Copyright 2001 A. S. Cebenoyan1 B Policymaking in Financial Institutions Professor A. Sinan Cebenoyan NYU-Stern-Finance

Copyright 2001 A. S. Cebenoyan2 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)

Copyright 2001 A. S. Cebenoyan3 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

Copyright 2001 A. S. Cebenoyan4 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 Securities Suppose FI has a $1 million market value position in 7-yr zero coupons with a face value of $1,631, and current annual yield is Daily Price volatility = The modified duration = for this bond

Copyright 2001 A. S. Cebenoyan5 If we make the (strong and unrealistic) assumption of normality in yield changes, and we wish to focus on bad outcomes, i.e., not just any change in yields, BUT an increase in yields that will only be possible with a probability, i.e., a yield increase that has a chance of 5%, or 10%, or 1%…(We decide how likely an increase we wish to be worried about). Suppose we pick 5 %, i.e., there is 1 in 20 chance 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 area under the normal distribution is found within +/ standard deviations, then we are looking at 1.65  as 16.5 basis points. Our adverse yield move. Price Volatility = -MD (  R) = (-6.527) (.00165) = DEaR = 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:

Copyright 2001 A. S. Cebenoyan6 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

Copyright 2001 A. S. Cebenoyan7 Equities Remember your CAPM: Total Risk = Systematic risk + Unsystematic risk 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

Copyright 2001 A. S. Cebenoyan8 Portfolio Aggregation We need to figure out the aggregate DEAR, summing up won’t do, REMEMBER: If the correlations between the 3 assets are: BondSWF/$US Stock Index Bond-.2.4 SWF/$.1

Copyright 2001 A. S. Cebenoyan9 Then the risk of the whole portfolio, DEAR treated like , will be Substituting the values we have: = $39,969

Copyright 2001 A. S. Cebenoyan10 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%

Copyright 2001 A. S. Cebenoyan11 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.

Copyright 2001 A. S. Cebenoyan12 Consolidation in Banking Berger, Demsetz, Strahan article Chapter 14 Saunders Economies of Scale Economies of Scope Efficiencies Consolidations

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Copyright 2001 A. S. Cebenoyan14 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

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Copyright 2001 A. S. Cebenoyan16 Several hundred M&A’s each year Supermegamergers Citi-Travelers BankAmerica-Nations BancOne-First Chicago Norwest-WellsFargo UBS-Swiss Bank Corp >>largest in EU

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Copyright 2001 A. S. Cebenoyan18 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

Copyright 2001 A. S. Cebenoyan19 Consolidation across sectors rare in US, more important in EU

Copyright 2001 A. S. Cebenoyan20 Similar to earlier panel, International M&A’s within sectors exceed across sectors, but across M&A’s relatively more important in EU

Copyright 2001 A. S. Cebenoyan21 US much more fragmented, but not ‘overbranched’

Copyright 2001 A. S. Cebenoyan22 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...

Copyright 2001 A. S. Cebenoyan23 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

Copyright 2001 A. S. Cebenoyan24 International consolidation, globalization Deregulation – Riegle-Neal Interstate Banking and Branching Efficiency Act of 1994 – though with limits Early results generally positive

Copyright 2001 A. S. Cebenoyan25 Internet Banking: some early evidence

Copyright 2001 A. S. Cebenoyan26 Market share and concentration

Copyright 2001 A. S. Cebenoyan27 Loan Distribution

Copyright 2001 A. S. Cebenoyan28 Expenses and Profitability