risk management and indian banking: opportunities and challenges · risk management and indian...

Risk managementand Indian Banking:

Opportunities and ChallengesSusan Thomas

http://www.igidr.ac.in/˜susant

[email protected]

IGIDR

Bombay

Risk managementand Indian Banking:Opportunities and Challenges – p. 1

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

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

Background


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”.


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


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 creditrisk.

The minimum equity requirement was set through aminimum Capital Adequacy Ratio (CAR), attypically 8% of RWA.


What was right about the Basel Cap-ital Accord

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


What was wrong about the BaselCapital Accord

The calculation of RWA 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 tofind new ways of bearing risk.(Eg. higher exposure in interest rate risk, OTC derivatives.)

Ignored the problem of opacity - loans, OTCderivatives, OTC trading.

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


Negative consequences

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


Basel II

An attempt to move away from linear rules of thumb.

Some of the implementation involvesTrying 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.


What India should do with Basel-II

We should not repeat the mistakes about Basel-I –that of blindly adopting some externally supplied setof rules.

We should treat Basel-II as a set of interesting ideas,and craft a new framework of banking regulationbased on genuine understanding of risk.


Improving Indian banking regula-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, not

loans; exchange traded, not OTC derivatives.

These require sound market design.

Importance of market discipline; models of bank failure

probability based on the stock price.

Importance of correct pricing of deposit insurance.


Progress in India oninterest rate risk modelling


Importance of fixed income risk

There is some evidence that banks in Indiasubstituted credit risk by interest rate risk when RBIlay down a common risk management frameworkfor the Indian banking sector based on Basel-I.

Patnaik & Shah (2002): Roughly two-thirds ofbanks in India would lose more than 25% of equitycapital when faced with a 99% shock.

Existing rules about interest rate risk regulation arewrong: i.e. 2.5% risk weightage, and IFR.


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 exceededtomorrow with a 1% probability.


Fixed income VaR

faces serious hurdles

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.

Reprice the full portfolio at each of these draws.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.


Fixed income VaR




Reprice the full portfolio at each of these draws.

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



Fixed income VaR






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.


Fixed income VaRfaces serious hurdles






This is easy.



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 is easy.






This is easy.


Difficulties in testing

VaR methodologies must be backed by testing.

Banking applications require VaR over longhorizons.

Here the tests of VaR are particularly weak.

Data in India is weak.

We should be careful in knowing what we do notknow.


Fixed income derivatives

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

Jayanth Varma’s Risk Management Committee hasworked on models for computing VaR on a one-dayhorizon.(The committee report is on the SEBI website.)

This is fundamentally easier, since we seek only aone-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.

RBI’s guidelines for OTC interest rate derivatives(1999) are not based on analysis of risk.


Progress in India oncredit risk modelling


Credit risk requires three steps

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

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

The above two give how much risk premium tocharge for a loan.This has logic like the CAPM - betas, systematicrisk.

How do we put the pieces together to think aboutportfolio VaR?What you can’t diversify has to be priced over andabove simple risk-neutral reasoning.


Situation in India

Problem What we know

Failure of a bond Quite a bitLoss given default Little.Portfolio credit risk Very little.


Progress on modeling failure of onefirm

Requirement

How we get there

1. Underlying firm-level ac-

counting database

CMIE - available in 1989.

2. Defaults database


3. Model predicting default

CMIE Credit Model.

4. Working through stock

market data (Merton

model, KMV model)

Shah & Thomas, 2000; Thomas, Shah

& Karandikar (2002).



Requirement How we get there


counting database





CMIE Credit Model.


market data (Merton

model, KMV model)







counting databaseCMIE - available in 1989.




CMIE Credit Model.


market data (Merton

model, KMV model)








2. Defaults databaseCMIE - available in 2002.


CMIE Credit Model.


market data (Merton

model, KMV model)








2. Defaults databaseCMIE - available in 2002.

3. Model predicting defaultCMIE Credit Model.


market data (Merton

model, KMV model)




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 1Altman Z-Score 2Model with no prediction capabilityModel with perfect prediction capability


From Thomas, Shah & Karandikar,2002:

-200 0 200

Number of days

1.0

1.5

2.0

Avg

. DfD

DowngradesUpgrades Reaffirmations


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