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Risk managementand Indian Banking:

Opportunities and Challenges

Susan Thomas

http://www.igidr.ac.in/susant

[email protected]

IGIDR

Bombay

Risk managementand Indian Banking:Opportunities and Challenges p.
http://www.igidr.ac.in/~susant/http://www.igidr.ac.in/~susanthttp://www.igidr.ac.in/~susanthttp://www.igidr.ac.in/~susant/


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Background



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The problem

Banking is risky business!

1. Very high leverage:

India: Food 1.1, Machinery 0.6, Automobiles 1.1, Auto

Ancillaries 1.19, Banking 17.6.

US: Manufacturing 0.25-0.35, Utilities 1.4-1.5, Trade 0.3-0.4,

Banking 15.

2. Opaque assets: Loans, OTC derivatives. Non-transparent OTC

trading.

3. Moral hazard: Deposit insurance.

Nobel Laureate Merton Miller: Banking is a disasterprone 19th century industry.



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The goals of Basel norms

In the late eighties, there was a lot of cross-borderlending particularly by the Japanese banks.

Japanese banks grew enormously and gatheredmarket share; Western banks complained aboutJapanese banks being regulated badly.

Basel I was an attempt to standardise the regulationgoverning the global banking industry.

The heart of the Basel I norms defined minimum

required equity capital, i.e. an attempt to containleverage.



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Basel I, contd.

Required equity capital was a single numbercalculated as a fraction of the risk weighted assets

(RWA).RWA = w1x1 + w2x2 + . . ., where x1 was corporateexposure, and w1 = 1.

The weights for all the other classes of assets was setat less than 1.

The main focus appeared to be on addressing credit

risk.The minimum equity requirement was set through aminimum Capital Adequacy Ratio (CAR), at

typically 8% of RWA.


t t t t


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at was r g t a out t e ase ap-ital Accord

The CAR requirement did reduce the extremely highlevels of leverage in the banking industry.



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w wr n uCapital Accord

The calculation ofRWA is incorrect.Risks in the banking portfolio are not linear.

Assets were classified on very broad lines.(Eg. OECD government bonds.)

The focus on credit risk gave banks incentives to

find new ways of bearing risk.(Eg. higher exposure in interest rate risk, OTC derivatives.)

Ignored the problem of opacity - loans, OTC

derivatives, OTC trading.

Ignored differences between countries.(If 8% works for the OECD, what is correct for India?)



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Negative consequences

Even though these were broad recommendations,they became rigid in the hands of weak banking

regulators.This became especially problematic countries wherethe regulatory framework was not strong enough todevelop their own risk management rules.

The focus shifted from taking risks with a clearunderstanding of the returns, to blindly using BISrules.



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Basel II

An attempt to move away from linear rules of thumb.

Some of the implementation involves

Trying to improve upon the linear formula.

Reliance on credit ratings.

Exploit internal models of risk measurement in banks.Taking more interest in incentives - of banks, of securities

markets.

This is still playing the game of Basel I - but trying tofind a better formula for equity capital.



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What India should do with Basel-II

We should not repeat the mistakes about Basel-I that of blindly adopting some externally supplied set

of rules.We should treat Basel-II as a set of interesting ideas,and craft a new framework of banking regulation

based on genuine understanding of risk.

Risk managementand Indian Banking:Opportunities and Challenges p. 1

mprov ng n an an ng regu a


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mprov ng n an an ng regu a-tion

Need to develop models for interest rate VaR.

Need to develop models of credit risk.

Banks must be given incentives to create such models

internally. One proposal:

The bank must present their internal risk models to the

regulator, and if they are good enough, be used for the

calculation of CAR.

Need to move towards more transparent assets bonds, notloans; exchange traded, not OTC derivatives.

These require sound market design.

Importance of market discipline; models of bank failure

probability based on the stock price.Risk managementand Indian Banking:Opportunities and Challenges p. 1


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Progress in India oninterest rate risk modelling



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Importance of fixed income risk

There is some evidence that banks in Indiasubstituted credit risk by interest rate risk when RBI

lay down a common risk management frameworkfor the Indian banking sector based on Basel-I.

Patnaik & Shah (2002): Roughly two-thirds of

banks in India would lose more than 25% of equitycapital when faced with a 99% shock.

Existing rules about interest rate risk regulation are

wrong: i.e. 2.5% risk weightage, and IFR.



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Fixed income VaR

The yield curve fluctuates - this generates price riskfor every fixed income portfolio.

We seek statements like VaR for the portfolio at a99% level on a one-day horizon.

This is the rupee loss which will be exceeded

tomorrow with a 1% probability.


xe ncome a


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xe ncome a

Make a model about fluctuations of the yield curve.Simulate 10,000 draws from it.


xe ncome a


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xe ncome a


Reprice the full portfolio at each of these draws.


xe ncome a


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xe ncome a



So we get 10,000 outcomes for the profit/loss on theportfolio on a one-day horizon.Read off the 100th worst loss after sorting these10,000 numbers.


xe ncome a


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xe ncome afaces serious hurdles





xe ncome a


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This requires a good model telling us how the entireyield curve fluctuates.




xe ncome a


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This requires a good model telling us how the entireyield curve fluctuates.


This requires a sound pricing technology wherebythe impact of alternative yield curves is clearlyknown.



xe ncome a


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Make a model about fluctuations of the yield curve.Simulate 10,000 draws from it.This requires a good model telling us how the entireyield curve fluctuates.


This requires a sound pricing technology wherebythe impact of alternative yield curves is clearlyknown.

So we get 10,000 outcomes for the profit/loss on theportfolio on a one-day horizon.Read off the 100th worst loss after sorting these10,000 numbers.This is easy.



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Difficulties in testing

VaR methodologies must be backed by testing.

Banking applications require VaR over long

horizons.Here the tests of VaR are particularly weak.

Data in India is weak.

We should be careful in knowing what we do notknow.



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Fixed income derivatives

So far we have only talked about bonds.What about interest rate futures, interest rateoptions?

Jayanth Varmas Risk Management Committee hasworked on models for computing VaR on a one-day

horizon.(The committee report is on the SEBI website.)

This is fundamentally easier, since we seek only a

one-day horizon, not a one-year horizon.Committee observes lack of scientific knowledge,but is confident about conservative approximations.

This work will drive collateral requirements forinterest rate futures and options.



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Progress in India oncredit risk modelling



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Credit risk requires three steps

What is the failure probability of a bond?This is about predicting default.


C di i k i h


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What is the loss given default?This is about creditors rights.


C di i k i h


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The above two give how much risk premium to

charge for a loan.This has logic like the CAPM - betas, systematicrisk.


C dit i k i th t


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The above two give how much risk premium to

charge for a loan.This has logic like the CAPM - betas, systematicrisk.

How do we put the pieces together to think aboutportfolio VaR?What you cant diversify has to be priced over and

above simple risk-neutral reasoning.Risk managementand Indian Banking:Opportunities and Challenges p. 1


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Situation in India

Problem What we know

Failure of a bond Quite a bit

Loss given default Little.

Portfolio credit risk Very little.


rogress on mo e ng a ure o one


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g gfirm

Requirement

1. Underlying fi rm-level ac-

counting database

2. Defaults database

3. Model predicting default

4. Working through stock

market data (Merton

model, KMV model)


rogress on mo e ng a ure o one


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g gfirm

Requirement How we get there

1. Underlying fi rm-level ac-counting database



4. Working through stockmarket data (Merton

model, KMV model)


rogress on mo e ng a ure o onefi


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g gfirm



CMIE - available in 1989.




model, KMV model)




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g gfirm





CMIE - available in 2002.3. Model predicting default


model, KMV model)




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firm






CMIE Credit Model.


market data (Merton

model, KMV model)




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firm






CMIE Credit Model.


market data (Merton

model, KMV model)

Shah & Thomas, 2000; Thomas, Sha

& Karandikar (2002).


F th CMIE C dit M d l


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From the CMIE Credit Model:

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

CMIE Credit ModelMoodys

Altman Z-Score 1

Altman Z-Score 2

Model with no prediction capability

Model with perfect prediction capability


rom omas, a aran ar,2002


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2002:

-200 0 200

Number of days

1.0

1.5

2.0

Av

g.

DfD

Downgrades

Upgrades

Reaffirmations



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Further reading


VaR


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VaR

SUSAN THOMAS and AJAY SHAH. Risk and theIndian economy. In: India Development Report1999-2000, (editor) KIRIT S. PARIKH, chapter 16,pages 231242. Oxford University Press, 1999

AJAY SHAH and SUSAN THOMAS. Rethinkingprudential regulation. In: India DevelopmentReport 1999-2000, (editor) KIRIT S. PARIKH,chapter 17, pages 243255. Oxford University Press,1999

MANDIRA SARMA, SUSAN THOMAS, and AJAYSHAH. Selection of Value at Risk models. Journalof Forecasting, 22(4):pages 337358 (2003)


Recent work on fixed income VaR


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Recent work on fixed income VaR

ILA PATNAIK and AJAY SHAH. Interest-rate risk inthe Indian banking system. Technical report,ICRIER, New Delhi (December 2002)

Jayanth Varmas committee report on interest ratederivatives - came up on SEBI website on 19/3/2003.

Gangadhar Darbha has done work on extreme valuetheory for interest rate VaR.


Credit risk


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Credit risk

CMIE Prowess manuals.

SUBRATA SARKAR and SUSAN THOMAS.

Assessing default probabilities using accountingdata: a case of firms in india. Technical report,IGIDR (2003)

AJAY SHAH and SUSAN THOMAS. Systemicfragility in Indian banking: Harnessing informationfrom the equity market. Technical report, IGIDR,

Bombay, India (December 2000)SUSAN THOMAS, AJAY SHAH, and RAJEEVA L.KARANDIKAR. Does the stock market get it before

the rating agencies? Some evidence on the Mertonmodel. Technical report, IGIDR and ISI, DelhiRisk managementand Indian Banking:Opportunities and Challenges p.

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