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FRM 2017 Part 2

Book 3 – Operational and Integrated Risk Management

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SOUND MANAGEMENT OF OPS. RISK

Reading: Principles for the Sound Management of Operational Risk,” (Basel Committee on Banking Supervision

Publication, June 2011)

1. Definition:

a. Operational Risk: Risk of loss resulting from inadequate or failed internal process, systems or people or

from external events

b. Three lines of defense: Business management, independent risk management function, independent

internal audit function

ENTERPRISE RISK MANAGEMENT

Reading: Brian Nocco and René Stulz, “Enterprise Risk Management: Theory and Practice,” Journal of Applied

Corporate Finance 18, No. 4 (2006)

1. Definition:

a. ERM: Process of managing all of corporations risk within an integrated framework. Objective is to

optimize and not eliminate total risk by optimizing risk and return tradeoffs

2. Benefits of ERM:

a. Macro: Hedging diversifiable risks improves management’s ability to invest in value creating projects

b. Micro: Benefit is decentralizing risk management to ensure that each projects total risk is adequately

assessed by project planners during initial evaluation. Components include marginal risk assessment and

unit contribution to firm risk

3. ERM Framework:

a. Determine firms risk appetite

b. Estimate amount of capital needed to support desired level of risk

c. Determine optimal level of capital and risk that achieves the target rating

d. Decentralize the management of risk

4. Diversification benefits: Aggregation of market, credit and operational risks results in VaR which is less than the

sum of all VaR’s from each category

DEVELOPMENTS IN RISK APPETITE FRAMEWORK

Reading: Observations on Developments in Risk Appetite Frameworks and IT Infrastructure,” Senior Supervisors

Group, December 2010

1. RAF:

a. It represents the firms core risk strategy

b. It sets in place a clear, future oriented perspective of firms target profile in a number of scenarios

c. Specifies what risks a firm is willing to take and their limits

2. Risk reporting Requirements:

a. Specific Metrics (liquidity Ratios, Capital Adequacy, Risk Concentrations etc.)

b. Data Accuracy and Collection infrastructure

c. Regulatory limits

d. Supervisory expectations

INFORMATION RISK

Reading: Information Risk and Data Quality Management (Chapter 3, Anthony Tarantino and Deborah Cernauskas, Risk

Management in Finance: Six Sigma and Other Next Generation Techniques (Hoboken, NJ: John Wiley & Sons, 2009))

1. Impact of Poor Quality Data: Financial (Operating Costs), Confidence (Org. trust), Satisfaction (Customer),

Productivity (Workload), Risk (Credit Assessment), Compliance

2. Causes of Data Error: Data Entry errors, Missing Data, Duplicate records, Inconsistent Data, Nonstandard

formats, complex data transformations, Failed Identity Management process, Incorrect or misleading metadata

3. Key dimensions of Data Quality: Accuracy (Real to Model), Completeness, Consistency, Reasonableness

(expectations within operational context), Currency (Degree to which information is current), Uniqueness,

Semantic Consistency (Same meaning of data), Format Conformance and others.

4. Data Governance, Data Quality Inspection and Data Validation:

a. Data Governance: Operational data governance is the manifestation of the processes and protocols

necessary to ensure that an acceptable level of confidence in the data effectively satisfies the

organization’s business needs.

b. Data Validation: The data validation process reviews and measures conformance of data with a set of

defined business rules.

c. Data Inspection: Data inspection processes are instituted to measure and monitor compliance with data

quality rules. They are ongoing processes.

5. Data Quality Scorecard: Complex data quality metrics can be accumulated for reporting in a scorecard in one of

three different views: by issue, by business process, or by business impact.

a. Data Quality issues view: Evaluating the impacts of a specific data quality issue across multiple business

processes. Used for prioritizing tasks for diagnosis and remediation

b. Business Process view: Operational managers overseeing business processes may be interested in a

scorecard view by business process.

c. Business Impact view: Business impacts may have been incurred as a result of a number of different

data quality issues originating in a number of different business processes. This reporting scheme

displays the aggregation of business impacts rolled up from the different issues across different process

flows

OP RISK DATA AND GOVERNANCE

Reading: OpRisk Data and Governance (Marcelo G. Cruz, Gareth W. Peters, and Pavel V. Shevchenko, Fundamental aspects of Operational Risk and Insurance Analytics: A Handbook of Operational Risk (Hoboken, NJ: John Wiley & Sons)

1. Basel 2 Even driven Op Risk Classification:

Risk Event Explanation

Execution, Delivery &

process Management

What: Failed transaction processing, problems with counterparties and vendors.

Reasons: Human errors, miscommunication etc.

Clients, Products & Business

Practices

What: Disputes with clients/ counterparties, Regulatory fines for improper practices

Reasons: Human Errors

Business Disruption &

Business Failures

What: System crash

Reason: Systems

External Frauds What: Third party or outsiders attempting fraud against the firm

Internal Frauds What: Frauds committed or attempted by own employees

Employment Practices &

Workplace safety

What: Stringent labour and safety laws

Damage to Physical Assets What: Natural disasters or other events causing loss from external sources

2. Elements of Op Risk framework:

a. Internal Loss Data: All expenses associated with Operational loss except for opportunity costs, foregone

revenue and costs related to risk management to prevent future occurrence. Need to be collected for at

least 5 years as per Basel 2

i. Collection Threshold: Different threshold will result in different risk appetite

ii. Completeness of Database: Data from disparate sources needs to be complete, employees need

to send all data to a central database

iii. Recoveries and Near Misses: Gross loss to be considered, no recoveries to be considered.

However, reserves need to be included as losses

iv. Time for Resolution: Need a clear procedure to handle large and long duration losses (cases

which decide whose fault it was may take very long time for resolution)

3. Risk Control Self Assessment (RSCA)

a. Firms ask experts to conduct reviews on status of each process/ sub-process. Requires documentation

and assessment of risks

b. Universe of risks, mitigation measures and control are identified.

EXTERNAL LOSS DATA

Reading: External Loss Data (Chapter 8, Philippa X. Girling, Operational Risk Management: A Complete Guide to a Successful Operational Risk Framework (Hoboken: John Wiley & Sons, 2013))

1. External Loss databases:

a. Primary goal of external databases is to collect information on tail losses and examples of large risk

events

b. Databases are of two types:

i. Subscription based: IBM Algo FIRST which collects data from the press and publicly declared

events

ii. Consortium Data: Operational Riskdata eXchange Association (ORX) where member firms

upload anonymous data events

CAPITAL MODELING

Reading: Capital Modeling (Chapter 12, Philippa X. Girling, Operational Risk Management: A Complete Guide to a Successful Operational Risk Framework (Hoboken: John Wiley & Sons, 2013))

1. Operational Modeling:

a. Basic Indicator Approach: Operational Risk capital is based on 15% of the banks gross annual income. It

is calculated as KBIA = (∑ GIi× αn

i=1 )

n where GIi is gross income, α is 15% as set by Basel 2 and n is

number of years in which gross income was positive years

b. Standardized Approach: The approach uses separate factors for each of the business lines as against the

standard 15% used above. The weights and factors are as below

Business Line Weight Business Line Weight The risk capital is calculated

as: KTSA =

[∑ max(GIi×αi ,0)3 years ]

3

IB (Corp Finance) 18% Settlement & Payment 18%

IB (Trading and Sales) 18% Agency & Custody 15%

Retail banking 12% Asset Management 12%

Commercial Banking 15% Retail Brokerage 12%

c. Advanced Management Approach: banks are allowed to construct their own models for calculation of

risks. Following 3 conditions should be met:

i. Demonstrate ability to capture potential fat tails (99.9% confidence interval with 1 year interval)

ii. Include loss data, external data, scenario analysis and internal control factors

iii. Allocate capital that incentivizes good behavior

d. Loss Distribution Approach: It relies on internal data as the basis of design. Issues remain with the

amount of time for which data is available

2. Modeling:

a. Frequency: Poisson distribution is used which requires mean, variance and λ (avg. number of events in a

given year)

b. Severity Distribution: Generally lognormal distributions are used. But low frequency events may make

Gamma, Pareto and Weibull distributions better. Regulators are interested in goodness of fit

c. Monte Carlo simulations are then used to generate a loss distribution

STANDARDIZED MEASUREMENT APPROACH

Reading: Standardised Measurement Approach for operational risk—consultative document,” (Basel Committee on Banking Supervision Publication, March 2016)

1. Operational Modeling:

a. SMA (Standardized Measurement Approach): Consists of financial statement risk exposure (Business

Indicator or BI) and operational loss data specific for a bank

b. NIM (Net Interest Margin): net Interest income divided by interest earning assets

PARAMETRIC APPROACHES-EXTREME VALUE

Reading: Parametric Approaches (II): Extreme Value (Chapter 7, Kevin Dowd, Measuring Market Risk, 2nd Edition (West

Sussex, England: John Wiley & Sons, 2005))

1. Extreme Values: The challenge of analyzing and modelling extreme values is that there are only a few

observations for which to build a model and there are ranges of extreme values that are yet to occur.

a. Extreme value theory is a branch of statistics developed to address problems associated with extreme

outcomes

2. Fisher-Tippet: As sample size n gets large, the distribution of extremes converges to the following know GEV

(Generalized Extreme Value) distribution.

a. The symbol ξ is called the tail index and indicates the shape or heaviness of the tail

i. ξ > 0, tails are heavy as in t-distribution or Pareto Distribution

ii. ξ = 0, tails are light, as in Normal and Lognormal distributions

iii. ξ < 0, tails are lighter than Normal as in the Weibull Distribution

b. Risk Management focuses only on ξ > 0 and ξ = 0

c. We apply a statistical test for hypothesis ξ = 0 and if we cannot reject the hypothesis, then we assume

that ξ = 0

3. POT(Peaks over Threshold): It is an application of EVT to the distribution of excess losses over a high threshold

a. VaR = µ + β

ξቀ

n α

Nuቁ

−ξ− 1൨

i. Where β is scale parameter, ξ is tail index, α is significance level, µ is threshold (as %)

ii. n is total number of observations, Nu is no. of observations above threshold

b. ES = VaR+ β− µξ

1−ξ

4. Generalized Pareto Distribution (GPD): As µ gets large, the distribution converges into a GPD. The GPD exhibits

a curve that dips below the Normal Distribution prior to the tail and it then moves above the normal distribution

until it reaches the extreme tail

5. Issues:

a. Choosing a threshold requires a trade-off that it needs to be high enough for GPD to apply and low

enough so that there are sufficient observations

b. GEV requires one more variable (σ) which POT does not require. POT requires a threshold

c. EVT is used to measure extreme events and compute VaR. Both β and ξ can be computed using

maximum-likelihood techniques

VALIDATING RATING MODELS

Reading: Validating Rating Models (Chapter 5, Giacomo De Laurentis, Renato Maino, Luca Molteni, Developing, Validating and Using Internal Ratings (Hoboken, NJ: John Wiley & Sons, 2010))

1. Definitions:

a. Quantitative Validation: Comparing ex post(based on actuals) results of risk measures to ex ante(based

on forecasts) estimates, parameter calibrations, benchmarking and stress tests

b. Qualitative Validation: Issues pertaining to model development like logic, methodology, controls,

documentation and information technology

2. Elements of qualitative validation: obtaining PD, completeness of rating system, objectivity of rating system,

acceptance of rating system, consistency of rating system,

3. Elements of quantitative validation: sample representativeness, discriminatory power, dynamic properties,

calibration

MODEL RISK

Reading: Model Risk (Chapter 15, Michel Crouhy, Dan Galai and Robert Mark, The Essentials of Risk Management, 2nd Edition (New York: McGraw- Hill, 2014))

1. Model Risk Examples:

a. JP Morgan Chase(London Whale):

i. Prices of synthetic derivatives were reported in a favourable range instead of mid-price on a

daily basis to show more profits/ lower losses

ii. Different valuations for same products by Investment banking group and Synthetic Credit group

iii. To lower RWA, instead to trimming positions, Synthetic Credit group added more long positions

to offset short positions. This increased the RWA and risks

iv. Risk limit breaches in key metrics were reported but never acted upon

v. New model for VaR was adopted by Synthetic Credit group which grossly underreported VaR

limits as compared to banks model due to various formula errors and manual entries

b. LTCM:

i. Implemented a strategy based on the empirical fact that spreads between corporate bonds and

government bonds would converge

ii. This was true during normal market conditions but due to the financial crisis of 1998, the

spreads increased causing huge losses due to a leverage ratio of 25 to 1

iii. Most losses at LTCM were due to breakdown of correlation and volatility patterns observed in

the past

2. Model Assumptions/ Errors:

a. To assume that distribution of the underlying asset is stationary when in fact it changes over time

b. Oversimplification of models e,g, assuming rate of returns are normally distributed, volatility is constant

c. OTC products are illiquid and usually cannot be perfectly hedged

d. Same models applied to different situations

e. Incorrect inputs to a correct model

3. Mitigation of Model Risks

a. Independent vetting of process to establish how models are selected and construed

b. Invest in research to improve models

RISK-ADJUSTED PERFORMANCE MEASUREMENT

Reading: Risk Capital Attribution and Risk-Adjusted Performance Measurement (Chapter 17, Michel Crouhy, Dan Galai and Robert Mark, The Essentials of Risk Management, 2nd Edition (New York: McGraw-Hill, 2014))

1. Definitions:

a. Risk Capital: Provides protection against various risks inherent in the business

b. Economic Capital: Risk Capital plus strategic reserve

c. Regulatory Capital: Regulatory capital is mandatory capital required to be maintained as per laws

2. RAROC (Risk Adjusted Return on Capital)

a. Generic Equation: RAROC = Revenue−Costs−Expected Loss−Taxes+Return on Risk Capital ±Transfers

Economic capital

where economic capital is used as proxy for risk, Return on risk capital is return on the risk capital

allocated to the activity and is generally assumed that it is invested in government securities

b. RORAC (Return on Risk Adjusted Capital): RORAC = Profit or Loss

VaR

c. Sharpe Ratio: S = Expected Return –Risk free rate

Volatility

d. Adjusted RAROC: Adjusted RAROC = RAROC − βE(Rm − Rf) where βE is the beta of the equity

firm. The Adjusted RARCO takes into account the systemic risks of returns.

ECONOMIC CAPITAL FRAMEWORKS

Reading: Range of Practices and Issues in Economic Capital Frameworks,” (Basel Committee on Banking

Supervision Publication, March 2009)

1. Definitions:

a. Risk Aggregation: Involves making choices in aggregating certain types of risks.

2. Making Risks Comparable:

a. Risk Metric: Metrics used for quantification need to be sub-additive for aggregation

b. Confidence Interval: Loss distributions for different types of risk are different

c. Time Horizon: Risks measurements can have different time horizons

3. Aggregation Methodologies:

a. Simple Summation: Adding together individual capital components

b. Constant Diversification: Subtracts a fixed diversification percentage from the overall amount

c. Variance-Covariance Matrix: Summarizes interdependencies and provides framework for recognizing

diversification benefits

d. Copulas: Combines marginal distributions into a joint probability distribution

e. Full Modeling or Simulation: Simulate impact of risk drivers on all types of risks and construct a joint

distribution

LARGE BANK HOLDING COMPANIES

Reading: Capital Planning at Large Bank Holding Companies: Supervisory Expectations and Range of Current

Practice,” Board of Governors of the Federal Reserve System, August 2013

1. Capital Plan Rule: It is how Federal Reserve maintains interest in survivability and smooth functioning of

BHC(Bank Holding Company). The guidelines apply to all US domiciled BHC with assets equal to or greater than

$50 billion

a. Risk management foundation, Resource estimation methods, Loss estimation methods, Impact on

capital adequacy, Capital planning policy, Internal controls, Effective oversight are the 7 principles for

Capital Adequacy Process(CAP)

2. Practices for CAP:

a. Develop plans to effectively identify all risk exposures on firm wide basis

b. Establish a mechanism for review of all models used for Capital Adequacy

c. Develop a capital policy that defines principles and guidelines for capital goals and usage

d. Stress testing scenario must be based on risk factors faced by BHC

e. Attention should be paid to interrelationships between variables within a given scenario

REPURCHASE AGREEMENTS AND FINANCING

Reading: Repurchase Agreements and Financing (Chapter 12, Bruce Tuckman, Angel Serrat, Fixed Income

Securities: Tools for Today’s Markets, 3rd Edition (Hoboken, NJ: John Wiley & Sons, 2011))

1. Definitions:

a. Repo: One party sells a security with a commitment to buy it back at a future date at a higher price. The

difference between current price and future price is implied interest. Repo rates are always annualized

and are quoted in actual/360 format

b. Open Repos: Contracts that renew each day until cancelled

c. GC rate (General Collateral): Repo trades secured with general collateral (acceptable securities are

defined) are called GC rate

d. Federal Funds Rate: Interest rate that institutions charge each other for lending funds maintained at

Federal Reserve

2. Repo Trades: They can be secured with either general collateral or with specific collateral. Lenders in special

collateral repo trades receive a particular security as collateral and hence charge a lower rate than GC. The

collateral received can be used to finance purchase of a bond or finance.

3. Special Spread: Difference between GC and special rate is called a special spread. Spreads generally move within

0 to GC rate band

ESTIMATING LIQUIDITY RISKS

Reading: Estimating Liquidity Risks (Chapter 14, Kevin Dowd, Measuring Market Risk, 2nd Edition (West Sussex,

England: John Wiley & Sons, 2005))

1. Definitions:

a. Liquidity Risk: Degree to which a trader cannot trade a position without excess cost, risk or

inconvenience

b. Bid-Ask spread: It is the cost of liquidity. A wider spread indicates lower liquidity and higher risk

c. Exogenous Liquidity: refers to bid-ask spread not being affected by individual trades

d. Endogenous Liquidity: refers to when a trade can affect the bid-ask spread

2. Liquidity adjusted VaR:

a. Constant Approach Spread: It calculates LVaR assuming the bid-ask spread is constant. LC =

0.5×V×spread where Spread = ask−bid

(ask+bid)/2

the VaR is given as VaR = V×Zα×σ thus LVaR = VaR + LC

i. If the return distribution is Lognormal then, VaR = [1 − e(μ−(σ×Z))]

b. Exogenous Spread Approach: It replaces the Spread as μ + (σ×Z) in place of standard formula above

c. Endogenous Spread: We estimate elasticity as E =∆P/P

∆N/N and use this elasticity as LVaR = VaR× (1 −

E∆N

N)

3. Liquidity at Risk: It is also known as Cash Flow at Risk and is the maximum likely cash outflow over the horizon

period specified confidence level

ASSESSING QUALITY OF RISK MEASURES

Reading: Assessing the Quality of Risk Measures (Chapter 11, Allan Malz, Financial Risk Management: Models,

History, and Institutions (Hoboken, NJ: John Wiley & Sons, 2011))

1. Definitions:

a. Mapping: Assignment of risk factors to positions

2. Data Preparation: Crucial in risk measurement systems

a. Market Data: Time series data is used in forecasting the future portfolio returns

b. Security Master Data: Descriptive data on securities including maturity dates, currency etc.

c. Position Data: It matches the firms books and records

3. Major Defects in model assumptions of 2007-09:

a. Assumption of future house price appreciation

b. Assumption of low correlations

LIQUIDITY AND LEVERAGE

Reading: Liquidity and Leverage (Chapter 12, Allan Malz, Financial Risk Management: Models, History, and

Institutions (Hoboken, NJ: John Wiley & Sons, 2011))

1. Definitions:

a. Liquidity: An asset is liquid if it is close to cash i.e. it can be sold quickly, cheaply an without moving the

price too much

b. Transaction Liquidity Risk: Buying or selling an asset would result in adverse price movement

c. Funding liquidity or balance sheet risk: Borrowers credit position is perceived to be deteriorating.

Balance sheet risks happen when long term assets are funded using short term liabilities

d. Leverage Effect: ROE is higher as leverage increases as long as ROA exceeds cost of borrowing funds

e. Hurdle rate: A firms required ROE

f. Gross leverage: Value of all assets/ capital.

g. Net leverage: Difference between long and short positions/ capital

2. MMMF (Money Market Mutual Funds): High credit quality instruments with short maturities. They are not

mark to market daily.

3. ROE: RoE = (Leverage Ratio x ROA) – [(Leverage Ratio – 1) x Cost of Debt]

4. Transaction Liquidity:

a. Trade Processing Costs: Finding a counterparty, clearing costs, trading costs are trade costs

b. Inventory Management: Dealers provide trade immediacy. The dealer must be compensated for this

exposure

c. Adverse Selection: Differentiate between liquidity traders and information traders. Spreads are more for

information traders as they know more.

d. Differences of Opinion: Difficult to find a counterparty when all participants agree on the price

e. Transaction Cost: P x 0.5(s + α σ) where s = expected spread i.e. (ask price- bid price)/ mid-price, α is

value of confidence interval and σ is standard deviation

5. Market Liquidity:

a. Tightness refers to cost of round trip transaction including brokers commission

b. Depth: How large the order must be to move the price adversely

c. Resiliency: Length of time for large orders to make the adverse move in prices

STRESS TESTING BANKS

Reading: Stress Testing Banks,” Til Schuermann, prepared for the Committee on Capital Market Regulation,

Wharton Financial Institutions Center (April 2012)

1. Definitions:

a. Stress Testing: It stimulates financial results given various adverse scenarios

b. Macro-prudential stress tests: It focuses on systematic risks and the banking industry as a whole.

c. SCAP: Supervisory Capital Assessment Program, was the first macro-prudential stress test for banks in

US. It rested for same scenarios at all banks

d. CCAR: Comprehensive Capital Analysis and Review: It required banks to submit their own tress tests

together with SCAP stress test results. This provided micro issues at each bank

e. PPNR: Pre-provision net revenue, gains and losses on available for sale and held to maturity securities,

trading and counterparty losses for sic institutions with the largest trading portfolios

2. Challenges in designing a stress test:

a. The scenarios must be extreme but be reasonable and plausible

b. Not everything goes bad as once. If money is pulled out from one asset class, it must flow into another

c. Model Errors: Translation of macro factors like GDP, HPI(House Price Index) and Unemployment

employed in SCAP into actual models

d. Balance Sheet: A stress test is typically for 2 years. Balance sheet needs to be modeled for each quarter

assuming if assets will be sold or bought, prices etc.

BASEL 1, BASEL 2 AND SOLVENCY 2

Reading: Basel I, Basel II, and Solvency II (Chapter 15, John Hull, Risk Management and Financial Institutions, 4th

Edition (Hoboken, NJ: John Wiley & Sons, 2015))

1. Definitions:

a. Basel 1: Norms for Banks. Contains only credit risk. Introduced in 1988

b. Basel 2: Norms for banks. Contains market, credit and operational risk

c. Solvency 2: Norms for Insurance industry

d. Credit Equivalent Amount: Loan principal that is considered to have the same credit risk as that of the

off-balance sheet items

2. Basel 1:

a. Capital to total assets has to be greater than 5%

b. Banks on and off balance sheet items need to be used to calculate RWA(Risk Weighted Assets). Ratio of

capital to RWA must be more than 8% (Called Cooke Ratio)

c. Tier 1 includes Equity minus goodwill and Tier 2 includes Subordinated debt, preferred stock.

Requirement of at least 50% tier 1 capital

d. In 1996 amendment, market risk was also to be calculated at 99% confidence interval for a period of 10

days. This has to be back-tested for a period of 250 days

i. Market Risk: 12.5 x [max(VaRt-1, VaRavg x mc)+SRC]

ii. Credit Risk: RWA of on and off balance sheet items

iii. Mc is set depending on the number of exceptions found during back testing

3. Basel 2:

a. Three approaches to measure credit risk: Standardized Approach, Foundations-Internal Rating

Based(IRB) and Advanced-IRB approach

i. Standardized approach is similar to RWA approach under Basel 1 but with different weights as

per ratings rather than OECD status

ii. Foundations-IRB: Required Capital = 12.5 x [EADi x LGDi x (WCDRi-PDi) x MA]. Than bank provides

value for PD rest are provided by Basel

iii. Advanced-IRB: Banks supply their own estimates of EAD, LGD, PD and MA

b. Requires banks to maintain capital for Operational Risk

i. Basic Indicator Approach: [Net Interest Income + Other Income]x0.15

ii. Standardized Approach: Similar to basic indicator approach. Changes the multiplier for different

lines of business

iii. Advanced Measurement Approach: calculates risk at 99.9% confidence interval over a 1 year

time horizon

4. Solvency 2: Solvency 2 was to be implemented from 2013 but is now postponed. This is applicable in EU onluy

and is not a international standard

a. Standardized Approach: Similar to Standardized approach under Basel 1

b. Internal Models: Similar to IRB approach under Basel 2

BASEL 2.5, BASEL 3 AND OTHER CHANGES

Reading: Basel II.5, Basel III, and Other Post-Crisis Changes (Chapter 16, John Hull, Risk Management and

Financial Institutions, 4th Edition (Hoboken, NJ: John Wiley & Sons, 2015))

1. Basel 2.5:

a. Requires calculation of a stressed VaR for a period of 250 days where the bank’s portfolio performed

poorly. Banks may choose the time period.

b. Market Risk Charge: [max(VaRt-1, VaRavg x mc)+ max(SVaRt-1,SVaRavg x mc)] where avg. VaR is for past 60

days in 10 day periods

c. Incremental risk charge (IRC): A 99.9% VaR over a 1 year horizon for instruments on the trading book

which are sensitive to credit risk (Earlier, trading book had lower capital charge as compared to

investment book)

d. Comprehensive Risk Measure: Single capital charge for correlation dependent instruments that replaces

SRC and IRC.

2. Basel 3:

a. Tier 1 equity capital must be 4.5% of RWA at all times, Tier 1 Capital must be 6% at all times and total

capital must be 8% at all times

b. Capital conservation buffer consisting of Tier 1 equity capital equal to 2.5% of RWA is to be built.

c. A counter cyclical buffer of up to 2.5% of RWA as decided by each countries individual central banks

d. A minimum leverage ratio (capital/ total exposure) of 3%. LCR (Liquidity coverage ratio) > 100% where

LCR is high quality liquid assets/ net cash outflows for 30 days. NSFR (Net stable funding ratio) for 1 year

> 100% where NSFR is amount of stable funding/ required amount of stable funding

3. Contingent Convertible Bonds: They convert to equity under conditions of stress. E.g. Conversion is triggered if

Tier 1 equity capital falls below 7%; as issued by Credit Suisse in 2011

4. Dodd-Frank Act: Became law in 2010 in US

a. It established Financial Stability Oversight Council (FSOC) to look out for systemic risks

b. Establishment of Office of Financial Research (OFR) to conduct research on economy and risks in US

c. Identification of SIFI (Systematically Important Financial Institutions) . They are required to hold

additional capital

d. Requirement of central clearing houses for standardized OTC derivatives

FUNDAMENTAL REVIEW OF TRADING BOOK

Reading: Fundamental Review of Trading Book (Chapter 17, John Hull, Risk Management and Financial Institutions, 4th Edition (Hoboken, NJ: John Wiley & Sons, 2015))

1. Definitions:

a. Fundamental Review of Trading Book (FRTB): Basel committee proposed major revisions in May 2012 on

the way capital is calculated for trading book. This is called FRTB

b. Trading Book vs. Banking Book:

i. Trading book is what bank expects to trade and banking book consists of instruments expected

to be held till maturity.

ii. Instruments in the trading book are marked to market daily while in the banking book are not.

iii. Instruments in trading book are subject to market risk while instruments in banking book are

subject to credit risk

2. Changes proposed in FRTB:

a. Market Risk: In place of VaR at 99% confidence interval, ES (Expected Shortfall) with 97.5% confidence

interval is proposed. Distributions with heavy tails will have considerably higher ES

b. Liquidity Horizon: Earlier a 10-day VaR was used (1 day VaR calculated was multiplied by square root of

10), now it is proposed that five different liquid horizons be used of 10 days, 20 days, 60 days, 20 days

and 250 days.