Journal of Housing Economics 10, 253–277 (2001) doi:10.1006/jhec.2001.0290, available online at http://www.idealibrary.com on Quantifying Lending Risks without Historical Data: An Application of Stress Tests to Mortgage Lending in Russia 1 Robert Buckley World Bank, 1818, H St. NW, Washington, District of Columbia 20433 Elena Klepikova The U.S.–Russia Investment Fund, PC Mortgage Finance Netherlands B.V, Sadovnicheskaya naberezhnaya 9, 113 03 S, Moscow, Russia Robert Van Order Freddie Mac, 8200 Jones Branch Drive, McLean, Virginia 22102 Received February 12, 2001 I. INTRODUCTION The liberalization of financial systems around the world has been accompanied by severe financial distress in many places. As Caprio and Klingebiel (1996) show, severe systemic distress or crises have affected 58 countries in the past 15 years. As a result, the question of how to deal with the risks of new financial products has received increasing attention by both policy-makers and academics. The question of systemic risk exposure is particularly important for transition economies, which are now establishing de novo financial systems from the ashes of financial systems that served more as accounting devices than they did as ways of allocating and pricing risks, see Long (1999). As shown by Hegedus et al. (1997), for many years these economies essentially did not have any market- based mortgage finance. As a result, they lack an historical basis to establish risk classes and underwriting standards. Correspondingly, from a financial supervisory 1 An earlier version of this paper was presented to the last professional conference Steve Mayo attended, at the European Network of Housing Researchers Conference at Lake Balaton, Hungary, in September 1999. We thank Peter Englund, Joszef Hegedus, Bengt Turner, Steve, an anonymous referee, the editors, and other conference participants for their comments. The views expressed do not represent those of the authors’ institutions. 253 1051-1377/01 $35.00 q 2001 Elsevier Science All rights reserved.
Transcript
Journal of Housing Economics 10, 253–277 (2001)
doi:10.1006/jhec.2001.0290, available online at http://www.idealibrary.com on
Quantifying Lending Risks without Historical Data:An Application of Stress Tests to Mortgage
Lending in Russia1
Robert Buckley
World Bank, 1818, H St. NW, Washington, District of Columbia 20433
Elena Klepikova
The U.S.–Russia Investment Fund, PC Mortgage Finance Netherlands B.V,Sadovnicheskaya naberezhnaya 9, 113 03 S, Moscow, Russia
Robert Van Order
Freddie Mac, 8200 Jones Branch Drive, McLean, Virginia 22102
Received February 12, 2001
I. INTRODUCTION
The liberalization of financial systems around the world has been accompaniedby severe financial distress in many places. As Caprio and Klingebiel (1996)show, severe systemic distress or crises have affected 58 countries in the past15 years. As a result, the question of how to deal with the risks of new financialproducts has received increasing attention by both policy-makers and academics.The question of systemic risk exposure is particularly important for transitioneconomies, which are now establishing de novo financial systems from the ashesof financial systems that served more as accounting devices than they did asways of allocating and pricing risks, see Long (1999). As shown by Hegedus etal. (1997), for many years these economies essentially did not have any market-based mortgage finance. As a result, they lack an historical basis to establish riskclasses and underwriting standards. Correspondingly, from a financial supervisory
1An earlier version of this paper was presented to the last professional conference Steve Mayoattended, at the European Network of Housing Researchers Conference at Lake Balaton, Hungary,in September 1999. We thank Peter Englund, Joszef Hegedus, Bengt Turner, Steve, an anonymousreferee, the editors, and other conference participants for their comments. The views expressed donot represent those of the authors’ institutions.
253
1051-1377/01 $35.00q 2001 Elsevier Science
All rights reserved.
254 BUCKLEY, KLEPIKOVA, AND VAN ORDER
perspective, it is much more difficult for them to assess and manage institutionalrisk exposure as attempted, for example, in the Basel Accords, which are de-scribed below.
For a number of reasons, concerns with managing institutional risk exposureare particularly important for mortgage lending. First, mortgage lending hascontributed to the severe problems of systemic financial distress in even someof the most stable economies. For instance, while the difficulties of the U.S.savings and loan crisis are well known, following financial liberalization, similarproblems also arose in the UK, Sweden, Denmark, and Finland (see Mishkin(1996)). Hence, in the transition economies, which are subject to considerablyhigher levels of macroeconomic instability, the question of how to integratemortgage finance into their emerging financial systems may be of considerableinterest.
Second, in order to appreciate the systemic risks of mortgage lending, clarityas to the implications of the effects borrower behavior can have on loan valuecan be very important. Jappelli and Pagano (1989) and Diamond and Lea (1992),for example, both show the wide differences in mortgage lending rates acrosscountries that arise due to the differences in the credit risks implied by seeminglyminor variations in loan terms. Similarly, the lack of strong empirical models ofmortgage prepayment led to the large variations in the pricing of mortgage-backed securities in the United States during the 1980s (see Lewis (1999)). Again,empirical evidence, or, in its absence, sensitivity analysis of likely scenarios, canbe useful in reducing the effects of unanticipated mortgage price variability, andhence risk exposure.
Finally, in the coming years these countries are likely to establish de novohousing finance systems in any event, probably with little firm data. This islikely to be the case because, as shown by Hegedus et al. (1997), followingprivatization the transition economies have both a greater share of homeownersthan do OECD economies and the least-encumbered housing stock in the world.The stock of mortgage debt for these nations of homeowners rarely approachesand never exceeds 2% of GDP. Their position in this regard is in sharp contrastwith that of the 12 richest OECD economies where the stock of mortgage debtoutstanding averages more than 40% of GDP, and in some places approaches100% of GDP even though homeownership rates in these countries are lower.2
Now that relative macroeconomic stability and growth have been achieved bymost of the transition economies, it is likely that mortgage finance in thesecountries will grow rapidly in the coming years. However, despite the prospectsfor rapid growth no transition economy has yet developed even the generalstructure of their mortgage lending system sufficiently to be able to characterizethe type of system being developed into one of the two broad types of systems
2The OECD figures come from the EU Mortgage Federation, Brussels, 1999; the figures ontransition economies can be inferred from Merrill (2000).
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described by Allen and Gale’s (1995) welfare analysis of financial systems.3
Because housing finance systems have been shown to be sensitive to macroeco-nomic volatility—again, even in some of the oldest systems of the world’s moststable economies—it may be useful to consider, at least in broad terms, thepossible path dependency implications of alternative approaches for new systemsin newly restructured economies. Such an analysis could help assure that thehousing finance system’s indirect effects on the overall financial system are not,as Glaser (2000) says about civil law, “baked in the cake” of decisions takenlong ago.
This paper considers a stress test approach to evaluating mortgage risks, usingRussia as a case study. It presents some simple versions of stress tests like thosethat financial institutions and regulators in the United States use to evaluate theirown portfolios. Russia is an interesting place to apply such models for at leasttwo reasons. First, in the states of the former Soviet Union mortgage finance isan important policy question in its own right. The famous “500 days” blueprintfor reform prepared for Gorbachev in the early 1990s, known as the ShatalinReport, for example, indicated that the housing sector was the least efficientsector in the old regime. More recent studies, by Freinkman et al. (1999) andKosareva et al. (2000), have shown that in the absence of finance, housingsubsidies have remained extraordinarily high. Given these distortions and fiscalconsiderations, it is not surprising that reform of this sector has motivated consid-erable donor and government interest and assistance.4 For example, with U.S.Government support, the government of Russia has capitalized a secondarymortgage market facility modeled on the U.S.’s Federal National Mortgage Asso-ciation (Fannie Mae), known as “Nastasha Mae” (see Struyk and Kosareva(1999)). How this new sort of lending or institution will fit into Russia’s fragilefinancial system, and how it might affect or replace the current high levels ofhousing subsidies is an important policy question.
Second, in many ways mortgage stress test models are in much the same spiritas the Angel and Mayo (1996) Housing Indicators Program. Just as the indicatorsprogram was designed to help describe the functioning of a country’s housingsector in broad summary statistics, so that the effects of policy distortions mightbe more easily inferred, so too does the exercise of applying simple stress testmodels to mortgage lending decisions provide a synthetic summary perspectiveof the possible broader risks involved in particular approaches to lending. Like
3Allen and Gale’s analysis categorizes financial systems into two basic types—i.e., either thedirect intermediation/bond market model in which individual investors bear specific investment risksor the financial intermediary model in which financial institutions stand between investors andinvestments. They describe these two stylized systems as being representative of the U.S. and Germanfinancial systems, respectively.
4See Renaud (1995) for a fuller discussion of the rationales for housing reforms in Russia.
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analysis of the data developed in Indicators Program (see Angel (2000)) suchsimple models can sometimes provide insights or clues as to the effects of variouspolicies. They can, for example, provide a basis for broadly evaluating the risksand contingent liabilities that are implied by various loan conditions and policies.Further, based on their analysis one can, at least qualitatively, determine theeffects that variations in seemingly minor loan terms—e.g., such as variationsin prepayment conditions—can have on capital requirements. In this sense, whileRussia is used as a case study, the discussion is presented so that the methodologyitself and the linkages of this methodology to other areas of the literature onfinancial institutions is made clear.5
The plan of the paper is as follows. First we discuss what is involved in stresstests. Then we apply a simple test to two current mortgage programs operatedby a donor-supported intermediary in Russia, the U.S.–Russia Investment fund,TUSRIF, to evaluate a hypothetical lending program of the sort that TUSRIFmight undertake. A final section concludes.
II. CAPITAL, RISK, AND STRESS TESTS
Mortgage lending is risky. The risks come primarily from two sources, creditrisk and interest rate risk (e.g., duration mismatch). Here we set up pilot stresstests for analyzing these risks and the means for coping with them.
For instance, credit risks can be mitigated with bigger downpayments or asin the case of both the primary market and secondary market programs that weanalyze below, by someone else, the banks or a local government, taking on orat least sharing the credit risk. Ultimately the details of risk sharing depend ontradeoffs among things like the relative abilities to bear risk, information differ-ences, and scale economies. Here the interest is in using stress tests to show roughorders of magnitudes of various risks. An important risk is that the guarantors maynot be able to handle the risk they are guaranteeing. Hence, even with recoursean institution like TUSRIF is still subject to some sort of credit risk, and as itexpands to take on nonguaranteed mortgages it may take on credit risk moredirectly.
Interest rate risk can be managed, for instance, by making loans whose durationsmatch those of the liabilities; in many countries short-term deposits are used tofund short-term or adjustable rate mortgages (ARMs) or pass-through securitiesor long-term bonds are used to fund long-term fixed rate mortgages, but theserisks can be complicated by call risk (e.g., when mortgages can be refinanced
5The work in this paper was begun as a way of resolving disagreements as to the risks of variouslending policies by Russian mortgage intermediaries. The modeling exercise was designed not togive definitive answers but rather to serve as a basis for comparing and discussing the likelyimplications of various measures.
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at par) and basis risk (e.g., when mortgage payments are indexed to interest ratesthat do not move exactly with the lender’s borrowing costs).
We look at both interest rate and credit risk; in particular we focus on therisks to TUSRIF in the primary market program where the banks guarantee thecredit risk but the risk to TUSRIF is that the banks will fail. We do this by firstlooking at the risks themselves, as if TUSRIF were taking on all the credit andinterest risk itself, and then we model the guarantees implied by lending to banksas equivalent to holding the loans but with recourse back to the banks. We usethe stress test models to determine the amount of capital required for a financialinstitution to survive for a period of 10 years after some stressful shock. Firstwe discuss the role of capital.
Capital Adequacy
Capital is a buffer that absorbs losses when a firm gets into financial difficulty.Once the firm’s capital runs out, debt-holders or those who guarantee the firm’sdebt absorb the losses. At that point, the firm is said to have failed and mayenter bankruptcy proceedings and liquidation. The greater the capital cushion,the less likely it is that a firm will fail and the less likely that debt-holders orguarantors will lose money. From TUSRIF’s perspective a bank’s capital is thecushion that helps it absorb credit losses. When it runs out of capital or isbankrupt, then it can no longer fulfill its obligation to take credit risk and TUSRIFis on the hook.
Most financial institutions are subject to capital requirements to control theprobability of failure, because failure of a financial institution, especially a largeone, can be disruptive to the real economy and/or costly to the taxpayer. U.S.banks, for example, are subject to regulatory capital requirements in part becausethe federal government guarantees most deposits. In any event, capital require-ments are intended to limit the probability of failure, and it is in that contextthat we need to evaluate different ways of determining capital requirements.
Bank capital requirements are typically based on an international standardknown as the Basel Accord (1988) (see Stone and Zissu (1993) for discussions),which was originally meant to apply to internationally active banks as a minimumstandard to help level the playing field across countries. Required capital is equalto 8% of “risk-weighted assets,” where the weights are to be chosen to beproportional to the relative risk of the different assets. Such a requirement is animprovement over the simple leverage ratio (i.e., a single capital-to-asset ratio)requirements of the past because riskier assets, at least from the point of viewof credit risk, are given in the formula more weight than less risky assets, andoff-balance sheet exposures, which represent risks but are not “assets,” are, inprinciple, included in the definition of “risk-weighted assets.” Thus, today’scapital adequacy standard for banks relates more to risk than it did before.
While an improvement over the past, current capital rules still only crudely
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reflect risk (see Golding and Van Order (1993) for a discussion). Commercialloans (capital requirement 8%), for example, are not likely to be only twice asrisky as qualifying single-family mortgage loans (capital requirement 4%). Awide range of credit risks is possible within a given risk category under the risk-based capital rules.
Perhaps more important, the capital adequacy rules as of 1988 do not try tocapture risks other than credit risk, such as interest rate risk, or measure howdifferent risks move together to determine the overall risk of the portfolio, leavinga lot of potential for hiding real risk-taking. It is the overall portfolio risk thatdetermines the probability of “failure,” the limitation of which is the objectiveof capital requirements. The most important risks faced by most financial institu-tions, credit risk and interest rate risk, can be combined in ways that offset eachother, and they can be minimized by diversification. For these and other reasonsthe Basel accords are in the process of revision, adding more risk buckets andmore sophisticated ways of measuring and controlling risk in an effort to providebetter incentives for banks’ risk-taking.
Given the limitations of the Basel Accord’s “risk-based” capital adequacystandard in identifying relative risks of portfolios, many banks have advancedtheir own models to evaluate risk and capital adequacy. The more sophisticatedof those models are known as “Value at Risk” (VaR) models (see Kupiec (2001) fora discussion). VaR models have the common characteristic that capital adequacy isdefined as the amount of capital needed to limit the probability of failure (“failure”is zero or negative net worth; for a given portfolio (e.g., the trading book) “failure”occurs when losses exceed capital allocated) over a specified period of time toa low level, say, 1%. Thus, the “value” that is at risk is the value of the portfolio,and the amount of portfolio value at risk (which is close to the amount of capitalrequired) is the amount that could be lost with a given probability over thespecified period of time. The VaR approach is attractive because capital adequacyis defined in terms of an explicit probability of failure, which takes most relevantrisks and risk mitigators (like diversification) into account.
VaR models so far have mainly been used to evaluate the risks and capitalneeds of trading portfolios. This is because a good deal of price information isavailable for frequently traded, standardized assets. Thus, the probability distribu-tion of the trading portfolio’s value can be estimated from observing what hashappened to market prices over the relevant time horizon (e.g., two weeks).However, applying VaR models to large portfolios of less liquid or more compli-cated assets or, as in the case of Russia, assets for which there are few data onpast performance is difficult because of both the lack of data and the greateruncertainty about the stability and nature of the probability distributions of failure(particularly the properties of the tails of the distributions) in the future. Hence,VaR models are not often used to evaluate credit risk, or to evaluate capitaladequacy of the firm as a whole (i.e., taking account of credit risk and interest raterisk of the nontraded portfolio as well as the market risks of the trading portfolio).
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Stress Tests
Stress tests (see Allen and Foster (1987) and Kupiec (1998)) are an alternateapproach to risk analysis and capital adequacy that attempt to address the limita-tions of the capital/asset ratio-type standards such as traditional leverage ratiosor the Basel requirements without the implementation problems or data demandsof VaR models. Stress tests sidestep the problem of developing a completestatistical model of the firm (the problem faced by VaR models) by simplypostulating stressful situations and estimating the amount of capital necessary tosurvive these situations. As such, they can be thought of as an approximation toVaR models where the worst 1% of what the firm can experience is replaced byone or more specific stressful scenarios. This is likely to be a preferred form ofanalysis (vs VaR) in situations where there is great uncertainty about the parame-ters of the distributions needed to generate the VaR model, but some agreementabout what constitutes a “bad” scenario or set of scenarios.
A stress test is a simulation of an economic shock that negatively affects thefirm’s solvency. Choice of stress test depends on data availability, the Depressionin the 1930s being a source for some U.S. models, but it is possible that therecent crises in Asia and Russia will provide better tests. A “survival” standardis then imposed to determine the capital adequacy target, such as that capital besufficient to survive 10 years. Capital is typically measured in accounting terms,but it could also be the estimated market value at the end of the stress test period.Capital requirements that come from a stress test can be thought of as crude riskmeasures. Critical elements of a stress test are the time period, the size of theeconomic shock, and the path along which the shock is realized.
The stress test approach to determining capital adequacy has clear virtues overthe capital/asset ratio-type standards such as traditional leverage ratios or theBasel requirements. First, stress tests provide a portfolio-wide approach to measur-ing risk and capital adequacy and capturing interest rate risk together with creditrisk or other risks, as well as the interactions among the risks. They also areharder to “game” by redefining or rebundling the institution’s assets; risks cannotbe “hidden” by being put off balance sheet. Second, they are a natural way ofhandling risk-sharing programs because it is possible to run simultaneous testsfor two or more institutions and examine the cash flows to each to see if, giventhe terms of the risk-sharing, the institutions can fulfill their obligations understress. Third, because stress tests can have a basis in real data, they can capturerisk in a way that has an empirical foundation. This kind of empirical advantage,however, is not the case in situations like Russia where there are at most a fewyears of data.
Nevertheless, one of the points of this paper is that stress test models can stillbe very useful in these contexts, particularly when the limited empirical evidencethat is available can be more misleading than helpful. For example, when thecredit risk implied by a particular financing structure can vary by a factor of
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almost 5 within a 5-year period (as has been the case in the United States withFHA),6 a couple of conclusions are clear. First, mortgage credit risk can bestrongly affected by macroeconomic considerations, and second, that in manycircumstances even a decade or more of data (perhaps just one business cycle)does not provide much sense of the likely risks involved in some contracts. Insuch contexts, as we hope to show, conjectures about data can still provideconsiderable insights into the risks involved in particular financing arrangements.
At the same time, the stress test approach has important limitations. First,some risks cannot be easily quantified and included in the analysis, such asmanagement and operations risk. This might be handled by adding a “surcharge”for such risk, based on examination results or on a credit rating done by one ormore private rating agencies. Second, the approach does not give credit forgeographic diversification. Finally, unlike the VaR framework, the stress testapproach does not attach a clear probability of failure to the capital adequacystandard. It is difficult to tell if different stress tests are equally probable, and itis difficult to judge the comparability of stress tests applied to different institutionsand to allow for changes in what is stressful over time.
It is probably impossible to calculate the “right” level of capital for a financialinstitution. Stress tests, while imperfect, are capable of estimating when risk hasgone up, and what types of strategies are riskiest; they are much more difficultto “game” than are the current “risk-based” capital rules, making implicit subsidiesfrom risk-taking more explicit and costs more transparent, and they do not relyon tenuous assumptions about the tails of distributions. In the United States theregulator of the two major secondary market institutions, Fannie Mae and FreddieMac, is about to use stress tests as the main tool for assessing capital adequacy.
There are, of course, important limitations in applying stress tests to a Russianinstitution, because of both data and accounting limitations and the embryonicstage of Russian financial markets. On the other hand, stress tests and judicioussimulation are probably the best way of getting a handle on risks in a genuinelyuncertain situation.
III. THE U.S.–RUSSIA INVESTMENT FUND MORTGAGEPROGRAM IN RUSSIA
Strategy after the Financial Crisis
TUSRIF or (the Fund) is a private, but partly public-funded, U.S. investmentfirm established to promote the development of a free market economy in the
6Capone(2000) follows the cohort performance of FHA insured loans for various years. He showsthat the projected claims rate for the 1977 cohort is 4.67% while that of the 1981 cohort is 23.1%.
STRESS TESTS TO MORTGAGE LENDING IN RUSSIA 261
Russian federation. The Fund’s mandate is to encourage private sector develop-ment while assisting in the long-term growth and profitability of businesses ofall sizes in Russia.
Despite the financial crisis in Russia the Fund considers the banking sector tobe an important and promising direction for its investment activities:
• The 1998 financial crisis in Russia made long-term residential mortgagelending even more important as an instrument of recovery of the Russian bankingsystem. Through offering mortgages the banks may attract potential clients forother loans or deposits.
• The acceptance of traditional bank retail lending instead of the speculativeinvestments in governmental securities and currency operations in which Russianbanks have participated until recently will attract nongovernment funds intohousing, housing construction, business loans, etc., thus moving money into thereal sector of the economy and moving Russian banks toward more traditionalbanking activities.
• Most recently the economic conditions for long-term mortgage lendinghave become less favorable because of increases in risk factors (both interestrate risk and credit risk). Although those risks can be managed to some degreethrough matching assets and liabilities, fixing the cost of funds, choosing thetargeted markets and loan instruments, and conservative underwriting criteria,they are still large, and understanding and controlling them is very important.There are likely to be external benefits to the rest of the financial system fromcontrolled experiments in mortgage lending by the Fund. Although the Fund’srecent experience has demonstrated the possibility of residential mortgage lendingwithin the existing Russian legal framework and economic environment, a lotremains to be done in order to facilitate the development of large-scale residentialmortgage lending in Russia and to achieve the goals set by the Fund.
The activity of the Fund under the residential mortgage program can be broadlydivided into two areas: the primary market program, in which the Fund lendsmoney to banks who in turn make mortgage loans, and the secondary marketprogram, which involves lending through a secondary market intermediary. Bothprograms involve risks, e.g., from default on the mortgages, but the programsallocate the risks differently. Stress tests are a good way of analyzing importantaspects of risk and risk-sharing schemes. First we turn to descriptions of theprograms.
Key Elements of the Mortgage Mechanism
Mortgages will be made by commercial banks to borrowers for the purposeof acquiring residential property. Loans will be fully amortized, and the termwill be 5 to 10 years. The interest rate will be fixed and loan terms denominatedin dollars. This leaves the borrower with some exchange rate risk, which also
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implies some credit risk due to exchange rate-induced fluctuations in the dollarvalue of the property and cash flow risk for borrowers not earning incomein dollars. The Agency and TUSRIF will establish underwriting criteria forparticipating banks. It is the responsibility of the bank to ensure that the mortgagedocuments are fully enforceable and that the bank is clearly identified as themortgagee. Clearly a risk is that in Russia there is uncertainty about what contractsare enforceable.
The Primary Market: Lending to Banks
This program provides direct long-term financing to the Russian banks. Thebank takes the credit risk, and TUSRIF only takes credit risk if the bank is unableto pay the debt payments to TUSRIF.
At this stage in Russia’s development the role of the Fund’s activity in establish-ing a mortgage system is increasing significantly. The Bank Partner Program ofthe Fund is unique in that the financing comes to Russian banks at a fixed costof funds in dollars. In some regions it is now the only source of funds for thebanks in general and for mortgage lending in particular. The Fund is offeringlong-term funding that supports affordable residential mortgages offered by thebanks. The lack of significant and external sources of long-term funding is acrucial obstacle to the large-scale development of the mortgage market in Russia.At the moment two mortgage programs are being launched, one in St. Petersburgand one in Sakhalin in the Far East. The Fund is in the process of evaluatingthe financial stability and readiness of the potential banks to be designated thepartners of the Fund.
The bank will borrow from TUSRIF and originate residential mortgages, takingthe credit risk. However, banks can fail, and if they do, TUSRIF is on thehook for residual credit risk, which could be substantial. In terms of Americanexperience, this program is rather like TUSRIF buying the loans outright butwith recourse to the originating bank. There appears to be no interest rate riskin the program because the loans to the bank will have the same duration as themortgages and prepayment risk does not, as yet, appear to be a problem.
The Secondary Market (Cooperation Strategy): Investing in Mortgages
The cooperation scheme includes TUSRIF as an investor in both the primaryand secondary mortgage markets, with the Agency for Home Mortgage Lending,or Natasha Mae, as described above.7 The agency would act as a secondary
7The Agency for Housing Mortgage Lending is the only existing Russian secondary marketinstitution. It was created to encourage the development of a private sector mortgage lending industryby creating the liquidity and long-term funding facility for housing mortgage loans made by Russiancommercial banks, to encourage sound mortgage lending practices by providing technical assistanceto the banking community and to standardize mortgage lending procedures in the context of develop-ment of a domestic capital market. The Agency has not yet created a portfolio of mortgages and islooking for potential long-term investors. The Fund seems to be the first one.
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market institution and commercial banks as mortgage originators and servicers.In this scheme TUSRIF makes warehouse loans to the banks but invests inmortgages indirectly by lending money to the Agency to buy the loans from thebanks (who take the credit risk), while a third party (e.g., the government of St.Petersburg) provides a backup guarantee.
The first step is that TUSRIF will act as a warehouse lender, disbursing short-term (up to 3 months) loans to commercial banks for the purpose of originating(relatively) long-term (typically 5 years) mortgages to individual borrowers. TheAgency will play the role of technical and organizational consultant to commercialbanks in the primary mortgage market. Simultaneously the Agency will confirmits intention to purchase the mortgages after origination.
The second step involves lending to the Agency. The Agency is to receive acredit line amounting to the value of the mortgages the Agency will purchasefrom the banks. The credit period will coincide with the period the credit willbe on the Agency’s balance sheet (e.g., 5 years). TUSRIF will receive monthlyinterest and principal payments on the loan. According to the most recent agree-ment, the credit line will amount to $5 million and will be disbursed during oneyear from the beginning of the program. This program looks, to the extent thatthe government of St. Petersburg can make good on its guarantee if it needs to,like it has little credit risk, but depending on funding strategy, it could haveinterest rate risk for either the Agency or, e.g., if it funds some of its investmentwith debt, TUSRIF.
IV. THE STRESS TESTS
Basic Model
We use a very simple model of a firm with a balance sheet and an incomestatement. The model is essentially an accounting model, which defines variablesin an accrual, as opposed to cash flow or market value, framework. However,while in some cases accounting variables can be misleading, in our example thatis less the case. The simplicity of the model makes most of the accounting notionsquite similar to cash notions, and that we run our stress test for 10 years meansthat we are capturing most of what mark-to-market measures of equity wouldcapture (over 10 years most gains or losses will be “realized”).
To simplify the analysis we begin with a de novo financial institution, whichholds mortgages either on balance sheet, in which case it issues debt and equityto fund them and earns income from the spread between borrowing and lendingrates, or off balance sheet, in which case it funds them with pass-through securities(on which it might retain credit risk) and earns income from fees, and we assumethe firm does no new business as the mortgages are paid off. The basic equationfor the firm’s balance sheet is
Debt is a net number: if it is negative then the firm is holding rather thanissuing debt. In the simulations that follow short-term debt is defined as a residualthat balances the balance sheet; that is, other things equal, increases in equitycoming, e.g., from increases in income or prepayment will be used to reduceshort-term debt. Given the time paths of mortgage rates, debt costs, fee income,etc., dividend policy, and the initial conditions given by the balance sheet, (1),(2), and (3) give a complete description of the firm’s equity over time, and inthis simple model distinctions between accounting notions and cash flow areunimportant as are distinctions between what is on or off balance sheet (withrespect to the evolution of equity).
There are two purposes to the model: to control the probability of failure andto use that control to provide proper incentives for risk management. When andin what circumstances a firm “fails” (e.g., bankruptcy) is quite complicated anddepends in large part on legal and regulatory structures. We take a simple approachby focusing simply on solvency, which we measure by equity in Eq. (1). Failureis assumed to occur if book equity is zero, and “success” means going someperiod of time (10 years) without failing.
Equations (1), (2), and (3) yield a difference equation for equity. If we couldattach probabilities to the levels of the elements in (1), (2), and (3) we couldproduce a simple VaR model, which would give us the probability of equitybeing positive after T years and/or the amount of equity necessary to limit theprobability of equity being negative after T years to no more than some level p.We cannot generally expect to have data to do this properly; in particular, wecan only conjecture about the shape (normal distribution, a distribution with fattails, etc.) of the distributions, much less their parameters. Instead we use thestress tests, which pose a series of scenarios, and ask if equity will be positivefor the scenarios after T years.
Specific Stress Tests
Our stress tests begin with a spread sheet model of a financial firm that holdsmortgages that are financed with debt and/or pass through securities and equity.
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The table entitled “Base Case” (Table A-I in the Appendix) depicts a base casesimulation of the model with annual data over a 10-year period, depicting majorelements of the firm’s income statement and balance sheet, all in cash flow termsas given by Eqs. (1), (2) and (3). Bold face items are inputs; the rest of the itemsin the table are determined by the model. We model default and interest rate riskin a very simple way. The credit risk is simply imposed on the model, ratherthan, say, being derived from an option-based model (see Deng, Quigley andVan Order (2000)), and interest rate risk comes from duration mismatch, basisrisk (for ARMs) and (in one simulation) prepayment risk.
The first part gives basic assumptions used to generate projections of income,equity, etc. The first column gives the initial conditions, and the next 10 columnsgive assumptions—or perhaps more accurately, conjectures—for ten years. Thefirst row gives the short-term rate, which is the rate that the institution pays onone-year debt (or receives if it invests in short-term debt), for the initial yearand the next 10 years. In this case it is fixed at 20%. The next line gives thespread above the short-term rate that the financial firm earns if it has nonmortgageinvestments, and the discount factor is the reciprocal of one plus the short-termrate raised to a power equal to the number of years from the base year.
There are two types of mortgages, fixed rate (FRM) and adjustable rate (ARM,which adjust every year). Both mortgages have an interest rate, a prepaymentrate (which includes both prepayments and principal payments), and a defaultrate, all measured in percent per year, and a loss severity rate, which measureslosses from foreclosure as a percent of loan balance. Absent Russian data aboutdefault foreclosure rates are calibrated to be considerably higher than currentFreddie Mac projections for typical loans. For instance, the ARM projectionsimply a cumulative default rate (the fraction of loans that ever go throughforeclosure) of about 6%. This is a level two to three times higher than thehistoric Freddie Mac experience with ARMs with loan-to-value ratios of 70%.So, before considering how various shocks might perform, we have also assumeda relatively risky business environment. For ARMs there is a margin, which isadded to the short-term rate to obtain the rate on the ARM. Each mortgage typehas a guarantee fee, which is charged if the mortgage is securitized. It is calibratedto cover expected losses from default plus general and administrative costs(G&A costs below). Also depicted are long-term borrowing costs (assuming5-year debt), dividend payouts as a percent of income, investments as a percentof mortgages, administrative costs (G&A) as a percent of total mortgages (bothon and off balance sheet), and the tax rate, assumed to be zero.
These generate the projections for the income statement and balance sheet inwhat might be characterized as “normal” circumstances. In the case in the tablethe mortgages are all funded with pass-through securities, and the loans are allARMs, because all are securitized, so that income comes only from guaranteefees, and there is no interest rate risk. There are no taxes or dividends, and pricing
266 BUCKLEY, KLEPIKOVA, AND VAN ORDER
is such that in this simulation the firm has a rate of return equal to the short-term rate (20%) and equity also grows at 20% per year. Again, short-term debtis a residual that balances the balance sheet given prepayments, defaults, and thechange in equity, which is equal to net income.
This case gives the same results as that with 100% recourse to the banks(lenders) where the guarantee fee covers only G&A. If some loans are kept onbalance sheet, the various rates are calibrated so that the rate of return will besomething greater than 20%.
Results
The stress tests shock the base case and calculate the amount of capital, definedas equity in the base year, such that equity is zero at the end of the 10th year.A summary of results from various simulations is presented in Table I, whichpresents brief descriptions of the shocks. Table A-II in the Appendix (labeled“Credit Stress, No Recourse”) shocks the previous table by making both defaultrates and loss severity rates larger; e.g., the cumulative default rate increases toabout 23% (which is something like the experience during the Great Depressionin the United States and on the order of 10 times larger than FHLMC’s typicalexperience) and there is no recourse. As the table shows, with 4% capital equityis negative by 3,329 at the end of 10 years. For 5% capital equity is positive atthe end of 10 years, so that the capital requirement is between 4 and 5%, whichis recorded under “Credit Stress 1” in Table I.
The next table, Table A-III, adds recourse (with correspondingly lower guaran-tee fees), which is similar to the primary market model, where TUSRIF lendsmoney to the banks secured by the loans, but there is a risk that the recoursewill not be honored (or that the loans to TUSRIF won’t be paid off in full). Weassume a rather extreme case in which 50% of the banks giving the recourse failand TUSRIF is stuck with the loans. This is modeled by assuming that severityrates are half of what they were in the stress test without recourse (reflecting thehalf that do not fail). As the table shows the capital requirement is about 3%,which is recorded under “Credit Stress 3.”
Part 1 in Table I examines variants of the model by shocking the base casewith higher defaults and loss severity rate, but no interest rate risk. Defaults riseby a factor of 4 and, assuming no recourse, capital should be 4 to 5% of assets.With recourse the requirement is less. For instance, if 75% of the banks succeedin providing recourse and the firm has access to the mortgages of the failedbanks, then the capital requirement is about 2%. If 50% fail (as in Table A-III)it’s 3%. If there is no access to the mortgages (so that TUSRIF loses 100% ondefaulted loans at failed banks) and 50% fail, then it is 5%.
The second part analyzes interest rate risk. It simulates the model for portfoliosthat are funded (except for Rate Stress 5) with 1-year debt with base case creditrisk. For uncapped ARMs with no lags or basis risk there is essentially no interest
STRESS TESTS TO MORTGAGE LENDING IN RUSSIA 267
TABLE 1Implied Capital Requirements from Stress Test
(See Below for Details)
1. All ARMs, no interest rate risk
Cumulative defaults Recourse Capital requirementTest (%) (%) (%)
Base Case 6 Perfect —Credit Stress 1 23 None 4–5Credit Stress 2 23 75 2Credit Stress 3 23 50 3Credit Stress 4 23 50, no access 5
2. Interest rate riskCapital requirement
Test Rate changes Mortgage type (%)
Rate Stress 1 Up 10% for 2 years FRM 12Rate Stress 2 Up 10% for 2 years ARM —Rate Stress 3 10% 1 year, 2% loss with spread ARM 10Rate Stress 4 Permanent 10% Rate Capped ARM 12Rate Stress 5 Down 10% long-term debt FRM 11Rate Stress 6 Up 20% for 2 years lagged adj. ARM 9Rate Stress 7 Same as 6 w/Credit Stress 3 ARMs 10Rate Stress 8 Same but with credit Stress 4 ARMs 12
Description of tests—Base Case (see text)
Credit Stress 1 All ARMS. Defaults go from 1% per year to 4% and severity from 40 to 50%.Credit Stress 2 Same, but with recourse, 75% of banks make good on recourse. Agency takes
over loans on the 25% that fail.Credit Stress 3 Same but only 50% make good on recourse.Credit Stress 4 Same but no access to mortgages made by failed banks and severity 5 100%.Rate Stress 1 Low credit risk. Rates rise by 10% for 2 years then back to original levels.
Mortgages are fixed-rate financed with 1-year debt.Rate Stress 2 Same but mortgages are uncapped ARMs adjusted annually.Rate Stress 3 Rates increase 10% permanently. Mortgages are ARMS, but 1-year lag in adjust-
ment and agency borrowing spread over ARM index increases 2%.Rate Stress 4 Rates increase 10% permanently. Mortgages are U.S.-type ARMs with 2% annual
cap and 6% for life of loan.Rate Stress 5 Rates fall 10% permanently. Mortgages are fixed rate. Prepayments increase to
40% per year for 3 years. Financed with 5-year debt.Rate Stress 6 Rates up 20% permanently, but ARM rates lag, adjusting by 10% immediately
and 5% next year, and finish adjusting in the year after.Rate Stress 7 Same but with defaults as in Credit Stress 3 (50% recourse).Rate Stress 8 Same but with Credit Stress 4 (no access to mortgages).
Note. The results are relatively insensitive to dividend rates (especially if dividends cannot bepaid when there are losses) and taxes (especially if taxes cannot be negative). Raising default ratesby 50% (from 23 to 34%) raises the capital requirement to about 6% in Credit Stress 1 and 3% inCredit Stress 3. ARM results are insensitive to prepayment rates.
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rate risk (Rate Stress 2). However, lags and basis risk can lead to much highercapital requirements. For instance, Rate Stress 3 has rates go up by 10% with a1-year lag in the index and a 2% deterioration in the spread between the ARMrate and the bank’s borrowing rate. This raises the capital requirement to about10%. In Rate Stress 6 a 20% rise in rates over 2 years, a situation not unlikethat of the Russian crisis of 1998 but with no change in spreads, requires 9%capital. If only the spread deteriorates, the capital requirement is only 3%, butif it happens while Credit Stress 3 is happening, the requirement is 5 to 6%.Other simulations suggest that capital requirements for interest rate risk couldbe in the 10 to 15% range.
Not surprisingly, FRMs involve considerable interest rate risk. In Rate Stress1, with 10-year FRMs, the one-year rate increases for just 2 years before revertingto the initial level, but the capital requirement is 12%. Prepayment risk can alsobe important. In Rate Stress 5 the mortgages are 10-year FRMs funded with 5-year debt, but the mortgages are prepayable and the debt is not. In that case aninterest rate decline will be stressful, and in our simulation (with 40% prepaymentrates for 3 years) requires 11% capital.
V. CONCLUSIONS AND COMMENTS
Our conclusions are of two sorts. First, with respect to the risks of TUSRIF’sproposed program, and second, with the more general issue of how stress tests maybe helpful in evaluating the risks of mortgage lending in de nova environments.
On TUSRIF’s proposals, the results are, of course, preliminary exercises.Nevertheless, they suggest that stress tests can be used to get reasonable rangesof answers to questions about TUSRIF’s risks. Our major points are:
1. The sort of program envisioned by TUSRIF, with little or no interest raterisk and “recourse,” does not appear to be very risky and probably needs nomore than 4% capital, with the Basel 8% being high. However, to the extent therecourse is imperfect (e.g., the extent to which banks fail and/or there is notgood access to the collateral) capital requirements can change a great deal. Inan emerging financial system, such as Russia’s, these exogenous risks remainvery important considerations.
2. Adding interest rate risk can have a big effect, even with ARMs, if thereare lags, and basis risk. Given the instability in the markets for government debt(especially due to credit risk), basis risk is especially important for ARMs indexedto government rates (e.g., in 1998 the Russian Government defaulted on its debt),but funded with deposits or other short-term debt.
3. Stress tests can also be used to evaluate the ability of third parties, likethe banks, or local governments to be able to fulfill their obligations if theyaccept recourse.
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4. Within categories (Part 1 or 2 in Table I) capital requirements are relativelystable, but they can increase greatly as new categories of risk are entertained.
5. Stress tests need not be confined to disaster-like scenarios, but can be usedto simulate the firm’s experience through a wide range of situations. To the extentthat probabilities can be attached to the scenarios they can be used for estimatingexpected costs and ultimately for making pricing decisions.
On the broader issue of why stress tests may be useful, it is again helpful tocompare them to Angel and Mayo’s Housing Indicators Program (1993), and inparticular, Mayo’s perspective on why that kind of seemingly less rigorous datacollection could be helpful to policy-makers. His extensive involvement in verydetailed empirical work on housing demand, see inter alia Mayo (1981) andMalpezzi and Mayo (1987), demonstrated to his satisfaction that the wide differ-ences in housing market outcomes observed across countries could not be attribut-able to differences in demand. As a result, in his later work he focused increasinglyon tracing through the effects of supply conditions on outcomes, as he did inMalaysia in Hannah et al. (1989), and in Poland in Mayo and Stein (1995). Hewas particularly interested in identifying policies that were what he liked to term“supply inelasticizers,” that is, policies that were likely to have indirect effectswhich restricted or reduced the incentives of suppliers to be able to respond toprice changes. The indicators program became the vehicle through which hesought to build a data base of such systematic differences across countries.
Mortgage stress test models can be viewed in much the same light. They seekto determine whether specific capital requirements and loan terms are such thatthe supply of mortgage credit can be expected to be able to respond elasticallyand sustainably to shocks, shifts in demand, and third party agreements. Theyare a simple way to try to keep all the interactions that can affect mortgagetransactions explicit, and by so doing make the implications and risks clearer.
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