Integrated Risk Management in Financial Institutions

Presentation byBhaskar Majumdar, MS (Econ, Econometrics) DSE, ACIB (London)

Head of Risk Management The Industrial Bank of Kuwait

The changing role of risk managers

First he sat in the back seat and then he had his foot on the brake, now he’s got one hand on the steering wheel! Is there no end to the risk manager’s advancement into every aspect of risk-taking in a financial firm? Next he’ll be right there in the driving seat, with traders, salesmen, corporate financiers and chief financial officers doing his bidding. So, is the risk manager turning into something else?

Euromoney, February 1998

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Risk Management in Financial Institutions

Opportunities and Risks abound in these financial supermarkets.

We will see how these risks have been identified, mapped, quantified, embraced, mitigated, and managed.

Banks and financials institutions are heavily regulated and meet common international standards (sometimes with unintended consequences).

Despite failures and hiccups, it has reached a level of maturity in a very dynamic environment that may be illustrative to other industries.

The Risk Journey – Early Stage Early perception that the only risk worth considering was credit risk.

Though risk was intuitively understood, in a relatively stable world, risk was to be avoided, not embraced.

That individual human judgment was supreme and that judgment led to binary yes/no, good/bad decisions. That judgment was necessarily based on historical data linearly projected forward and the past was the only guide to the future.

A formal risk management function was considered superfluous as good audit departments did the job anyway. No one wanted another ‘control’ function.

To the extent that ‘group credit’ as an independent unit existed, some tension between the credit selling units and independent credit assessment was evident and more of such tension was considered avoidable.

We are the ‘Experts’ – what do you know? Don’t try to lecture us. When risk management as a function was formalized, initially ‘imposed’ by the central banks around the world, they faced an attack on their credibility.

The status quo was questioned as the financial world changedFinancial institutions kept failing regularly. Why?

YEAR OF FAILURE ORGANIZATION

1991 BCCI

1992 Credit Lyonnais

1992 Metallgesellschaft

1995 Barings Bank

1995 Daiwa Bank

1996 Sumitomo Corp.

1998 Long Term Capital Management

2008 Bear Sterns, Lehman Bros, WAMU

2009 + + + Northern Rock, Merryl Lynch + + +

Major scandals – Enron, WorldCom, Parmalat and more……..Major events – Black Monday US:23% crash in one day, Russian default, Asian

Crisis……

The theoretical roots of modern risk management

The theoretical base of modern risk management lies in Financial Economics, Statistics/Probability, Mathematical Economics and Econometrics.

Harry Markowitz – The Theory of Portfolio Selection ( University of Chicago 1952) – Nobel Prize in Economics, 1990.

William Sharpe (Stanford)and John Lintner (Harvard) 1960s– Capital Asset Pricing Model. Sharpe was given the Nobel Prize in Economics, 1990. Lintner had passed away.

Fischer Black and Myron Scholes (University of Chicago) and Robert Merton (MIT) (1973) - The Pricing of Options and Corporate Liabilities, Theory of Rational Option Pricing. Merton and Scholes received the Nobel Prize in Economics 1998. (Black had passed away in 1995).

Franco Modigliani (MIT)and Merton Miller (Carnegie Mellon) in 1958 -- The Cost of Capital, Corporation Finance, and the Theory of Investment. Both awarded the Nobel Prize in Economics in 1985.

Kenneth Arrow (Stanford) & Gerard Debreu ( University of Chicago). - Existence of an Equilibrium for a Competitive Economy. Quantifiable risk. Kenneth Arrow, Nobel prize in Economics 1972. Gerard Debreu, Nobel prize in Economics , 1983.

Needs of a dynamic and changing word

Demise of fixed rate systems, beyond Breton Woods -1971.

Deregulation, disintermediation , globalization and highly interconnected financial markets.

Financial Innovation: Large volumes of transactions in ‘synthetic instruments’ - derivatives and other structured products that are complex.

Derivatives are used to hedge ( transfer of risk), arbitrage and speculate. In 2005 the total outstanding notional positions in the derivative markets was US$ 343 trillion ($ 343,000 Billion) compared to US GDP of $ 12 Trillion (2005). 2011 EU $ 15 T, USA $ 16T, China $ 7 T, UK $ 2.4 T, India $ 1.8 T, Russia $ 1.8T.

Rapid rise of technology and speed of transactions. Program trading. Abilities to control lag.

Increased variety and complexities of banking business.

The need for quantification leads to financial modeling based on statistics.

Structured products that hover in between credit risk and market risk methodologies.

Banking regulation – The push from Basel – from gentle to extreme. Basel I, Basel II. Basel III.

The Risk Management ProcessRMP

Identify RiskExposures

Measure and EstimateRisk Exposures

Find Instruments andFacilities to Shiftor Transfer Risks

Assess Effectsof Exposures

Assess Costs andBenefits of Instruments

Form a Risk Mitigation Strategy:• Avoid• Transfer• Mitigate• Keep

Evaluate Performance ( RAROC)

Risk Mapping: Key risks faced by banks & financial institutions

TMarket risk

Liquidity risk

Credit risk

Operational risk

Legal and Regulatory risk

Business risk

Strategic risk

Reputation risk

Risks

Identifying Financial Risks – The IntegrationFR

Financialrisks

Market risk

Credit risk

Foreign exchange risk

Commodityprice risk

Interest-rate risk

Equity price risk

Transaction risk

Portfolio/creditconcentration

Trading risk

Gap risk

Generalmarket riskSpecific

risk

Counterparty/Borrower risk

Issuer risk

Issue risk

Liquidity risk

Funding liquidity risk

Asset liquidity risk

Operational risks

Market Risk and trends in developmentEverything floats !!

Market risk is the risk that changes in market prices and rates will reduce the value of a security or a portfolio.

FX, Interest rates, Equities, Bond prices, Commodity prices --- All Float.

Transaction cover. Arbitrage. Speculation (Proprietary trading).

In trading activities, risk arises both from open (unhedged) positions and from imperfect correlations between market positions that are intended to offset one another (basis risk).

Financial instruments like Futures, Options, FRAs, Interest rate Swaps and FX Swaps and many others are used to hedge (cover risk), arbitrage or speculate on market movements. Risk arises when the mandate to cover or hedge is overridden by un-mandated speculation.

Market Risk Assessment & Control - Toolkit

Portfolio construction and diversification.

Use of Synthetic instruments. Illustration of risk : Middle East case study

Value at Risk (VaR) and VaR Limits.

Common relationships for risk assessment – The Greeks: Beta, Delta, Duration, Convexity, Vega, Rho, Theta

Risk Adjusted Returns and assessment of investment portfolio performance: Sharpe ratio, Treynor ratio, Information ratio, Sortino ratio, Jensen’s Alpha, etc.

Risk modeling: Use of simulation techniques – Monte Carlo simulation. Not limited to historical data.

Risk modeling: Volatility estimates using methods like GARCH (generalized autoregressive conditional heteroscedasticity)

Position taking and Trading limits:

Operational Risk Control – Treasury Mid Office – independent monitoring and reporting.

Operational Risk Control : Segregation between front, middle and back office

Benefits and Dangers of Quantification and Risk ModelingQuantification has major benefits.

It takes one away from subjective judgment.

Can make comparisons more precise.

Helps in stress testing by varying parameters

Can quantify required risk buffers , regulatory and economic capital

Inappropriate quantification has its dangers and we must be knowledgeable and careful

Inappropriate application can exaggerate risks

Use of linear relationships when financial markets are highly nonlinear

Use of normal curves when relationships are skewed and there are tail risks

Volatilities are assumed constant when they are time varying

Using measures without knowing limitations

Eg Value at Risk. It measures the maximum loss at a certain confidence level over a certain period of time. P * Z * σ * √ T. It is essentially silent about risks in the tail beyond the confidence interval.

Credit Risk and its Evolution

Credit risk arises from the potential that an obligor is either unwilling to perform on an obligation or its ability to perform such obligation is impaired resulting in economic loss to the bank.

Because of the need to measure and quantify risk and to compare one risk from another the assessment of credit risk has moved forward from the good/bad binary judgment of the traditional credit analyst to more sophisticated credit modeling, supported by the roadmap of Basel II/III.

Credit Risk Assessment - Evolution

Traditional Credit Analysis:

Every lending banker is trained in analyzing a company, industry and sector to determine the factors financial and non financial that makes a company strong enough to pay its dues on time.

Binary decision – Acceptable/ Non acceptable. It is highly subjective and in general, mathematically non-comparable, even if a subjective rating is given.

Credit decision depends on the experience and judgment of the analyst.

Based on a limited database .

While useful at the company analysis level, this is not well suited to credit portfolio analysis, cannot be used for capital calculations or stress tests.

Credit Risk Assessment - Evolution

Early Credit Scoring for Companies:

Subjective factor score: Each factor may have a score between 0-10. The score within this range is based on the subjective judgment of the credit analyst.

Subjective weighting: Someone may be decide that 60% belong to factors in the financial category, while 40% belongs to the non-financial factors. Is it 70:30, or 80: 20 or 90:10? Depending on choice, end result could be statistically unpredictable.

The factors and scores makes it look scientific, when in reality it is not, has little statistical validity, though the presentation of all the factors and individually judged scores, is nice to observe and is an improvement on the traditional credit methodology.

Traditional credit analysts love it !

Credit Risk Transition and the use of Probabilities

Using transition matrices, we can see how different rating categories have changed over time. This table is based on S&P’s experience from 1981 to 2004; shows empirical results for the migration from one risk category to all other credit-risk categories within one year.

Source: Standard & Poor’s, Annual Global Corporate Default Study, Jan. 26, 2004.

From/To AAA

AA A BBB

BB B CCC/C D

AAA 91.67 7.69 0.48 0.09 0.06 0 0 0

AA 0.62 90.49 8.1 0.60 0.05 0.11 0.02 0.01

A 0.05 2.16 91.34 5.77 0.44 0.17 0.03 0.04

BBB 0.02 0.22 4.07 89.71 4.68 0.08

0.20 0.29

BB 0.04 0.08 0.36 5.78 83.37 8.05 1.03 1.28

B 0 0.07 0.22 0.32 5.84 82.52 4.78 6.24

CCC/C 0.09

0 0.36 0.45 1.52 11.17 54.07 32.35

Credit Risk Assessment - Evolution

Credit Modeling using statistical techniques and simulation:

Sophisticated modern developments where credit factors are modeled using very large proprietary databases that are not available to single banks.

Financial and statistical methodology is combined to provide forward probability estimates.

Applicable to portfolios with large numbers of credit instruments,

Backtesting is easier as it can be done immediately against a very large database. Probabilities of default are more precisely available and the models are fully statistically valid at high level of significance 95%+ and as required under Basel Advanced.

Expensive to implement and are feasible only in very large international banks .

Requires model calibration for each bank by experts.

Credit Risk Assessment - Evolution

Statistical Modeling using principal component analysis:

This is a modern non-parametric method that makes no assumptions on statistical distributions such as the normal distribution and also does not assume linearity.

Recognizes that financial markets are nonlinear, can be discreet with many breaks in patterns and trends. A significant part of credit is explained by the financial ratios.

Critical ratios that principally contribute to the probability of default are captured and the probability of default is mapped to the Internal rating scale.

It validates the credit judgment approach.

Can be used for capital calculations based on Basel IRB and ICAAP stress tests.

Appropriate for medium/smaller banks.

The Evolution of Risk Management

1. Band Aid 2. Reactive 3. Proactive 4. Integrated

Approach

Cause AnalysisRisk System DesignReporting

Risk AppetiteCrisis prevention Risk PolicyControl Analysis Risk cultureTreating problems Common wavelength

Risk integrated withbusiness strategy

Treating symptoms Risk adjusted returns Crisis investigation Economic Capital

KRI, predictability Risk Monitoring

Risk disclosure

Compliance

Control Reviews

Risk Analysis, Measurement,Assessment

Integrated Risk

Management – Risk Culture

Organizational Risk Appetite

Approach to the assessment of risk appetite

Integrated Risk Management

Role of the Chief Risk Officer

Ensure people who know the business intimately , are in the risk team.

Make business people see the light and communicate risks that may be ignored in part or whole and to reach a balance between risk and reward.

Apply breaks in overheated markets and prevent reckless behavior especially when herd mentality is prevalent.

Independence, factual evidence, calm logic, latest risk management methods.

Providing the overall leadership, vision and direction for organizational risk management across the bank.

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