copyright 2001 a. s. cebenoyan1 b40.3312 policymaking in financial institutions professor a. sinan...

Copyright 2001 A. S. Cebenoyan 1

B40.3312 Policymaking in Financial Institutions

Professor A. Sinan Cebenoyan

NYU-Stern-Finance


Market Risk

• Market Risk (Value at Risk, VAR): dollar exposure amount (uncertainty in earnings) resulting from changes in market conditions such as the price of an asset, interest rates, market volatility, and market liquidity.

• The five reasons for market risk management:– Management information (senior management sees exposure)

– Setting Limits(limits per trader)

– Resource Allocation (identify greatest potential returns per risk)

– Performance Evaluation (return-risk per trader Bonus)- Regulation (provide private sector benchmarks)


JPM’s RiskMetrics Model• Large commercial banks, investment banks, insurance

companies, and mutual funds have all developed market risk models (internal models). Three major approaches to these internal models:– JPM Riskmetrics– Historic or back-simulation– Monte Carlo simulation

• We focus on JPM Riskmetrics to measure the market risk exposure on a daily basis for a major FI.

• How much the FI can potentially lose should market conditions move adversely:

Market Risk = Estimated potential loss under adverse

circumstances


Daily earnings at risk= ($ market value of position) x (Price volatility)where,Price volatility = (Price Sensitivity) x (Adverse daily yield move)

We next look at how JPM Riskmetrics model calculates DEaR in three trading areas: Fixed income, Foreign exchange, and Equities,and how the aggregate risk is estimated.

Market Risk of Fixed Income SecuritiesSuppose FI has a $1 million market value position in 7-yr zero coupons with a facevalue of $1,631,483.00 and current annual yield is 7.243 .

Daily Price volatility = )()(1

RMDRR

D

P

dP

527.6)07243.1(

7

1

R

DMDThe modified duration =

for this bond


If we make the (strong and unrealistic) assumption of normality in yield changes, and we wish to focus on bad outcomes, i.e., not justany change in yields, BUT an increase in yields that will only bepossible with a probability, i.e., a yield increase that has a chanceof 5%, or 10%, or 1%…(We decide how likely an increase we wishto be worried about). Suppose we pick 5 %, i.e., there is 1 in 20chance that the next day’s yield change will exceed this adverse move.If we can fit a normal distribution to recent yield changes and get a mean of 0 and standard deviation of 10 basis points (0.001), and we remember that 90% of the areaunder the normal distribution is found within +/- 1.65 standard deviations, thenwe are looking at 1.65 as 16.5 basis points. Our adverse yield move.

Price Volatility = -MD (R) = (-6.527) (.00165) = -.01077DEaR = DEAR = ($ market value of position) (Price Volatility)

= ($1,000,000) (.01077) dropping the minus sign = $10,770 The potential daily loss with 5% chance

For multiple N days, DEAR should be treated like , and VAR computed as:NDEARVAR


Foreign Exchange

Suppose FI has SWF 1.6 million trading position in spot Swiss francs. What is the

DEAR from this?

•First calculate the $ amount of the position

•$ amount of position = (FX position) x ($/SWF)

= (SWF 1.6million) x (.625) = $ 1 million

If the standard deviation () in the recent past was 56.5 basis points, AND

we are interested in adverse moves that will not be exceeded more than 5%

of the time, or 1.65:

FX volatility = 1.65(56.5) 93.2 basis points THUS,

DEAR = ($ amount of position) x (FX volatility)

= ( $1million) x (.00932)

= $9,320


Equities

Remember your CAPM:

Total Risk = Systematic risk + Unsystematic risk

2222

eitmtitit

If the FI’s trading portfolio is well diversified, then its beta will be close to 1, and

the unsystematic risk will be diversified away….leaving behind the market risk.

Suppose the FI holds $1million in stocks that reflect a US market index, Then

DEAR = ($ value of position) x (Stock market return volatility)

= ($1,000,000) (1.65 m)

If the standard deviation of daily stock returns on the market in the recent past

was 2 percent, then 1.65( m)= 3.3 percent

DEAR = ($1,000,000) (0.033) = $33,000


Portfolio Aggregation

We need to figure out the aggregate DEAR, summing up won’t do, REMEMBER:

22)}](){([ yxEyxE

yx

22

))}](())({([ yEyxExEyx

))}(())(({2

))(())(( 222

yEyExExE

yEyExExEyx

yxxyyxyx 2

222

If the correlations between the 3 assets are:

Bond SWF/$ US Stock Index

Bond -.2 .4

SWF/$ .1


Then the risk of the whole portfolio, DEAR treated like , will be2/1

.

.

.2

22

2

2

2

DEARDEARDEARDEARDEARDEARDEAR

DEARDEAR

swf

usswfusus

busbswf

bswfbus

swfb

portfolioDEAR

Substituting the values we have:2/1222

)33)(32.9)(1(.2)33)(77.10)(4(.2

)32.9)(77.10)(2.(2)33()32.9()77.10(

portfolioDEAR = $39,969


•BIS Standardized Framework for Market Risk

•Applicable to smaller banks.

•Fixed Income

•Specific Risk charge (for liquidity or credit risk quality)

•General Market Risk charge

•Vertical and horizontal offsets

•Foreign Exchange

•Shorthand method: (8% of the maximum of the aggregate net long or net short positions)

•Longhand method: Net position, Simulation, worst case scenario amount is charged 2%


•Equities

•Unsystematic risk charge (x-factor): 4% against the gross position

•Systematic risk charge (y-factor): 8% against the net position

•Large Bank Internal models

•BIS standardized framework was criticized for crude risk measurements + lack of correlations + incompatability with internal systems.

•BIS in 1995 allowed internal model usage by large banks with conditions:

•Adverse change is defined as 99th percentile - Minimum holding period is 10 days - correlations allowed broadly

•Proposed capital charge will be the higher of the previous day’s VAR, or the average daily VAR over the last 60 days times a factor (at least 3). Tier 2 and 3 allowed up to 250% of Tier 1.


Consolidation in Banking

• Berger, Demsetz, Strahan article

• Chapter 14 Saunders

• Economies of Scale

• Economies of Scope

• Efficiencies

• Consolidations


•Reduction in numbers 30%

•Concentration -- largest 8 banks’ share from 22 to 36%

•MSA Herfindahls declined

•Total bank offices up by 17%

•Bank + Thrift offices down by 0.1%, thrifts acquired by banks


•Several hundred M&A’s each year

•Supermegamergers

•Citi-Travelers

•BankAmerica-Nations

•BancOne-First Chicago

•Norwest-WellsFargo

•UBS-Swiss Bank Corp >>largest in EU


•Not much change in securities brokerage, life and P&A insurance

•Securities and Life ins. Less concentrated in 90’s

•P&A more concentrated

•Substantial reduction in Thrifts

•CU’s very unconcentrated because of nature


Consolidation across sectors rare in US, more important in EU


Similar to earlier panel, International M&A’s within sectors exceed across sectors, but across M&A’s relatively more important in EU


US much more fragmented, but not ‘overbranched’


• Causes of Financial Consolidation

• value-maximization: increase market power (set prices, increase concentration, market power) increase efficiency (more efficient takes over less efficient)

Too big to fail protections

• Non-value maximizing: The role of managers: weak corporate control, empire building, compensation and size, too-big-to fail, entrenched managers, etc...


•Role of government: approve-disapprove,excessive market power, too-big-to-fail, CRA requirements may encourage acquisition of weaker institutions.

•Why is consolidation increasing?

•Technological progress

•Improvements in financial conditions (internal capital markets)

•accumulation of excess capacity – financial distress


•International consolidation, globalization

•Deregulation – Riegle-Neal Interstate Banking and Branching Efficiency Act of 1994 – though with limits

•

•

•

• Early results generally positive


Internet Banking: some early evidence


Market share and concentration


Loan Distribution


Expenses and Profitability

copyright 2001 a. s. cebenoyan1 b40.3312 policymaking in financial institutions professor a. sinan...

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