Transcript Risk Management Practice and Evolution

Presentation by
Bhaskar 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
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.
3
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 changed
Financial institutions kept failing regularly. Why?
YEAR OF FAILURE
1991
ORGANIZATION
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 Process
RMP
Identify Risk
Exposures
Measure and
Estimate
Risk Exposures
Assess Effects
of Exposures
Find Instruments and
Facilities to Shift
or Transfer Risks
Assess Costs and
Benefits of
Instruments
Form a Risk Mitigation Strategy:
• Avoid
• Transfer
• Mitigate
• Keep
Evaluate Performance ( RAROC)
Risk Mapping: Key risks faced by banks & financial institutions
T
Market risk
Liquidity risk
Credit risk
Risks
Operational risk
Legal and
Regulatory risk
Business risk
Strategic risk
Reputation risk
FR
Identifying Financial Risks – The Integration
Equity price risk
Market
risk
Financial
risks
Liquidity
risk
Interest-rate risk
Foreign exchange
risk
Commodity
price risk
Specific risk
Gap risk
Funding liquidity risk
Asset liquidity risk
Transaction risk
Credit
risk
Trading
risk
General
market risk
Portfolio/credit
concentration
Counterparty/
Borrower risk
Issuer risk
Issue risk
Operational
risks
Market Risk and trends in development
Everything 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 Modeling
Quantification 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.
From/To AAA
AA
A
BBB
BB
B
CCC/C D
0.48
0.09
0.06
0
0
0
0.60
0.05
0.11
0.02
0.01
0.44
0.17
0.03
0.04
0.08
0.20
0.29
1.03
1.28
AAA
91.67 7.69
AA
0.62
90.49 8.1
A
0.05
2.16
91.34 5.77
BBB
0.02
0.22
4.07
89.71 4.68
BB
0.04
0.08
0.36
5.78
83.37 8.05
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
Source: Standard & Poor’s, Annual Global Corporate Default Study, Jan. 26, 2004.
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 Analysis
Risk System Design
Reporting
Crisis prevention
Control Analysis
Treating problems
Treating symptoms
Crisis investigation
Compliance
Control Reviews
Risk Analysis,
Measurement,
Assessment
Integrated Risk
Management –
Risk Culture
Risk Appetite
Risk Policy
Risk culture
Common wavelength
Risk integrated with
business strategy
Risk adjusted returns
Economic Capital
KRI, predictability
Risk Monitoring
Risk disclosure
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.
 COMMUNICATE
COMMUNICATE
COMMUNICATE