s objectives.





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Meredith, et al. analyzed the extent to which five specific individual risk factors, when

considered in isolation, influence private equity allocation drift (or volatility of the

private equity exposure). The individual factors are:

1. volatility in the performance on public equity and fixed income,

2. volatility in the performance of the private equity investments,3. uncertainty regarding capital calls,

4. uncertainty in distributions, and

5. uncertainty regarding private equity valuations.

The authors perform this experiment once their model portfolio has achieved a steady-state

(or stable) allocation of 25% to private equity. Results show that, on a stand-alone basis:

1. Volatility in the performance on public equity and fixed income (the other two asset

classes in the portfolio) generates a +/- 2.8% volatility in the portfolio’s private equity

exposure,

2. Volatility in the performance of the private equity investments leads to a

+/- 4.6% volatility in the allocation to private equity,

3. Uncertainty regarding capital calls generates a +/- 1.8% volatility in the private equity

allocation,

4. Uncertainty in distributions leads to a +/- 2.7% volatility in the private equity

allocation, and

5. Uncertainty regarding private equity valuations leads to a +/- 2.7% volatility in the

portfolio’s private equity exposure.

It would appear that the combined effect of these individual risk factors would have a

significant impact on the volatility of the allocation. Simply summing the effects wouldgive a possible range of +/-14.6%. However, this level of risk is not realistic because

these sources of uncertainty are not perfectly correlated; therefore some of the potential

risk is diversified away. In other words, the possible total effect of uncertain investment

returns (stock and bond returns), cash flows (capital calls and distributions), and valuation

on the volatility of the private equity allocation is lowered from low correlations of the

risk factors and some of the potential total risk being diversified away.

This result is evident from the findings of Meredith, et al. That study estimates the total

potential volatility from the five individual sources of risk was equal to about 6%, which is

less than half of the total of the volatilities at 14.6%. They argue that this percentage is not an

unreasonable magnitude since institutional investors tend to allow their target asset class

allocations to drift within a range of about 5% (to minimize transaction and administrative

costs) before they begin to rebalance their portfolios.

References

Bhansali, V. “Tail Risk Management.” The Journal of Portfolio Management . Summer 2008.

Sullivan, R. “Taming Global Village Risk.” The Journal of Portfolio Management . Summer 2008.

Meredith, R., N. De Brito, and R. De Figueiredo. “Portfolio Management with Illiquid

Investments.” Citi Alternative Investments. June 2006.




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TOP I C 11

Research Issues in Alternative Investments

M a i n P o i n t s Evaluating the risk and return components of commodity futures returns from

various perspectives

Comparing approaches to evaluate smoothing effects in the real estate market

Assessing research on hedge fund risk factors and biases in hedge funddatabases

Describing the impact of unique characteristics of private equity returns oninvestor decision-making

1. Illustrate how an investment in commodity futures can earn a positivereturn when spot commodity prices are falling.

This learning objective covers an interesting and important point on how expected spot price changes are embedded into commodity futures prices and what the implications are

for the changes in futures prices.

Long positions in commodity futures earn higher than expected returns when commodity prices are higher than expected . The key is that profits and losses are driven by changes in

commodity prices relative to expectations.

The following diagram illustrates the concept. Assume that the current spot price of oil is$70 but that over the next year it is expected to decline to $60. The commodity futurescontract for one year is priced at $55. A holder of a long position in the contract expectsto earn $5 per contract as the current futures price ($55) rises to the expected spot price atthe end of the year ($60). However, the spot price of the commodity is expected to fall by

$10 over this year. Note that this example exactly addresses the learning objective's point: how a long position in a futures contract can earn a positive return when acommodity price is falling. The key is that the commodity price is expected to fall.



Topic 11: Research Issues in Alternative Investments


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Futures contract holders also can earn a risk premium. Thus, even in an example where

commodity prices are not expected, on average, to rise or fall, the holder of a long position might still earn a positive return while commodity prices fall if the anticipated

risk premium exceeds the losses caused by a small unexpected decline in the spot price.In the above example, the long position expects to receive a $5 profit—which could be anormal profit from bearing risk (a risk premium for bearing risk or it could include anexpected abnormal profit from superior forecasting abilities). The expected profit (loss) toa futures contract is the risk premium earned (paid) for bearing (hedging) risk.

Unexpected, rather than expected commodity price changes, drive returns. When spot prices fall they do not cause futures prices to fall unless the spot prices fall more than

expected.

2. Compare commodity spot returns and commodity futures returns.

The returns from physically holding commodities can be substantially different from thereturns of long positions in (collateralized) commodity futures contracts.

One explanation is that positions in collateralized commodity futures earn interest (e.g.,the T-Bill yield) on the collateral. For example, having $1,000,000 in T-Bills and long

positions in $1,000,000 of gold futures should be expected to outperform having $1 of gold. Actual physical possession of commodities can have storage and convenience yieldcosts.

Also, spot commodity price changes can differ from commodity contract price changes because commodity futures prices are based on expected commodity prices.

According to Gorton and Rouwenhorst, inflation adjusted over the period 1959 to 2004,

collateralized commodity futures vastly outperformed commodity spot prices (perhapshaving double the total inflation adjusted return).

Many spot commodity prices exhibit seasonal price fluctuations (for example, heating oil

prices tend to be higher during the winter). As a result, temporary price movements can be very prominent in commodity spot prices. In spite of this, seasonality in spot pricesshould not have an influence on futures prices because seasonality is a predictableoscillation that market participants take into account when they establish futures prices.





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3. Compare commodity futures returns with stock returns and bond returns.

In a nutshell, collateralized commodity futures contracts (long positions in commodityfutures combined with interest bearing collateral deposits) have performed well,especially adjusted for risk.

Gorton and Rouwenhorst compare collateralized commodity futures returns with stock returns (measured using the returns on the S&P 500) and corporate bond returns(measured using the returns on a corporate bond index) return for the period July 1959through 2004. All the series were deflated by the U.S. CPI (Consumer Price Index) and,thus, provide a measure of real return (i.e. inflation-adjusted) performance. According toGorton and Rouwenhorst's analysis of inflation adjusted returns from 1959-2004:

● Average annual returns of collateralized commodities and US common stocks have

been roughly equal (with both earning a risk premium of about 5%)

● Both commodities and stocks had higher average returns than bonds (bonds earned a

risk premium about half as much as stocks and commodities)

● Commodities outperformed stocks in the 1970's and for a few years after 1999, butstocks performed better in the 1990's (and perhaps a little better in the 1980's).

4. Compare commodity futures risk with equity risk.

In a nutshell, collateralized commodity futures contracts (long positions in commodityfutures combined with interest bearing collateral deposits) have generated high averagereturns (roughly in line with US common stocks) and have done so with favorable risk

attributes and correlations.

According to Gorton and Rouwenhorst’s analysis of inflation adjusted returns from 1959-2004:

● Standard deviations were higher for stocks (4.27% per month) than for collateralizedcommodity futures (3.47%).

● Stock distributions were negatively skewed (which is bad), while collateralizedcommodity futures returns were positively skewed. Therefore, stocks have relativelymore weight in the “left tail” of the return distribution and commodity futures haverelatively more weight in the “right tail.”

● Collateralized commodity futures and stocks both had positive kurtosis (which meansthey are fat-tailed relative to the normal distribution), but the kurtosis for futures wasmore than double that of stocks.

●

Equities had substantially higher down side risk as would be measured by Value atRisk. The worst 5% of months for equities lost more than 6.34% while the worst 5%of months for commodities lost more than 4.10%.

● Commodity futures returns were negatively correlated with equities and bonds over long-term time horizons (but not over shorter intervals). This finding implies thatcommodity futures may provide diversification benefits when added to a portfolio of

stocks and bonds.





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● The negative correlation of commodity futures returns with stocks and bonds tends toincrease as the horizon lengthens, thus implying that the diversification benefits of

adding commodities to a traditional portfolio are larger at longer horizons.

● During the worst 5% of monthly equity market returns, stocks lost an average of 9% per month while collateralized commodity futures actually earned positive monthly

returns averaging about 1%.

5. Discuss the use of commodity futures as a hedge against inflation.

In a nutshell, collateralized commodity futures contracts have provided superior inflation

hedges compared to stocks and, in particular, bonds. Inflation hedging is the idea that aninvestment at least keeps up with, and better yet outpaces, bouts of unexpected inflation.

Gorton and Rouwenhorst demonstrate that unlike stocks and bonds, collateralizedcommodity returns have had positive correlations with inflation, especially over longer term time horizons such as one or five years. The positive correlation means thatcollateralized commodity returns tend to be highest when inflation is highest.

Gorton and Rouwenhorst then formulate a measure of unexpected inflation (which theydefine as the actual inflation rate minus the nominal interest rate) and show that evencollateralized commodity quarterly returns have had positive correlations with

unexpected inflation while stocks and bonds have had negative correlations.

Commodities can be expected to be positively correlated with inflation through the directlink that commodity prices are part of inflation. However, it is also noted that shocks thatincrease inflation (e.g., oil price spikes) often tend to cause negative shocks in generaleconomic output. Therefore, the empirical results (1959-2004) of excellent inflationhedging with collateralized commodities and terrible inflation hedging with stocks and

bonds are not surprising.

6. Explain the diversification benefits of commodity futures.

While diversification benefits are typically discussed in the context of return correlations,this particular learning objective is focused on the relationship between returns and business cycles.

Gorton and Rouwenhorst analyze the performance of stocks, bonds and collateralized

commodity futures relative to the last seven US business cycles. The major conclusionswere:

1. In the early part of a recession, stocks on average lost almost 20% and collateralizedcommodity futures gained almost 20%!

2. In the late part of a recession, stocks on average gained almost 20% andcollateralized commodity futures lost a few percent.

They also found that bonds, somewhat like stocks, did very well near the end of arecession and somewhat poorly at the start.

The important conclusion being that this is further indication of the potential of collateralized commodity futures to provide diversification during recessions.







86

Example: The six-month gold futures contract has a price equal to $899. The current spot price for gold is $922, and the expected spot price in six months is $911. Does this

market exhibit normal backwardation? Is the market backwardated or in contango?

Answer: Since the futures price is below both the spot and expected spot price, normal

backwardation exists, and the market is in backwardation.

9. Describe a trading strategy that uses basis in futures markets as anindication of risk premium in futures markets.

Basis refers to the difference between the futures price and the spot price.

● A market in backwardation has a negative basis, i.e., futures price – spot price < 0.

● A market in contango has a positive basis, i.e., futures price – spot price > 0.

The basis will change from either a change in expectations about the future spot price or

variation in the expected risk premium. If markets are efficient, then trading strategies based upon changes in expectations will not earn excess returns. However, if changes in

basis are a function of the differences in required risk premiums across commodities or the changing risk of a given commodity over time, then a trading strategy that selectscommodities according to the size of their basis can be expected to earn positive profits.

There is evidence that the futures basis includes important information about the risk premium of individual commodities. The following steps outline a trading strategy thatuses the basis in futures markets as in indication of risk premium in futures markets:

1) Calculate the basis of a futures position as the slope of the futures curve between the

contract in our index and the next available expiration;

2) Rank the available commodity futures by their basis;

3) Compose two equally weighted portfolios: a high basis and a low basis group;4) Take a long position in the low-basis portfolio and a short position in the high-basis

portfolio.

At the end of each period, recalculate the basis of each contract and adjust the portfolio

accordingly. The intuition behind the strategy is that high-basis futures are overpricedand low-basis futures are underpriced. The strategy essentially goes long underpriced,low-basis contracts and goes short over-priced, high-basis contracts.

10. Describe the factors that cause smoothing and how smoothing impactsasset allocation decisions.

Marcato and Key define smoothing as a phenomenon that produces a lag effect andreduced volatility in valuation-based indices when compared to the underlying market,which is measured by more precise transaction-based indices. An important consequenceof smoothing is that it causes risk to be underestimated.

The following three main factors may cause smoothing:

1. The aggregation process behind the index construction.





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2. Valuations spread over time. This is also known as temporal aggregation and wouldmost likely be present when several spot valuations – taking place over a period of

time – are used to construct a real estate index.

3. Inertia in individual valuations can arise from “anchoring” to past values whenconclusive current market evidence is lacking. An example of this is the use of

thresholds by values (e.g. 1% of capital value) prior to the reporting of a change invalue.

Smoothing has an impact on asset allocation decisions because the estimation of risk-return profiles of various assets is critical to the design of efficient portfolios. For example, the mean-variance framework of Markowitz would assign an optimally highweight to the real estate asset class because valuation-based real estate indices exhibit low

risk levels. Contrary to this, portfolios of institutional investors typically have a realestate weight of only between 5% and 10%. The difference between the two is oftenattributed to the underestimation of risk in available real estate indices.

11. Compare the results of Stevenson (2004) with previous studies on theimpact of smoothing models on allocations to real estate.

Stevenson (2004) analyzes the effects of including real estate to an international portfoliocomposed of various assets. He finds an improvement in performance when real estate isadded to this portfolio. Stevenson also finds, contrary to previous studies, that the use of different unsmoothing models does not suggest different allocation weights.

Marcato and Key examine this issue by applying different unsmoothing techniques toidentify the reasons why Stevenson and previous studies reached different results. In fact,Marcato and Key reinforce Stevenson’s findings and highlight that calibration of the

unsmoothing parameter, rather than model selection, is the most important aspect whenunsmoothing real estate data.

12. Compare four approaches to generating an unsmoothed total realestate return series.

Using a series of historical market rents and cap rates, Marcato and Key create an incomereturn assumed to be equal to the cap rate. They then estimate the capital growth rate attime t (cg t ) of investing in real estate as:

where valuet is the value of a property at time t and is calculated as:

Marcato and Key then use four different approaches to generate an unsmoothed total realestate return series and test whether optimal real estate weights are caused by

unsmoothing model selection or by the choice of parameter levels (calibration).

cgt =

valuet

valuet −1

−1

valuet =

rent t

capratet





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The first unsmoothing procedure is the First Order Autoregressive Reverse Filter

(FOARF). Unsmoothed capital rates of growth for real estate investment (ucg t ) are

estimated as:

where cg t is the capital growth of the valuation-based index at time t , and α1 is the

unsmoothing parameter.

Three main assumptions are underlying this model. First, adjusted and unadjusted valuesof the mean for the series are equal. Second, the model holds over time (stationarity

assumption). Third, random errors are left out of the index (the assumption that there isno noise).

The second unsmoothing method is the Second Order Autoregressive Reverse Filter

(AR2), that is shown in this equation:

As can be seen in the AR2 equation, this autoregressive process has more than one lagand thus gives a more generalized model. However, Marcato and Key argue that there isno ex-ante motivation to assume the existence of an autoregressive process of an order higher than two when using annual returns. Therefore, they restrict their analysis to anAR2.

The third approach that Marcato and Key use applies a procedure suggested by Fisher,

Geltner and Webb (1994) with a First Order Autoregressive specification. Following this procedure, they obtain a Full Information Value Index (FIVI) (also known as FIVIunsmoothing method).

Residuals are computed from (cg t-α1*cg t-1), and their volatility is used to compute theweight (w0):

The weight (w0) is needed to find the unsmoothed rate of capital appreciation from thenext equation:

The fourth method – known as STATES – assumes that different phases of the marketcycle will tend to produce changes to the unsmoothing parameter. For instance, theunsmoothing parameter will be higher in falling markets versus rising markets because“valuers” will be inclined to resist downward adjustments more than upward adjustments.

ucgt =

cgt − (α 1 * cgt −1 +α 2* cgt −2)

(1−α 1 −α 2)

w0=

2*σ resid

σ equity

ucgt =[cgt −α 1

* cgt −1]

(1−α 1)

ucgt =

(cgt −α 1* cgt −1)

w0





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This method also assumes that the stronger the capital appreciation, the higher theunsmoothing parameter, and the stronger the capital depreciation, the lower the

unsmoothing parameter.

Different unsmoothing parameters are then applied for different market growth states(hence the name STATES for this method). First, the parameter is fixed for returns

ranging between the mean and the mean plus its standard deviation. For returns fallingoutside this range, new parameters are estimated by adding a varying coefficient to thefixed parameter following the next schedule:

● 0.10 for returns lying between the mean plus 1 standard deviation and the mean plus 2standard deviations;

● 0.20 for returns falling above the mean plus 2 standard deviations.

● 0.05 for returns lying between the mean and the mean minus 1 standard deviation;

● 0.15 for returns included between the mean minus 1 standard deviation and minus 2standard deviations; and

● 0.25 for returns falling below the mean minus 2 standard deviations.

The STATES method uses the same equation as in the First Order Autoregressive

Reverse Filter to unsmoothed capital growth rates. In this case however, unsmoothing

parameters vary, which are then employed for different market growth states.

After the computation of unsmoothed capital growth rates (ucg t ) using the four different

models just presented (FOARF, AR2, FIVI, and STATES), the next step consists inobtaining an income return (uir t ) recalibrated for the unsmoothed capital value index(ucgit ) as follows:s

where inct is the income (at time t ) and ucg t-1 represents the unsmoothed capital growthindex (at time t −1).

Finally, the unsmoothed total return for real estate at time t (utr t ) is calculated as the sumof the unsmoothed capital growth and the unsmoothed income return at time t :

This formula reminds us that the unsmoothed total return for real estate has twocomponents. The first is the unsmoothed capital growth (property price appreciation),which is analogous to the capital gains component when investing in stocks. The secondis the unsmoothed income return (rents collected from real estate), which is analogous todividend returns in the case of stocks.

t t t uir ucg utr +=

uir t =inc

t

ucgit −1

ucgt =

[cgt −α 1* cgt −1]

(1−α 1)





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Example:

We illustrate the use of these equations in a numerical example. Suppose you would liketo analyze the time series behavior of the real estate returns for a certain city. After searching for information, you find a real estate time series of capital growth rates thatwas calculated using the formula [(valuet /valuet-1) – 1], where valuet = rent t / capratet .

The corresponding last 16 quarters of real estate capital growth rates were:

Quarter Real Estate

Returns

1 2.05%

2 1.75%

3 1.38%

4 1.52%

5 0.84%

6 -1.43%

7 0.05%

8 0.01%

9 -0.33%

10 -5.33%

11 -0.03%

12 -1.03%

13 -0.44%

14 -2.81%

15 0.77%

16 -0.24%

However, you are concerned that this time series may have been the subject of smoothing

as it was calculated from an appraisal-based index.To get a clearer picture from the data, you decide to unsmooth the time series using the

First Order Autoregressive Reverse Filter (FOARF). What would the unsmoothed real

estate return be for the third quarter using the FOARF? (Note: The value of theunsmoothing parameter α1 was estimated to be equal to 0.5).

Now, suppose that you suspect that the autoregressive process might actually have twolags. What would the unsmoothed real estate return be for the third quarter using the

Second Order Autoregressive Reverse Filter (AR2)? (Note: The unsmoothing parameters were estimated to have the following values: α1 = 0.4, and α2 = 0.3).

ucgt =cg

t − (α 1 * cgt −1 +α 2* cgt −2)

(1−α 1 −α 2)

=1.38 − (0.4 *1.75+ 0.3* 2.05)

(1− 0.4 − 0.3)= 0.22%

ucgt =

cgt −α 1

* cgt −1

(1−α 1)

=1.38 − 0.5*1.75

(1− 0.5)= 1.01%





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In this example, what would need to happen for the unsmoothing method known as FIVI to yield the same results as the FOARF? (Note: Assume that the value of the

unsmoothing parameter α1 was estimated to be equal to 0.5).

We know the equations for FOARF and FIVI, and how to compute for weight ( w0). Then,we can compute the following:

From this equation, we can see that the term (cgt-α1*cgt-1) will simplify since it is in

both numerators. Therefore, we have:

Now, since we know that in the case of FIVI w0 is equal to:

We will have, substituting this formula in the previous equation, that:

Now, since a1 = 0.5, then, substituting a1 above by 0.5, we obtain:

We find that:

01)1( w=−α

resid equity σ σ 4=

(cgt −α 1* cgt −1)

(1−α 1)

=(cgt −α 1

* cgt −1)

w0

w0 =

2*σ resid

σ equity

= 0.5

w0 =2 *σ resid

σ equity

(1−α 1) =2*σ resid

σ equity





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That is, the standard deviation of the residuals computed from (cg t-α1*cg t-1) will need to be four times larger than the standard deviation of equity returns for FIVI to yield the

same results as FOARF.

13. Describe the impact of varying smoothing parameters for UK real

estate return data on the optimal allocations to real estate.Marcato and Key use data for the U.K. for the period 1921-2005 and compare thesuggested allocations to real estate arising from the four methods presented in LearningObjective 10. They also calculate the four models for the following three sub-periods:1921-2005, 1921-1970, and 1971-2005.

The First Order Autoregressive Reverse Filter (FOARF) suggests a very high weightfor real estate in the mixed asset portfolio. Allocation choices are also found to be verysensitive to the value of the unsmoothing coefficient. For instance, results suggest thatreal estate should have a weight of 60% in the original data. As the unsmoothing parameter increases in value, the real estate weight decreases, disappearing when the

parameter is greater than 0.60. When a coefficient ranging between 0.50 and 0.60 is used,as some authors have previously done, the calculated weights tend to be similar to thosecurrently held by institutional funds in the U.K. (between 5% and 10%).

The Second Order Autoregressive Reverse Filter (AR2) also points to a decreasing realestate weight in the mixed asset portfolio as the unsmoothing parameter increases.However, the minimum coefficient that will produce a zero real estate weight is lower

(0.40) than the one generated by the FOARF. This is due to the inclusion of a second-order parameter.

The third unsmoothing method, the Full Information Value Index (FIVI), yields

portfolio compositions that are comparable to the first method. However, this modelshows even more sensitivity to the value of the unsmoothing coefficient. For example,real estate weights fall rapidly (from 40% to 1%) when the unsmoothing parameter isincreased slightly, from 0.40 to just 0.45.

Finally, the fourth method, the STATES model, yields a relationship between weightsand the unsmoothing coefficient that is comparable to the one suggested by FOARF.

However, in this fourth method, smaller unsmoothing parameters are necessary to suggestthe same asset allocations.

Summarizing, the results for the U.K. point to larger differences between minimum and

maximum values of the unsmoothing parameter than between unsmoothing methods.This finding supports the hypothesis that calibration in smoothing techniques (i.e., choiceof the parameter) is much more important than model selection.

14. In the Marcato and Key (2007) study, compare and contrast the resultsof using UK data with those employing US and Australia real estatereturn data.

Using a FOARF unsmoothing model for the U.S. and Australia, Marcato and Keyreinforce the findings arising from the analysis of the U.K. market for the period 1971-





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2005, with real estate weights diminishing as the value of the unsmoothing parameter increases. In spite of this, the authors point out three differences:

1. For the U.S. and Australia, the unsmoothing parameter has a significant (and muchlarger than for the U.K) impact on the overall portfolio weighting. This is becausethere exists a much higher first order serial correlation coefficient in the U.S. and

Australia compared to the case of the U.K. This result also suggests the presence of more serious inefficiencies in the valuation-based indexes used in the U.S. andAustralia when compared to those used in the U.K.

2. The maximum suggested portfolio weights of equities in both the U.S. and Australianever reach the 80% level of the U.K. when the unsmoothing coefficient is 1. This iscaused by the relative risk-return profile of bonds, which is appreciably more

attractive in the U.S. and Australia than in the U.K.

3. There exists a substitution effect between real estate and cash in both the U.S. andAustralia that is due to a correlation coefficient that is near 0.50.

15. Argue the best method of adjusting a real estate return series whenconducting an asset allocation study.

Marcato and Key calculate and compare the Sharpe ratio obtained for four portfolios

(using different unsmoothing methods) and three benchmarks. Results suggest that animplicit unsmoothing parameter exists and that its value ranges between 0.40 and 0.60.This range is similar to the values found for this parameter in previous research. For practical use, Marcato and Key suggest using the simplest form of unsmoothing method,the First Order Autoregressive Reverse Filter (FOARF), with a coefficient whosevalue is included in the mentioned range.

The authors conduct a series of portfolio simulations and arrive at the very important

conclusion that all unsmoothing methods are highly sensitive to the choice of the parameter (calibration). On the other hand, unsmoothing model specification has littleimpact on asset allocation. This finding is consistent and supports Stevenson’s conclusionthat calibration, rather than model specification, is the most important concern whenunsmoothing real estate data. As a result, Marcato and Key argue that previous research

that proclaimed the significance of model specification may be biased due to the selectionof different (and not necessarily comparable) parameters for alternative models.

16. Describe the hedge fund business model presented by the authors.

Fung and Hsieh propose a hedge fund business model that is based on the followingeconomic rationale. Assume that a money manager has a limited amount of personalwealth and believes that he/she could earn risk-adjusted returns that would be aboveaverage. To start a trading operation, the manager ought to leverage his/her skills bydrawing external capital so that he/she can meet the resulting fixed costs. Fung and Hsieh

compared this business model to the financing of a new venture. This external capitalcould be either equity financing or debt financing. However, typically, the manager’s personal wealth would not be enough to attract significant debt financing. As a result, thecreation of a hedge fund arises as the only realistic financing alternative.





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Hedge fund managers attempt to maximize the enterprise value of their funds subject to anumber of constraints, such as diminishing return to scale or the possibility that, for

reasons external to the fund manager, the investment strategies they follow may becomemore or less popular among investors.

The compensation contract design between the hedge fund manager and investors

(e.g. the fee structure) will, in turn, determine the characteristics of the businessmodel (e.g. degree of leverage, the allocation of capital to factor-related bets, etc.). Inthe end, optimal contracting between hedge fund managers and investors must align

the interests of the two and must consider the potential effects arising from thosesystematic risk factors that are intrinsic to each hedge fund strategy. Some authorshave argued that the co-investing by the hedge fund manager and the investors mayimprove the alignment of interests on the downside but can also produce excessiveconservatism by the fund’s manager on the upside.

17. Analyze the issues in measuring the growth of the hedge fund industry.

Fung and Hsieh offer an overview of the following five “issues” related to recentdevelopments in the growth of the hedge fund industry:

1. Increasing institutional investments in hedge funds. Demand for hedge funds byinstitutional investors in the U.S. has been increasing in recent years. While the leadwas initially taken by university endowments, pension plans are also growing their hedge funds investments.

2. Growth in the supply of funds and in assets under management. Although thenumber of hedge funds has increased in recent years, in spite of the high attritionrate inherent to the industry, the actual size of the hedge fund industry is very

difficult to measure. This is because, unlike mutual funds, hedge funds provideinformation to one or more databases on a voluntary basis. The three most importanthedge fund databases are: (i) Center for International Securities and DerivativesMarkets (CISDM), (ii) Hedge Fund Research (HFR), and (iii) Lipper TASS (TASS).

3. Changes in styles and strategies. Hedge funds constitute a heterogeneous group thatemploys many diverse investment strategies. Databases usually classify hedge funds

according to self-described styles. For example, TASS classifies hedge funds intothe following ten styles:

(i) Convertible arbitrage: Hedge funds attempt to generate alpha by buying

securities while hedging the equity, interest rate, and credit risks withshort positions of the equity of the issuing firm or other fixed-incomederivatives.

(ii) Dedicated shorts: Hedge funds that short sell securities (typically equities)that are estimated to be overpriced.

(iii) Equity market neutral: Hedge funds trade long-short portfolios of stockswhile keeping a neutral exposure to the general stock market.

(iv) Macro funds: Hedge funds that invest money on directional movements instocks, bonds, commodity prices and foreign exchange rates.





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(v) Fixed-income arbitrage: Hedge funds trade long-short portfolios of bonds.

(vi) Long/short equity: hedge funds are invested in a long-short portfolio of stocks

with a long bias.

(vii) Managed futures: Hedge funds specialize in futures trading.

(viii) Event driven: Hedge funds focus on corporate events (typically merger transactions or corporate restructurings).

(ix) Emerging market: Hedge funds invest in securities of developing economies.

(x) Others: All other hedge fund strategies.

4. Management fees and performance fees. Similar to the case of mutual funds, hedgefunds charge a fixed management fee that is calculated as a percent of net assetsunder management. Most hedge funds charge a management fee that ranges between

1% and 2%. However, unlike mutual funds, hedge funds also charge an incentive

fee or a performance fee. Roughly 80% of hedge funds charge a 20% incentive fee.

5. Style evolution and changing investor clientele. A growing recent trend has been for hedge funds to progress from single-strategy specialists into multi-strategy hedge

funds.

Fung and Hsieh comment on the increasing research on synthetic hedge funds. These aredefined as the replication of hedge-fund-like returns, available at a lower cost toinvestors, using mathematical models. However, the authors are not convinced that

synthetic hedge funds represent a reasonable solution to the current imbalance between

supply (alpha producers) and demand (alpha buyers).

18. Evaluate the potential biases in hedge fund databases.

Hedge fund databases – CISDM, TASS, and HFR – all suffer from the following four potential biases:

1. Selection bias. This bias arises because inclusion in a database is voluntary (i.e., it isat the discretion of a hedge fund manager). On the one hand, one might expect thathedge funds having superior performance would go into a database to seize theinterest of investors. On the other, many successful hedge funds that are closed tonew investors decide not to be included in a database as they do not have an

incentive to be there. Therefore, it is very difficult to estimate the magnitude of thisimportant bias. The existence of this bias implies that hedge funds in a database maynot be representative of the industry universe.

2. Survivorship bias. A hedge fund may become defunct when investors aredissatisfied by the fund’s performance and vote to redeem their capital, or when thefund-raising attempts of the hedge fund manager fall short to draw the critical massrequired for the hedge fund to remain a feasible business proposition. Empiricalevidence suggests that surviving funds have had better returns than dead funds. The

survivorship bias can be measured as the average return of surviving (or live) funds

in excess of the average return of all funds, both surviving and defunct. Fung andHsieh argue that, as the hedge fund industry matures, the importance of the





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survivorship bias will diminish. Survivorship bias amounts to roughly 2.5% ayear.

3. Incubation bias (backfill or instant history bias). New hedge funds typicallyundergo an incubation period to accumulate a track record. This incubation period typically lasts at most a few years, as the opportunity costs related to a fund’s

incubation period can be significant. If the track record is decent, the manager usually registers the hedge fund into a database hoping to draw the interest of prospective investors. Since the incubation history of the hedge fund before the entry

date is backfilled, it is logical to speculate that the early part of a hedge fund’shistory will be upwardly biased. Incubation bias has been estimated to be around1.5% per annum. A related concept is the hazard rate, which is the proportion of hedge funds that drop out of a database at a given age. For example, Fung and Hsiehfind that the highest dropout rate tends to happen when a hedge fund is 14 months

old.

4. Liquidation bias. This bias refers to the finding that fund managers discontinue

reporting their returns to a database before the final liquidation value of a hedgefund, thus causing an upward bias in the observed returns of dead funds. Fung and

Hsieh mention that during the Russian debt crisis of August 1998, several hedgefunds (including the famous Long Term Capital Management) lost all their capitaland became defunct. However, managers stopped reporting their hedge fund’sreturns in July of that year. Had they reported the corresponding -100% returns inAugust of 1998, observed hedge fund returns would have been lower during thatmonth.

Finally, past research has also shown that hedge fund indexes have serial correlation of

hedge fund returns (autocorrelation) and that the returns are correlated to past returns of market factors (such as the S&P 500). It is still not clear whether this correlation arises as

a result of infrequent trading of illiquid securities by hedge funds in their portfolios or whether it is a reflection of manipulation by hedge funds managers to smooth or “massage” their returns.

19. Review the approach and describe the main findings of bottom-upresearch on hedge fund risk factors.

The bottom-up approach to hedge fund risk factors, as opposed to the top-down

approach, is an approach in which risk factors inherent in specific styles are identified.

1. Managed futures. The majority of managed futures funds employ a trend-followingstrategy. A market timer who switches between Treasury bills and stocks creates a

return profile that is similar to that of a call option on the stock market. It has also been shown that the resulting return profile is similar to that of lookback straddles.A lookback straddle is a derivative that pays the holder the difference between themaximum and minimum prices of the underlying security over a certain time period.

2. Merger arbitrage. It has been shown that merger arbitrage returns are comparable tothose of merger arbitrage hedge funds. Merger arbitrageurs tend to be long “dealrisk” as they bet on the success of a merger. Fung and Hsieh argue that merger arbitrage returns can be considered an insurance premium arising from selling a





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policy against the risk that the merger is not completed. Mergers bear a significantidiosyncratic risk that is usually mitigated by the hedge fund by holding a portfolio

of merger transactions.

3. Fixed-income hedge funds. Convertible bond funds are strongly correlated to aconvertible bond index and high-yield funds are strongly correlated to a high-yield

bond index. Furthermore, all styles have correlations to changes in default spreads.Past research has shown that the returns arising from fixed-income hedge funds can be “created” using different fixed-income arbitrage trades that are often used by

hedge funds, such as:

● Swap spreads A bet that the fixed side of the spread will remain higher thanthe floating side of the spread.

● Credit spread: A bet on the difference in the prices or interest rates of twofixed-income securities, where the value of the position is determined byfluctuations in the differentials between the prices or interest rates.

●

Mortgage spreads: A bet on prepayment rates.● Yield-curve spreads: A bet that bond prices deviate from the overall yield curve

only in the short-run due to liquidity issues, which disappear over time.

● Capital structure arbitrage (spread): These are credit arbitrage spreads onmispricing among different securities (typically bonds and stocks) issued by thesame company.

● Fixed-income volatility trade: A bet that the implied volatility of interest ratecaps will be higher than the realized (observed) volatility of the Eurodollar futures contract.

4. Long/short equity hedge funds. Long/short equity hedge funds have been found tohave a positive exposure to the stock market and to long small-cap/short large-cap positions. The performance of this type of hedge funds is highly idiosyncratic, as

hedge fund managers in this style are stock pickers possessing diverse opinions andabilities.

5. Convertible arbitrage. Empirical results suggest that convertible arbitrage hedgefunds offer liquidity to the convertible bond market trading mostly from the longside while hedging the underlying risk factors of the bond. Past research suggeststhat the following three strategies are commonly used by convertible arbitrage hedge

funds:

● volatility arbitrage strategy, which is a bet that the option embedded in the

convertible bond is not correctly priced,

● credit arbitrage strategy, which is a bet that the convertible bond’s credit risk isnot correctly priced, and

● carry strategy, which is a combination of the volatility arbitrage and the creditarbitrage strategy.

6. Niche styles. Past research suggests that emerging market hedge funds returns arestrongly correlated with an emerging market stock index; distressed securities hedge





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funds returns are strongly correlated with a high-yield bond index; equity non-hedge(hedge funds that usually trade from the long side, leaving their market risk

essentially unhedged) returns are strongly correlated with a small growth stock index; and dedicated short-sellers’ returns are strongly negatively correlated with asmall growth stock Index. In the case of equity market neutral, the difficulty in

correctly classifying the strategies that fall into this style has made this categorychallenging to analyze.

7. Macro hedge funds. Disentangling the risk of macro funds has been a difficult task

to researchers because of the dynamics of risk in these hedge funds. A seven-factor model proposed by Fung and Hsieh (2004) does a reasonable job in capturing therisk of macro funds (see Learning Objective 21).

20. Describe and assess the adequacy of the asset-based style risk factormodel used by Fung and Hsieh to analyze hedge fund returns.

Fung and Hsieh stress that investors should try to understand the most important risk

factors in hedge fund portfolios so that they can evaluate their effect on their overall assetallocation profile. Furthermore, counterparties to hedge funds and regulators need torecognize the key sources of hedge fund risk so that they are able to assess capital at risk.

Fung and Hsieh propose an asset-based style (ABS) factor model consisting of sevenrisk factors to capture the risk of diversified portfolios of hedge funds. The term asset- based used to describe these sources of uncertainty arises because these top-down risk

factors are all based on tradable securities and their derivatives. The seven factors are:

1. The excess return of the S&P 500 (i.e. the return of the S&P 500 above the risk-freereturn),

2. Small-cap stocks minus large-cap stock returns,

3. The return of the 10-year Treasury bond above the risk-free return,

4. The return of Baa bonds above the return of the 10-year Treasury bond,

5. A lookback portfolio in bonds,

6. A lookback portfolio in currencies, and

7. A lookback portfolio in commodities.

The importance of the asset-based style (ABS) risk factor model to analyze hedge fundreturns resides in that the identification of these observable risk factors based on tradableassets allows us to indirectly get around the opaqueness of hedge fund operations. Thus,

the ABS risk factor model allows us to indirectly measure the systematic risk of hedgefund investing.

Another important feature of the ABS risk factor model is that it provides a more naturalway of defining hedge fund alphas and hedge fund betas, or “alternative alphas” and“alternative betas”. The ABS risk factor model provides investors in search of alphawith a way to assess the value of their hedge fund investment. Beta buyers, on the other

hand, can evaluate whether their investments have exposure to the right risk. And finally, both types of investors can assess whether the fees they paid are reasonable.





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21. Discuss the broader risks associated with hedge funds and describe theregulatory concerns.

Fung and Hsieh describe the following three primary regulatory concerns associated withhedge funds:

1. Investor protection. In general, regulators—mainly the Securities and ExchangeCommission (SEC) and, to a lesser degree, the Commodity Futures TradingCommission (CFTC)—maintain that hedge fund investors can “fend for themselves”, given that these investment vehicles are accessible only tosophisticated wealthy individuals and institutional investors. These “accreditedinvestors” are assumed to have the knowledge and the sufficient wealth to hold outthe risk of suffering potentially substantial losses from hedge fund investing.

2. Systemic risk . This is the risk that large losses from one or more hedge funds canwreak havoc to their counterparties, thus creating a domino effect to other market participants and institutions. Regulators generally consider that systemic risk should be dealt with by existing regulation of banks and other counterparties rather than by

new laws.

3. Market integrity. Some regulators are concerned about the potential impact thathedge funds may have on the markets, given that a number of these funds are largeenough to exert a major impact on the markets. A case in point was the effects of thenear bankruptcy of Long Term Capital Management (1998). Others, however, argue

that hedge funds are too small to be able to manipulate particular markets. Fung andHsieh argue that the potential impact that hedge funds may have on market integrityhas shifted from the failure of a mega hedge fund (such as LTCM) to that of a

convergence of leveraged opinions among funds that individually may functionunnoticed. A convergence of leveraged opinions can be defined as an event in

which the opinions of a large group of hedge funds converge onto the same set of bets, thus potentially threatening markets and creating systemic risk. Fung and Hsiehrecommend that risk monitoring of the hedge fund industry should reorient its focal point away from megafund collapses to the convergence of factor bets.

22. Describe the role of manager selection in the experience of a privateequity investor.

Research on the returns of private equity firms has shown that risks are often understatedand returns overstated. The key to successful private equity investing is to select the best private equity firms in which to invest, but this is not easy because of the lack of

transparency concerning the valuation and disclosure of the assets in each private equityfirm. This emphasizes the importance of the private equity investor somehow being ableto recognize and have access to the managers with the best record in order to earn the bestreturns.

David Swensen, chief investment officer of Yale’s endowment, indicated that passiveinvestment in private equity is bound to provide disappointing results. The superior performance is only the result of selecting top-quality managers who pursue value-added

strategies with appropriate deal structures.





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Simply recognizing the top managers is not enough, however, because the top managersmay not be open to new investments. An investor must be able to invest with the top

managers, e.g., by already having a relationship with them. The conclusion is thatsuccessful investors in private equity must have both the ability to identify, as well ashave access to, superior private equity firms and their funds.

Typically, the manager of a private equity firm is the general partner. He/she obtainscapital commitments from limited partners that are qualified investors such as financialinstitutions, university endowments, pension funds, and wealthy individuals. The general

partner looks for good investments, and when finding one, “calls” the limited partners for investment capital. The general partner generally receives a fee of about 2% of assets and20% of the gross profits on invested capital.

23. Discuss the challenges that an investor would face in measuring the risk-adjusted performance of private equity.

The three basic challenges when measuring the risk-adjusted performance of private

equity are:1. Making adjustments for stale prices and illiquidity.

2. Recognizing that earning the returns indicated by summary market measures, e.g., a

private equity index, requires identifying and having access to the managers thatearn the higher returns.

3. The limitations of data availability.

Stale prices and illiquidity complicate measuring the returns to private equity funds.Stale prices refer to when a firm does not change reported prices frequently. Measures of performance using such data are unreliable. Some private equity firms have been knownto not update the values of some of their investments for years. The fact that the firms areilliquid means that the potential returns are not accurately represented by the returnsestimated using reported prices. There are two layers to this illiquidity: the shares of the

private equity firms do not actively trade and the investments of the firms are illiquid.These two layers compound the problem.

The second challenge influencing returns is to be able to identify and have access to top- performing managers. Some managers outperform others on a consistent basis.Furthermore, there is asymmetry of information in this market, and investors in privateequity firms have different abilities with respect to both identifying the best managers and

having access to them. For this reason, the “average” investor in private equity firmscannot expect to earn the returns reported for the industry.

Thirdly, when analyzing the data of private equity, it is important to recognize the historyof private equity and the limitations of the data when compared to more conventional

publicly-traded investments such as stocks and bonds. Private equity has growndramatically in the U.S. in the last 30 years, and has recently spread around the world.The relatively short history, combined with the irregularities associated with the growthof this sector, presents challenges when analyzing the data. Investors should be lessconfident with respect to measures of risk and return. There is also “parameter





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uncertainty” with respect to the models. The challenges are greater for the data of firmsoutside the US.

24. Explain the implication of the observation that mean and medianreturns on private equity databases are significantly different.

A study by Kaplan and Schoar (2005) for the period 1980-2001 found that the meanreturn of venture funds and buyout funds were 17% and 18% respectively. This is far greater than the reported median returns of 12% across all funds. The median is the value below and above which 50% of the values fall. The differences between the mean andmedian returns indicate that the best private equity firms do outperform the market on afairly consistent basis.

One implication is that the ability to recognize top managers and have access to their funds plays a significant role in the realized returns. There are certain types of institutional investors, notably endowments, that have a documented record of higher returns with private equity relative to the general returns of private equity, and they

probably do this by focusing their investment in the best-performing private equity firms.A very important overall implication of these observations is that, when looking at theindustry’s aggregate reported returns, the attractiveness of private equity as a generalasset class of investments is overstated.

25. Explain and identify the potential bias in using the performance of liquidated funds to represent the overall performance of private equityfunds.

Phalippou and Zollo (2005) put forward the hypothesis that the returns of liquidatedfunds may not be a representative sample of the returns offered by all funds. Liquidatedfunds may represent a biased sample because the more successful funds are more likely toliquidate. Funds that are not performing well may not be as likely to liquidate to avoidhaving to recognize poor performance from unsuccessful results concerning their Initial

Public Offerings (IPOs) and asset sales. By keeping the assets on the books, i.e., notliquidating them, they can keep the assets at unrealistically high values.

Phalippou and Zollo examined 981 funds that had officially liquidated or were inactive inthe two years prior to when they took their sample. They compared the performance of these funds to 1,391 funds that were still active. The returns of the liquidated funds had ahigher proportion of good outcomes, e.g., successful IPOs, and a lower proportion of bad

outcomes, e.g., bankruptcies. When adjusting the sample for the bias, the authors foundthat investments in private equity from 1980 to 1996 had an annual return lower than the

S&P 500 by as much as 3.3%.

26. Compare the performance of companies in which private equity firmsinvest with small cap firms listed on NASDAQ.

Both the assets in which private equity firms invest and the smallest stocks on the NASDAQ have payoffs that are similar to those of options. Both have the potential for huge payoffs along with a high probability of a complete loss of capital.





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In statistical terms, both the investments made by private equity firms and small NASDAQ stocks have large means and volatilities. After adjusting for biases, Cochran

(2005) estimated the mean arithmetic return for early rounds of private equityinvestments to be 59% with a standard deviation of 109%. The returns for later rounds of investments decline as a firm becomes more established over time.

Also, given the similarities, small stock indices (e.g. NASDAQ) are sometimes used by practitioners to help gauge success in private equity.

27. Explain the liquidity characteristics of Listed Private Equity securities.

Listed Private Equity (LPE) securities are traded securities that have the properties of private equity but also trade on an exchange. Zimmerman et al. (2005) classified listed private equity securities (LPEs) into one of three categories:

(1) public companies whose core business is private equity;

(2) quoted investment funds that co-invest with specific private equity funds,

(3) specially structured vehicles that invest directly in private companies and/or indirectlythrough various private equity funds.

Zimmerman et al. (2005) examined 287 Listed Private Equity securities (LPEs) duringthe period 1986-2003. The LPEs represented the gamut of financing stages, e.g., early,later expansion, buyouts, and turnarounds. The sample included listings in NorthAmerica, Europe and Asia. Although the firms often sought listing to increase liquidity,the liquidity was still very limited when compared to other public stocks. The bid ask

spread exceeded 20% for over 40% of the LPEs. Liquidity issues tended to influence thecharacteristics of the returns of LPEs. The estimates of returns drop significantly whenadjustments for the bid ask spread are made. Also, adjusting for the biases of thin tradingincreases risk measures, and the beta of the LPEs increase from 0.60 to 0.99.

In summary, the studies suggest that private equity has not produced returns that arecompetitive with the returns of public equity. This is the case for both LPEs and unlisted

private equity, and it is certainly the case given the relative low liquidity and high risks of investments in private equity.

28. Discuss the impacts of adjustment for stale prices on risk, return, anddiversification benefits of private equity (candidates do need tomemorize exact figures).

Adjusting for stale prices increases risk and lowers diversification benefits, but does notaffect returns. It is true that without the adjustments, including a 20% allocation of private

equity to a traditional stock and bond portfolio would shift out the efficient frontier significantly; however, after the adjustments, the benefits are much lower.

Using quarterly data, Conroy and Harris’ 2007 study shows that the risks of privateequity investments increases dramatically after an adjustment for stale prices.Specifically, the standard deviation of the private equity quarterly index (PEQR) almost

doubles; and the standard deviation of the Sand Hill Econometrics index of venturecapital (Sand Hill) increases by more than half again as much. The correlations and betasof the indexes with the S&P 500 also increase.





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The table below summarizes the results found in Conroy and Harris. It gives the actualnumbers for the quarterly results for two private equity indexes and the monthly results

for one index. The adjustment employed was Dimson’s approach, which includes bothcontemporaneous and lagged risk values in the risk measures. The reported Dimson beta,for example, is the sum of regression coefficients on the S&P 500 and its lagged values.

The adjustment attempts to correct for the smoothing that is usually observed with thestale prices associated with illiquid assets.

Index(QuarterlyReturns)

Std. Dev. before/after adjustment

Corr. w/S&P 500 before/after adjustment

Beta before/after adjustment

Mean Return before/after adjustment

PEQR 13% / 25% 0.63 / 0.74 0.53 / 1.17 16.00% / 16.00%

Sand Hill (quarterly) 18% / 31% 0.76 / 0.84 0.90 / 1.71 15.97% / 15.97%

LPX America (monthly) 26% / 32% 0.68 / 0.69 1.15 / 1.41 16.59% / 16.59%

An increase in the standard deviation means that there is an increase in stand-alone risk.

The increases in correlation and beta means there is a lower benefit from diversification.

29. Identify the impact of IPO under-pricing on the performance of the PVCI.

The Post-Venture Capital Index (PVCI) is a measure composed by VentureEconomics. As a representative investment in private equity, the performance of the

PVCI would be biased because of the effect of IPO underpricing. A company becomes part of the index at the offering price when it goes public. If the IPO was underpricedand the price of the stock subsequently increases in value, the return of the PVCI will

increase from that effect. Thus, the initial impact is the tendency for the PVCI to increasewhen it includes an underpriced IPO stock at its issue price, and the stock subsequentlyincreases in value.

This has an impact on investors who attempt to replicate the PVCI. They will findthat they cannot achieve the level of the returns of the index because they cannot participate in the IPOs. Any strategy attempting to replicate the returns from PVCI would usually result in lower returns than those reported by PVCI. By the estimatesof some researchers, the return of a strategy that attempts to replicate the PVCI by buying stocks in the aftermarket will be about 2% lower than the PVCI itself.

Furthermore, adjusting the PVCI to remove the impact of IPO underpricing reducesthe attractiveness of private equity. In fact, the Sharpe ratio falls to such a degree thatsome models suggest that private equity should have a zero allocation in an efficient portfolio.

30. Explain how the following issues pose a challenge to private equityinvestors:

Private equity investors have several issues that complicate their analysis of privateequity. Four such issues are illiquidity, parameter uncertainty, absence of an investibleindex, and cross-sectional differences in private equity managers.





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a. Illiquidity.

Historical returns generally do not have built in any return premium required for

illiquidity. Such a premium would reduce the effective return from private equity andmake it less desirable. Adjusting for liquidity by reducing the expected return of PEQR by 1% per year, for instance, would reduce the recommended allocation to private equity

in an efficient portfolio significantly

b. Parameter uncertainty.

Parameter uncertainty about estimates of risk and return is larger for private equity than

for conventional assets, e.g., stocks and bonds. Estimates made based upon historicaldata may not indicate future performance.

c. Absence of an investible index.

No index is readily available for purchase, and access to some funds may be impossible.Thus, investors in private equity are unlikely to be able to invest in assets with the

properties of the indexes or the properties indicated by the private equity industry’ssummary statistics.

d. Cross-sectional differences in private equity managers.

There is a large cross-sectional difference in private equity managers. One indication isthe dispersion of their internal rates of return (IRR). Recent data indicates the mean IRR is 11.9%, which is much higher than the median of 5.6%. The dispersion is indicated bythe three quartile boundaries: -2.6%, 5.6%, and 15.9%. (These results represent 1,747

funds for the period 1969-2005. The data was from Venture Economics.) A random drawof a private equity fund has an equal chance of being below 5.6% as above 5.6%, whichwould not be desirable. As mentioned earlier, only investors with the ability to recognize

and have access to top managers can expect the higher returns.

References

Gorton, G. and K. G. Rouwenhorst. “Facts and Fantasies about Commodity Futures.” Financial

Analysts Journal . Vol. 62, No. 2, 2006.

Marcato, G., and T. Key. “Smoothing and Implications for Asset Allocation Choices.” The

Journal of Portfolio Management . Special Issue 2007.

Fung, W.K.H., and D.A. Hsieh. “Hedge Funds: An Industry in Its Adolescence.”

Federal Reserve Bank of Atlanta, Economic Review. Fourth Quarter 2006.

Conroy, R. and R. Harris. “How Good are Private Equity Returns?” Journal of Applied Corporate

Finance, Vol. 19, No. 3, Summer 2007.




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GLOSSARY: Level 2, Topics 7–11

Please note that this Glossary is to be used exclusively in preparation for the CAIA®

Exam.

The aim of this Glossary is to provide useful information, in context, that is directly related tothe CAIA®

Study Guide Keywords.

130/30: active extension strategies that invest 130% in long positions in one group of

strategies and 30% in short positions in another group of securities. (Topic 9)

Abatement Strategies: An approach to dealing with climate change that attempts to prevent

climate change. It contrasts with adjustment strategies that are reactions to the unavoidableconsequences of climate change. (Topic 7)

Adjustment Strategies: An approach to dealing with climate change that is a rational reaction

to the unavoidable consequences of climate change. It contrasts with abatement strategies that

attempt to prevent climate change. (Topic 7)

Alignment of interests: The idea that optimal contracting between hedge fund managers and

investors must align the interests of the two and must consider the potential effects arising

from those systematic risk factors that are intrinsic to each hedge fund strategy. (Topic 11)

Allocation drift: The situation in which one asset class in the portfolio becomes overweighted

while another becomes underweighted. Over the long run, allocation drift can change the portfolio’s risk level to a point where it might become misaligned with the investor’s

objectives. (Topic 10)

Alt-A mortgage loans: Loans issued to borrowers who have better credit scores than subprime borrowers but fail to provide sufficient documentation with respect to all sources of

income and/or assets. (Topic 9)

Alternative alphas: Alpha that is derived from the asset-based style (ABS) model of Fungand Hsieh, a risk factor model which provides investors in search of alpha with a way to assess

the value of their hedge fund investment. (Topic 11)

Alternative betas: Beta that is derived from the asset-based style (ABS) model of Fung and

Hsieh, a risk factor model where beta buyers can evaluate whether their investments have

exposure to the right risk. (Topic 11)

Arithmetic return: The simple average of period-to-period returns. (Topic 8)

Aspirational risk: One of the three dimensions of risk, according to Kahneman and Tversky

(1979), that the ideal portfolio must address; aspirational risk is associated with enhancingone’s lifestyle. (Topic 8)

Asset-backed securities (ABS): Bonds that are securitized or collateralized by the cash flows

from an underlying pool of assets—such as credit cards, home loans, auto loans, equipment



Glossary: Level 2, Topics 7–11


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leases, or other non-mortgage related assets. They are often issued by special investment

vehicles with several tranches of senior and subordinate securities, with the tranches being paid in order of seniority (Topic 7)

Asset backed security (ABS) trust: The owner of securitized loans acquired from theoriginators of the loans. They generate NPV from repackaging the cash flows in a way that can

absorb some losses. They typically use a waterfall payment structure for the collateral’s cash

flows. (Topic 9)

Asset-based style (ABS) factors: Top-down risk factors representing tradable securities and

their derivatives. Fung and Hsieh use seven such risk factors to capture the risk of diversified portfolios of hedge funds. (Topic 11)

Backfill: The process in which the incubation history of a hedge fund before the entry date in

an index is typically “backfilled”. (Topic 11)

Backwardation: The condition of the futures curve when near-month futures contracts trade

at a premium to further-out-month futures delivery contracts, i.e., the term-structure of futures prices has a negative slope. In contrast to contango, backwardation means that the price of a

commodity for future delivery is below the spot price. (Topic 9 and 11)

Bankruptcy remote: The attribute, related to the use of SPVs, that a bankruptcy of an

affiliated entity (e.g., a sponsoring bank or money manager) will not affect the functioning of

the structure (e.g., SPV) that is “bankruptcy remote”. (Topic 7)

Barbell strategies: The strategies that allocate a relatively high weight to the personal (low-

risk cushion) and aspirational (high-risk/return) risk buckets and a smaller weight to themarket (middle-risk/return) risk buckets. (Topic 8)

Basis: The cash price minus the futures price (i.e., the spread between the spot price of a

commodity and the price of, usually, a short term futures contract). (Topic 11)

Black-Litterman asset allocation: A model where the investment manager begins with

the equilibrium expected returns computed from the CAPM, which is called the neutral

reference point. Then, the manager combines his/her own expectations about the market withthe expectations from the CAPM. (Topic 8)

Bottom-up approach: An approach to identifying hedge fund risk factors based upon investmentstyles such as managed futures, merger arbitrage, and fixed-income arbitrage. It is in contrast to a

top-down approach, which identifies factors based upon investable portfolios. (Topic 11)

Buy-and-hold: A portfolio strategy in which there is no rebalancing even when market prices

move. (Topic 8)

“Buy to own” investing: Acquiring sizable stakes in companies with the goal of having

control or ownership rather than trading the securities. (Topic 7)





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Calendar spread strategy: A futures trading strategy that exploits the spreads between two

delivery months. It is typically used by sophisticated storage operators who recognize thattheir storage facilities are essentially a set of complex options on calendar spreads. (Topic 9)

Capital calls: When the general partner of a private equity fund recognizes a suitable

investment prospect, he/she is allowed to “call” the necessary equity capital at which timeeach partner typically funds a pro rata share of its commitment. (Topic 10)

Capital-structure arbitrage: This is an arbitrage consisting of credit arbitrage spreadson mispricing among different securities (typically debt and equity) issued by the same

company. (Topic 11)

Carbon funds: Funds that participate in the market for certified emission reductions (CERs),as created by the Kyoto Protocol. They may be government purchasing programs and private

commercial funds. (Topic 7)

Catastrophe bonds: Catastrophe risk-transfer instruments, which provide a cash flow

when a certain unavoidable event occurs. Coupons are usually based on LIBOR plus anappropriate risk premium, and when a predefined loss occurs, the investor forfeits the capital

invested. (Topic 7)

Catastrophe risks: Unavoidable natural catastrophe and weather risks that are used inrisk transfer instruments such as catastrophe bonds and weather derivatives, that provide

compensation for certain events. The market for catastrophe risks offer adjustment

strategies. (Topic 7)

Cat-risk CDOs: Securitized products that bundle various catastrophe risks and sell them in

individual risk tranches. Typically issued by a special purpose vehicle (SPV) that purchases

the underlying pool for a CDO. (Topic 7)

Centralized Clearing House (CCH): clearing house for OTC transactions recommended by the Basel-based Financial Stability Forum to the G-7. CCHs would monitor the risk

exposure of participants, to make sure they have sufficient collateral, and stand ready to back

defaults. (Topic 9)

Claw-back: When fees paid to the general partner by limited partners for profitable investments

may be subject to reclaim if significant losses from later investments occur. (Topic 7)

Clean Development Mechanism: Part of the Kyoto Protocol that allows for investment to be

made in a project that promises to yield future income in the form of certified emission

reductions (CERs) in the emerging markets. (Topic 9)

Climate-related investments: Public investment funds and private equity funds that invest in

assets that could profit from climate change. Examples are investing in the equity of companies that are developing environmentally friendly products and making loans to such

companies. (Topic 7)





108

Collateralized commodity obligation (CCO): The concept of collateralized obligations

(COs) extended into commodities. The CO structure facilitates exposure to commodity pricerisk through the use of CTSs (commodity trigger swaps). (Topic 7)

Collateralized fund obligation (CFOs): The application of the CDO concept to investing in

hedge funds and private equity. (Topic 7)

Commitment strategy: The pledge made by an investor to a private equity fund. If a private

equity fund cannot find appropriate investment prospects, it will not draw on an investor'scommitment. In this case, the investor may eventually invest less than was expected or

committed. (Topic 10)

Commodity trigger swaps (CTS): A derivative that is similar in concept to a credit default

swap and used by CCOs. In this case, the trigger event is a specified decline in commodity prices, e.g., a 35% decline. At the initiation of the CTS, the CCO would give a certain amount

of money (the principal) to the counterparty and receive coupons over a specified time. If,

during the life of the CTS, a trigger event occurs, then the CCO would not receive the

principal back at the maturity of the CTS. (Topic 7)

Compensation contract design: The fee structure design between the hedge fund manager and the investors. Optimal compensation design must align the interests of the two and must

consider the potential effects arising from those systematic risk factors that are intrinsic to

each hedge fund strategy. (Topic 11)

Complex adaptive systems: Term to describe the capital markets that are made up of many

interactive agents whose decisions impact each other in nonlinear and unanticipated ways, andwhose behavior changes when confronted with new situations, thereby allowing the system to

evolve and benefit from the changing environment. (Topic 10)

Concave payoff curves: In the context of the Perold and Sharpe study, refers to the tendency

of a strategy to decrease equity exposure (risk) as the equity market rises. (Topic 8)

Conditional factor models: Either rule-based approaches or econometric approaches that

model the time-varying factor exposures of hedge fund returns. (Topic 7)

Constant mix: A portfolio rebalancing strategy wherein there is periodic rebalancing suchthat the portfolio is adjusted back to being a specified percentage mix of securities or security

classes. (Topic 8)

Constant-proportion portfolio insurance: A portfolio reallocation strategy wherein the

investor sets a floor value at which all risky investing terminates. Furthermore, the investor increases risky asset holdings when the market rises and decreases risky asset holdings whenthe market falls. (Topic 8)

Contango: The condition when near-month delivery futures contracts trade at a discount tofurther-out-month futures delivery contracts, i.e., the futures curve has a positive slope. In

contrast to backwardation, the futures price is greater than the spot price. (Topic 8 and 9)





109

Contingent capital arrangements: Types of put options where option buyer has the right to

raise debt or equity capital or sell assets under specific terms if a given loss occurs. One useof this would be by a firm that would want to make sure it has adequate capital in the event of

a loss. Due to pricing difficulties, they are not used much today. (Topic 7)

Contrarian:trading strategies that increase the demand for losers (by buying losers) and add

to the supply of winners (by selling winners), thus providing market liquidity and helping

stabilize supply-demand imbalances. (Topic 9)

Convergence: The broadening of goals of hedge fund managers and private equity managersthat has led to the two types of funds becoming more similar. Specifically, hedge funds have

moved from just making short-term debt-type investments to some longer-term equity-type

investments with the goal of having some control in companies in which they invest. Private

equity funds are making more shorter-term investments without the goal of control. (Topic 7)

Convergence of leveraged opinions: The event where the opinions of a large group of hedgefunds (which are highly levered investment vehicles that, individually, may function

unnoticed) converge onto the same set of bets, thus potentially destabilizing markets andcreating systemic risk. (Topic 11)

Convex payoff curves: In the context of the Perold and Sharpe study, refers to the tendency

of a strategy to increase equity exposure (risk) as the equity market rises. (Topic 8)

Credit Enhancement: In an ABS trust, the amount of loss on the underlying collateral thatcan be absorbed before the tranche absorbs any loss. (Topic 9)

Credit spread: The difference in the prices or interest rates of two fixed-income securities based upon risk; it is used in fixed income strategies where the investor takes positions based

upon the disparity between the prices or interest rates. (Topic 11)

Decision rule: In the context of the Perold and Sharpe study, refers to the exact determination

procedure for a portfolio reallocation strategy such as the amount of dollars that will beinvested in a risky asset as the prices of the risky assets change. (Topic 8)

Dimson Beta: the sum of regression coefficients on the S&P 500 and its lagged values. It

attempts to correct for the smoothing that is usually observed with the stale prices associated

with illiquid assets. (Topic 11)

Distributions: Cash payments investors receive as compensation for investing in privateequity. (Topic 10)

Emission credits: While the EU Emission Trading System (EU-ETS) has a limit to tradable

emission rights for all companies, emission credits can be won by companies from additional

climate protection projects that are in other countries and that can be credited to their own

reduction target (baseline and credit). (Topic 7)

Emission rights: The EU Emission Trading System (EU-ETS) makes a distinction betweengreenhouse gas emission rights and emission credits. There are a limited number of emission





110

rights for all companies, and these rights can be traded among companies that emit the

greenhouse gases. (Topic 7)

EU Allowances (EUAs): The limited number of greenhouse gas emission rights that are

traded among companies in the EU Emission Trading System (EU-ETS). There are a limitednumber of emission rights for all companies, and these rights can be traded among companies

that emit the greenhouse gases. (Topic 7)

EU Emission Trading System (EU-ETS): The biggest market for greenhouse gas emissions.

It uses targets proposed by the Kyoto Protocol, which defines a number of different emission

certificates. There are a limited number of emission rights for all companies, and these rightscan be traded among companies that emit the greenhouse gases. (Topic 7)

Event loss swaps: A variant of conventional industry loss warrants (ILWs). They are moretradable because they are more highly standardized. (Topic 7)

Event risk: One of the components of personal risk of an individual investor, which refers to

his or her ability to adjust to events such as the loss of a job, health problems, market crashes,etc. (Topic 8)

Exposure: the degree to which an investor should be concerned about possible outcomes. It is

another factor when dealing with risk and uncertainty. (Topic 10)

Exposure diagram: In the context of the article by Period and Sharpe, a graph of the

relationship between desired stock position (amount of risk) on the vertical axis and total

portfolio value on the horizontal axis. Simply put, it tells the investor the risk exposure of the portfolio in relationship to the total portfolio’s cumulative performance. (Topic 8)

Factor-replication approach: attempt to replicate hedge fund returns using hedge fund risk

factors. (Topic 7)

First Order Autoregressive Reverse Filter (FOARF): An approach to generate anunsmoothed total real estate return series, with three assumptions underlying this model:

adjusted and unadjusted values of the mean for the series are equal; the model holds over time;

and random errors are left out of the index. (Topic 11)

Fixed income volatility: A bet that the implied volatility of interest rate caps will be higher

than the realized (observed) volatility of the Eurodollar futures contract. (Topic 11)

Floor: In the context of the Perold and Sharpe study, refers to a total portfolio value that, if

reached via a decline in portfolio value, causes a portfolio reallocation such as the termination

of investment in risky assets. (Topic 8)

Full Information Value Index (FIVI): An approach to generate an unsmoothed total real

estate return series, with a first-order autoregressive specification, without the need to assumethat the underlying property market is informationally efficient. (Topic 11)

Geometric return: return that takes into account compounding across periods, versus

arithmetic returns which is the simple average of period-to-period returns. (Topic 8)





111

Hazard rate: The proportion of hedge funds that drop out of a database at a given age. For

example, Fung and Hsieh find that the highest dropout rate tends to happen when a hedge fundis 14 months old. (Topic 11)

Hybrid asset: An asset that shares common characteristics with two or more other

assets. (Topic 7)

Hybrid funds: Funds that utilize both hedge fund and private equity strategies. (Topic 7)

Illiquidity: The property where an asset cannot be sold, either rapidly, with negligible loss of

value, or at anytime during market hours. Secondary markets for illiquid investments (such as private equity) are limited. (Topic 10)

Incentive fee: A performance fee charged by hedge funds on top of the management fee. Most

hedge funds charge a 20% performance fee. (Topic 11)

Incubation bias: (Also known as backfill or instant history bias) refers to the bias in hedge

funds returns caused when the incubation history of a fund before the entry date in an index is“backfilled”, potentially causing that the early part of a hedge fund’s history will be upwardly

biased. Incubation bias has been estimated to be around 1.5% per annum. (Topic 11)

Incubation period: A period that new hedge funds typically undergo in order to accumulate a

track record. It lasts at most a few years because the opportunity costs related to a fund’sincubation period can be quite significant. (Topic 11)

Industry loss warrants: A type of capital market-financed loss (re-)insurance, which is linkedto an industry loss index. It is usually in the form of private placements. (Topic 7)

Infrastructure funds: Funds that invest in companies that usually provide an essential service

to the community and have some monopoly power, e.g., a bridge, utility, or road. They

typically provide relatively steady income and provide a hedge against inflation. (Topic 7)

“Lend to own” debt financing: Providing debt financing, usually to highly levered

companies and in situations where the fund is indifferent about whether return is generatedfrom interest or principal repayments or from a hands-on operational turnaround if the

company defaults. (Topic 7)

Lifecycle: refers to the various stages of the infrastructure asset from inception to maturity. (Topic 7)

Lifecycle stage: One of the components of personal risk of an individual investor, which

refers to his or her earning power and the desire to leave a legacy. (Topic 8)

Liquidation bias: The finding that fund managers discontinue reporting their returns to a

database before the final liquidation value of a hedge fund, thus causing an upward bias in theobserved returns of dead funds. (Topic 11)

Liquidity conundrum: The relationship between liquidity and risk where liquidity isdependent upon the risk aversion of investors and not just determined by monetary





112

factors. The conundrum is that the increase in risk aversion lowers liquidity, which in turn

increases risk aversion and further lowers liquidity. (Topic 10)

Listed infrastructure funds: Infrastructure funds trading on exchanges (e.g. NYSE). (Topic 7)

Listed Private Equity: Traded securities that have the properties of private equity but also

trade on an exchange. These can be: public companies whose core business is private equity;quoted investment funds that co-invest with specific private equity funds; and specially

structured vehicles that invest directly in private companies and/or indirectly through various

private equity funds (Topic 11)

Lock-up: Period when the investor’s capital is committed to the fund and cannot be

withdrawn. (Topic 7)

Long/short equity: Strategies that employ a broad range of short-selling, that may or may not be quantitative, that may or may not be market-neutral, and where technology does not

necessarily play a significant role. (Topic 9)

Lookback straddles: A derivative that pays the holder the difference between the maximum

and the minimum prices of the underlying security over a certain period of time. (Topic 11)

Market integrity: A concept to describe the potentially major impact that a large hedge

fund(s) may have on the markets, given that a number of these funds are large enough to exerta major impact on the markets, and was made evident by the near bankruptcy of LTCM

(1998). (Topic 11)

Market risk: One of the three dimensions of risk, according to Kahneman and Tversky

(1979), that the ideal portfolio must address; increasing market risk should increase

returns. (Topic 8)

Market risk factors: Risk factors that affect hedge fund returns. Fung and Hsieh have

proposed a risk-factor model consisting of seven risk factors to capture the risk of diversified portfolios of hedge funds. (Topic 11)

Monoline Insurers: insurers that guarantee payments from certain types of structured credit

products. They often issued a “surety wrap” to increase credit status for senior tranches of anABS trust or CDO structure, despite the fact that the underlying collateral was subprime

mortgages. They are highly leveraged, yet carry a AAA rating. (Topic 9)

Mortgage spread: A trade that is a bet on prepayment rates, with a long position on a pool of

GNMA mortgages which is financed using a “dollar roll”. The position is delta-hedged with afive-year interest rate swap. (Topic 11)

Multiplier: Within a portfolio reallocation strategy such as “constant proportion portfolioinsurance,” it is the parameter that indicates how much an investor increases risky asset holdings

when the market rises and decreases risky asset holdings when the market falls. (Topic 8)





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Multistrategy hedge funds: Hedge funds that invest opportunistically among different hedge

fund strategies applied to global markets. A growing recent trend has been for hedge funds to progress from single-strategy specialists into multi-strategy investments. (Topic 11)

Normal backwardation: This term describes the case where the futures price for a

commodity is less than the expected spot price in the future, i.e., the expected price at thematurity of the contract, and suggests that futures prices generally trend up as they approach

maturity. This condition allows for positive excess returns as an investor takes long-term

futures positions and then rolls them over as they approach maturity. (Topic 8)

Option-based portfolio insurance: A portfolio reallocation strategy wherein the investor sets

a floor value at which all risky investing terminates and increases risky asset holdings atvalues above that floor value such that the ultimate risk exposure of the strategy mirrors that of

a portfolio comprised of risk free bills and call options on a risky asset. (Topic 8)

Payoff distribution approach: It attempts to replicate hedge fund returns by matching higher moments but not the first moment. The approach is based on the premise that two random

variables can be equal almost surely or equal in distribution. (Topic 7)

Personal risk: One of the three dimensions of risk, according to Kahneman and Tversky

(1979), that the ideal portfolio must address; personal risk refers to the possibility of a fall in

the investor’s lifestyle and the resulting anxiety associated with that possibility. (Topic 8)

Positive Feedback Mechanisms: The term for the circular dependence between refinancingand collateral valuation. If asset values decline, the ability to refinance declines, valuation of

counterparty collateral declines, value of monoline assets declines, and the value of the

guarantees given by monolines declines. This contributes to systemic risk.

Post-Venture Capital Index (PVCI): measure composed by Venture Economics as arepresentative investment in private equity. The performance of the PVCI is biased because of the effect of IPO underpricing. (Topic 11)

Private (direct) commercial real estate - debt: The direct purchase of issued whole loans.

This component of commercial real estate is accessible only to large investors, given the largeamounts of capital needed to participate in it. (Topic 8)

Private (direct) commercial real estate – equity: This segment of commercial real estate

involves the acquisition and management of actual physical properties. (Topic 8)

Public (indirect) commercial real estate - debt: This component of commercial real estate is

constituted primarily by commercial mortgage-backed securities (CMBS). (Topic 8)

Public (indirect) commercial real estate - equity: This segment of commercial real estate

involves buying shares of real estate investment companies (REITs) and other listed real estatecompanies. (Topic 8)





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Quantitative equity market-neutral: Investment strategies using broad types of quantitative

models, some with lower turnover, fewer securities and inputs other than past prices, such asaccounting variables, earnings forecasts, and economic indicators (Topic 9)

Resampled mean-variance optimization: An improved adaptation of the Markowitzframework that explicitly recognizes that there is uncertainty in the future regarding the capital

market assumptions driving the asset allocation model. (Topic 8)

Risk premium: The increased expected return (usually expressed as an annual percentage

rate) for bearing risk, typically defined as the expected return of a risky asset minus the risk free rate. In the futures market, it is the difference between the expected spot price and the

futures price. (Topic 11)

Roll Return: Also known as “roll yield” generated in a backwardated futures market is

achieved by rolling a short-term contract into a longer-term contract and profiting from the

convergence toward a higher spot price. (Topic 8)

Second order autoregressive reverse filter: It is an approach to generate an unsmoothedtotal real estate return series. This autoregressive process has more than one lag and thus gives

a more generalized model than the first order autoregressive model (FOARF). (Topic 11)

Selection bias: This bias arises because inclusion in a database is voluntary (i.e. it is at the

discretion of a hedge fund manager). The existence of this bias implies that hedge funds in a

database may not be representative of the universe of the industry. (Topic 11)

Serial correlation of hedge fund returns: Research has shown that hedge fund index returns

have serial correlation (autocorrelation), which implies that hedge fund current returns tend to

be correlated to past returns. (Topic 11)

Shadow banking system: Levered intermediaries such as hedge funds and structured conduits

provided by SIVs that are largely unregulated and that provide liquidity to the financial

markets by taking on risk. (Topic 10)

Short reset loans: Loans with low teaser rates for the first two or three years, referred to as

2/28 and 3/27 hybrid sub-prime ARMs. The rates increase after the initial period. (Topic 9)

Short-termism: Short-term thinking, related to group think, where an investor gets caught up

in the current market mood and ignores the long-term view or fundamentals. (Topic 10)

Side pockets: Separate accounts within hedge funds designed to hold investments that differ

from the primary strategy of the funds. These investments may be more illiquid and treated

separately as far as fees and redemptions are concerned. (Topic 7)

Smoothing: A phenomenon that produces a lag effect and reduced volatility in valuation- based indices when compared to the underlying market, which is measured by more precise

transaction-based indices. An important consequence of smoothing is that it causes risk to be

underestimated. (Topic 11)





115

Special Investment Vehicle (SIV): A limited-purpose, bankruptcy-remote company that

purchases mainly high-rated medium- and long-term assets from its parent company. The SIVfunds these purchases with short-term ABCP, MTNs, and subordinated debt capital. (Topic 9)

Special purpose vehicle (SPV): The legal entity (e.g., trust) that forms the core of

collateralized obligation (CO) structures. The SPV is the entity that legally owns the collateral(e.g., funds, securities or other assets) and is the entity that issues the various tranches that

have claims to the cash flows (senior, mezzanine and equity). (Topic 7)

Stale prices: A term to describe prices when a firm does not change reported prices

frequently, which makes it challenging for investors who want to measure the risk-adjusted

performance of private equity, (Topic 11)

Statistical arbitrage: Mean-reversion strategies which are short-term and highly technical;

they use large numbers of securities, very short holding periods, as well as substantialcomputational, trading and IT infrastructure (Topic 9)

Survivorship bias: In a hedge fund database, the average return of surviving (or live) funds inexcess of the average return of all funds, both surviving and defunct. This bias amounts to

roughly 2.5% a year. (Topic 11)

Swap spread: A bet that the fixed side of the spread will remain higher than the floating side

of the spread. (Topic 11)

Synthetic hedge funds: Replication of hedge-fund-like returns using mathematical models.

The idea is that these returns can then made available to investors at a lower cost than thatarising from directly investing in hedge funds. (Topic 11)

Systemic risk : The risk to the entire financial system as opposed to just one area. A general

lowering of liquidity and an increase in risk aversion can affect all financial markets and thecollapse of the system. (Topics 10 and 11)

Tail risk: This is the possibility of extreme and rare events that can produce large losses. It is

higher when the distribution of returns has “fat tails,” i.e., is leptokurtic. (Topic 10)

Time varying factor exposure: Hedge fund managers may change their exposures as

positions are closed out and new positions are opened. Depending on the duration of the trade,

the effects of these changes will be evident in the factor exposures. (Topic 7)

Toehold positions: Positions taken by private equity companies in the distressed securities of

public companies in order to identify opportunities in distressed companies that they intend totake private. (Topic 7)

Top-down approach: An approach in which the risk factors affecting diversified hedge fund

portfolios are modeled. Fung and Hsieh propose a risk-factor model using seven factors to

encompass the risk of diversified portfolios of hedge funds. (Topic 11)





116

Uncertainty: Describes a situation where outcomes and probabilities are not known.

According to Knight (1921), it is different from risk where the probabilities and outcomes areknown. (Topic 10)

Unlisted infrastructure funds: infrastructure funds not trading on exchanges. (Topic 7)

Valuation: In the context of illiquid investments, it is one of the challenges associated with private equity or a private real estate fund. This is because: a) these investments trade

infrequently, b) accounting rules tend to push general partners to account these assets at book

value, and c) there is always uncertainty regarding the precision of asset valuations. (Topic 10)

Waterfall Payment Structure: A payout scheme where cash flows are assigned to a range of

low-grade to high-grade tranches. The high-grade or “senior bonds” are paid first, and the junior tranches do not get paid if the collateral pool becomes stressed in certain ways, e.g.,

there is a change in the collateral/liability or cash-flow/bond-payment ratios. (Topic 9)




117

Index: Level 2, Topics 7–11

AAbatement Strategies, 7 – 8 Adjustment Strategies, 7 – 8

Alignment of interests, 94

Allocation drift, 79 – 80

Alt-A mortgage loans, 64

Alternative alphas, 98

Alternative betas, 98

Arithmetic return, 38

Aspirational risk, 29 – 32

Asset-backed securities, 1

Asset-backed security trust, 65

Asset-based style factors, 98

B

Backfill, 96

Backwardation, 47, 85 – 86

Bankruptcy remote, 1

Barbell strategies, 31 – 32

Basis, 86

Black-Litterman asset allocation, 42 – 43

Bottom-up approach, 96

Buy-and-hold, 19 – 27

Buy-to-own investing, 16

C

Calendar spread strategy, 48 – 49

Capital calls, 79

Capital-structure arbitrage, 97

Carbon funds, 12

Catastrophe bonds, 8 – 11

Catastrophe risks, 8 – 11

Cat-risk CDOs, 10

Centralized Clearing House, 71

Clean Development Mechanism, 12

Climate-related investments, 8 Collateralized commodity obligation, 3

Collateralized fund obligation, 2 – 3

Commitment strategy, 79

Commodity trigger swaps, 3

Compensation contract design, 94

Complex adaptive systems, 76

Concave payoff curves, 19 – 21, 26 – 27

Conditional factor models, 13

Constant mix, 19 – 28

Constant-proportion portfolio insurance, 21 – 28 Contango, 34 – 35, 39, 47 – 48, 52, 85 – 86

Contingent capital arrangements, 10

Contrarian, 59 – 61

Convergence, 14, 16 – 18

Convergence of leveraged opinions, 99 Convex payoff curves, 19 – 21, 26 – 27

Credit enhancement, 65

Credit spread, 97

D

Dimson beta, 103 Distributions, 79

E

Emission credits, 11

Emission rights, 11

EU Allowances, 11

EU Emission Trading System, 11

Event loss swaps, 10

Exposure, 77

Exposure diagram, 24

F

Factor-replication approach, 12 – 13

First Order Autoregressive Reverse Filter

(FOARF), 88 – 93

Fixed income volatility, 97

Floor, 22, 27 – 28

Full Information Value Index (FIVI), 88 – 92

G

Geometric return, 38

H

Hazard rate, 96

Hybrid asset, 4

Hybrid funds, 16 – 17

I

Illiquidity, 78, 104

Incentive fee, 95



Index: Level 2, Topics 7–11

Incubation bias, 96

Incubation period, 96

Industry loss warrants, 10

L

Lend to own debt financing, 16

Lifecycle, 4

Lifecycle stage, 4

Liquidation bias, 96

Liquidity conundrum, 77 – 78

Listed infrastructure funds, 5

Listed private equity, 102

Lock-up, 16

Long/short equity, 57 – 63

Lookback straddles, 96

M

Market integrity, 99 Market risk, 29 – 32

Monoline insurers, 65 – 67

Mortgage spread, 97

Multistrategy hedge funds, 95

N

Normal backwardation, 34 – 35

O

Option-based portfolio insurance, 23 – 25

P

Selection bias, 95

Serial correlation of hedge fund returns, 96

Shadow banking system, 77

Short reset loans, 64

Short-termism, 78

Side pocket, 15

Smoothing, 86 – 87 Special Investment Vehicle, 65 – 66, 68 – 70, 77

Special Purpose Vehicle, 1 Stale prices, 100, 102 – 103

Statistical arbitrage, 57, 61

Survivorship bias, 95 – 96

Swap spread, 97

Synthetic hedge funds, 95 Systemic risk, 64, 73, 77, 99

T

Tail risk, 63,73

Time varying factor exposure, 13

Toehold position, 16

Top-down approach, 96

U

Uncertainty, 75 – 78

Unlisted infrastructure funds, 5

V

Valuation, 79

W

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