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BUFN 722

BUFN 722 ch-10 Market Risk Overview This chapter discusses the nature of market risk and appropriate measures Dollar exposure RiskMetrics Historic or back simulation Monte Carlo simulation Links between market risk and capital requirements Market Risk:

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BUFN 722

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  1. BUFN 722 ch-10 Market Risk BUFN722- Financial Institutions

  2. Overview • This chapter discusses the nature of market risk and appropriate measures • Dollar exposure • RiskMetrics • Historic or back simulation • Monte Carlo simulation • Links between market risk and capital requirements BUFN722- Financial Institutions

  3. Market Risk: • Market risk is the uncertainty resulting from changes in market prices . It can be measured over periods as short as one day. • Usually measured in terms of dollar exposure amount or as a relative amount against some benchmark. BUFN722- Financial Institutions

  4. Market Risk Measurement • Important in terms of: • Management information • Setting limits • Resource allocation (risk/return tradeoff) • Performance evaluation • Regulation BUFN722- Financial Institutions

  5. Calculating Market Risk Exposure • Generally concerned with estimated potential loss under adverse circumstances. • Three major approaches of measurement • JPM RiskMetrics (or variance/covariance approach) • Historic or Back Simulation • Monte Carlo Simulation BUFN722- Financial Institutions

  6. JP Morgan RiskMetrics Model • Idea is to determine the daily earnings at risk = dollar value of position × price sensitivity × potential adverse move in yield or, DEAR = Dollar market value of position × Price volatility. • Can be stated as (-MD) × adverse daily yield move where, MD = D/(1+R) Modified duration = MacAulay duration/(1+R) BUFN722- Financial Institutions

  7. Confidence Intervals • If we assume that changes in the yield are normally distributed, we can construct confidence intervals around the projected DEAR. (Other distributions can be accommodated but normal is generally sufficient). • Assuming normality, 90% of the time the disturbance will be within 1.65 standard deviations of the mean. BUFN722- Financial Institutions

  8. Confidence Intervals: Example • Suppose that we are long in 7-year zero-coupon bonds and we define “bad” yield changes such that there is only 5% chance of the yield change being exceeded in either direction. Assuming normality, 90% of the time yield changes will be within 1.65 standard deviations of the mean. If the standard deviation is 10 basis points, this corresponds to 16.5 basis points. Concern is that yields will rise. Probability of yield increases greater than 16.5 basis points is 5%. BUFN722- Financial Institutions

  9. Confidence Intervals: Example • Price volatility = (-MD)  (Potential adverse change in yield) = (-6.527)  (0.00165) = -1.077% DEAR = Market value of position  (Price volatility) = ($1,000,000)  (.01077) = $10,770 Note if MD = -6.527, what is R? BUFN722- Financial Institutions

  10. Confidence Intervals: Example • To calculate the potential loss for more than one day: Market value at risk (VAR) = DEAR × N • Example: For a five-day period, VAR = $10,770 × 5 = $24,082.45 BUFN722- Financial Institutions

  11. Foreign Exchange & Equities • In the case of Foreign Exchange, DEAR is computed in the same fashion we employed for interest rate risk. • For equities, if the portfolio is well diversified then DEAR = dollar value of position × stock market return volatility where the market return volatility is taken as 1.65 sM. BUFN722- Financial Institutions

  12. Aggregating DEAR Estimates • Cannot simply sum up individual DEARs. • In order to aggregate the DEARs from individual exposures we require the correlation matrix. • Three-asset case: DEAR portfolio = [DEARa2 + DEARb2 + DEARc2 + 2rab × DEARa × DEARb + 2rac × DEARa × DEARc + 2rbc × DEARb × DEARc]1/2 BUFN722- Financial Institutions

  13. Historic or Back Simulation • Advantages: • Simplicity • Does not require normal distribution of returns (which is a critical assumption for RiskMetrics) • Does not need correlations or standard deviations of individual asset returns. BUFN722- Financial Institutions

  14. Historic or Back Simulation • Basic idea: Revalue portfolio based on actual prices (returns) on the assets that existed yesterday, the day before, etc. (usually previous 500 days). • Then calculate 5% worst-case (25th lowest value of 500 days) outcomes. • Only 5% of the outcomes were lower. BUFN722- Financial Institutions

  15. Estimation of VAR: Example • Convert today’s FX positions into dollar equivalents at today’s FX rates. • Measure sensitivity of each position • Calculate its delta. • Measure risk • Actual percentage changes in FX rates for each of past 500 days. • Rank days by risk from worst to best. BUFN722- Financial Institutions

  16. Weaknesses • Disadvantage: 500 observations is not very many from statistical standpoint. • Increasing number of observations by going back further in time is not desirable. • Could weight recent observations more heavily and go further back. BUFN722- Financial Institutions

  17. Monte Carlo Simulation • To overcome problem of limited number of observations, synthesize additional observations. • Perhaps 10,000 real and synthetic observations. • Employ historic covariance matrix and random number generator to synthesize observations. • Objective is to replicate the distribution of observed outcomes with synthetic data. BUFN722- Financial Institutions

  18. Regulatory Models • BIS (including Federal Reserve) approach: • Market risk may be calculated using standard BIS model. • Specific risk charge. • General market risk charge. • Offsets. • Subject to regulatory permission, large banks may be allowed to use their internal models as the basis for determining capital requirements. BUFN722- Financial Institutions

  19. BIS Model • Specific risk charge: • Risk weights × absolute dollar values of long and short positions • General market risk charge: • reflect modified durations  expected interest rate shocks for each maturity • Vertical offsets: • Adjust for basis risk • Horizontal offsets within/between time zones BUFN722- Financial Institutions

  20. Large Banks: BIS versus RiskMetrics • In calculating DEAR, adverse change in rates defined as 99th percentile (rather than 95th under RiskMetrics) • Minimum holding period is 10 days (means that RiskMetrics’ daily DEAR multiplied by 10. • Capital charge will be higher of: • Previous day’s VAR (or DEAR  10) • Average Daily VAR over previous 60 days times a multiplication factor  3. BUFN722- Financial Institutions

  21. Overview • The Corporate Treasurer’s Financial Risk Management Problem- Manage Risk – Not avoid it • The Market Value of the Firm and Channels of Risk • Accounting Measures of Foreign Exchange Exposure • Exposure of the Balance Sheet: Translation Exposure • Exposure of the Income Statement: Transaction Exposure • U.S. Accounting Conventions: Reporting Accounting Gains and Losses BUFN722- Financial Institutions

  22. Overview • Economic Measures of Foreign Exchange Exposure • The Regression Approach • The Scenario Approach • Empirical Evidence on Firm Profits, Share Prices, and Exchange Rates • Arguments for Hedging Risks at the Corporate Level BUFN722- Financial Institutions

  23. Overview • Financial Strategies Toward Risk Management • The Currency Profile and Suitable Financial Hedging Instruments • Policy Issues - International Financial Managers • Problems in Estimating Economic Exposure • Picking an Appropriate Hedge Ratio • The International Investor’s Currency Risk Management Problem • The Value at Risk Approach BUFN722- Financial Institutions

  24. Overview • Policy Issues - Public Policymakers • Disclosure of Financial Exposure • Financial Derivatives and Corporate Hedging Policies BUFN722- Financial Institutions

  25. The Corporate Treasurer’sFinancial Risk Management Problem • Corporate treasurers are directly responsible for managing the firm’s exposure to financial risk. • The risks that remain are held by the investor, who can reduce these risks through a diversified portfolio of shares, or by applying some of the same hedging techniques available to the corporate treasurer. BUFN722- Financial Institutions

  26. Types of Risk • Credit risk • Risk of default or failure of borrower or counterparty; unwilling to service loan; e.g., 1998 Russia 90-day moratorium on debt pay. • Market risk • Adverse changes in market prices, rates, exchange rates • Liquidity risk • Cash flows not sufficient to meet bank’s financial commitments • Interest rate risk • Earnings & returns fluctuate with changes in interest rates • Operational risk • Potential losses due to breakdown in information, communication, transaction processing, settlement systems, fraud, unauthorized transactions by employees • Cross-border risk • Call risk - Instrument called before maturity • Legislative risk change in laws that affect securities BUFN722- Financial Institutions

  27. Credit Risk Management • Screening • Monitoring • Long-term customer relationships • Loan commitments • Collateral • Compensating balances • Credit rationing BUFN722- Financial Institutions

  28. Market Risk Management The Value at Risk (VAR) Approach • The VAR approach is a relatively new approach for measuring the exposure of financial assets. • It can be applied to any portfolio of assets (and liabilities) whose market values are available on a periodic basis and whose price volatilities () can be estimated. • Assuming normal price distributions, calculate the loss in value of the portfolio if an unlikely (say, 5% chance) adverse price movement occurs. The result of this calculation is the value at risk. BUFN722- Financial Institutions

  29. Value at Risk (VAR) • Value at Risk • Estimates the largest expected loss to a particular investment position for a specified confidence level • Applying Value At Risk • Deriving The Maximum Dollar Loss • VAR = estimated potential loss from its trading business that could result from adverse movements in market prices. • Common Adjustments To The Value-At-Risk Applications BUFN722- Financial Institutions

  30. VAR VAR is a risk measurement that estimates the largest expected loss to a particular investment position for a specified confidence level. This method became popular in the late 1990s after some mutual funds & pension funds experienced abrupt large losses. VAR is intended to warn investors about potential maximum loss that could occur. If investors are uncomfortable with the potential loss that could occur in a day or week, they can revise their investment portfolio to make it less risky. VAR focuses on pessimistic portion of probability distribution of returns from the investment of concern. E.g., a port. mgr. Uses a 90% confidence level, which estimates the max. daily expected loss to an asset in 90% of the trading days over an upcoming period. The higher the confidence level desired, the larger the maximum expected loss that might occur for a given type of investment. E.g., one may expect that the daily loss from holding a particular asset won’t be worse than -5% when using a 90% confidence level & < -8% if a 99% confidence level.In essence the more confidence investors have that the actual loss won’t be > the expected maximum loss, the further they move into the left tail of the probability distribution. VAR is also used to measure risk of a portfolio. Some assets have high risk when assessed individually, but low risk when part of a portfolio because the likelihood of a large loss in the port. Is influenced by the probabilities of simultaneous losses in all of the component assets for the period of concern. BUFN722- Financial Institutions

  31. Applying Value at Risk • More precisely, VaR measures the worst possible loss that a bank could expect to suffer over a given time interval, under normal market conditions, at a given confidence level. E.g., a bank might calculate that the daily VaR of its trading portfolio is $35 million at a 99% confidence interval. This means that there is only 1 chance in 100 that a loss > $35 million would occur on any given day. Note: this is NOT a maximum loss; e.g., if a bank regularly measures VaR at the 99% confidence level, the actually losses should exceeds its estimate 1% of the time, or 1 day out of 100. • Methods of determining the maximum expected loss • Use of historical returns • Example: count the percentage of days an asset drops a certain level • Use of standard deviation • Used to derive boundaries for a specific confidence level • Use of beta • Used in conjunction with a forecast of a maximum market drop BUFN722- Financial Institutions

  32. Applying Value at Risk • Deriving the maximum dollar loss • Apply the maximum percentage loss to the value of the investment • Common adjustments to the value-at-risk applications • Investment horizon desired • Length of historical period used • Time-varying risk • Restructuring the investment portfolio BUFN722- Financial Institutions

  33. Widespread usage of VaR • Easy to understand • BIS meeting at Basel in 1995, at which major central banks amended the 1988 accord requiring financial institutions to hold capital against their exposure to market risk; this created an incentive for banks to develop sophisticated internal risk measurement systems to calculate VaR and thus avoid more regulatory requirements. Therefore, in 1998, large banks with substantial trading businesses began using their own internal measures of market risk to adjust their capital requirements. They use a VAR model, usually with a 99 percent confidence interval • JP Morgan made its RiskMetrics system available free from charge over the Internet; this system provided financial data & methodology to calculate a portfolio’s VaR. www.riskmetrics.com BUFN722- Financial Institutions

  34. Regulation of Capital • Testing the validity of a bank’s VAR • Uses backtests with actual daily trading gains or losses • If the VAR is estimated properly, only 1 percent of the actual trading days should show results worse than the estimated VAR • Related stress tests • Bank identifies a possible extreme event to estimate potential losses BUFN722- Financial Institutions

  35. Exact computation of VaR depends on assumptions about: • Distribution of price changes, normal or otherwise • Extent to which today’s change in the price of an asset may be correlated to past price changes • Extent to which the characteristics of mean U and standard deviation (volatility) are stable over time • Relationship between 2 or more different price moves • Data series to which these assumptions apply. • Financial managers use historical market data on various financial asses to create their VaR model. BUFN722- Financial Institutions

  36. JP Morgan’s VaR • Maximum estimated losses in the market value of a given position that may be incurred before the position is neutralized or reassessed. • VaRx = Vx x dV/dP x Dpi Vx = market value of position x dV/dP = sensitivity to price move per $ market value Dpi = adverse price movement over time i; e.g, if the time horizon is one day, then VaR becomes daily earnings at risk DEAR = Vx x dV/dP x DPday BUFN722- Financial Institutions

  37. JP Morgan’s assumptions in its measure of VaR • Prices of financial instruments follow a stable random walk; thus, price changes are normally distributed • Price changes are serially uncorrelated; there is no correlation between change today and changes in the past • Standard deviation (volatility) of price or rate changes is stable over time; i.e., past movements may be used to characterize future movements. • Interrelationships between 2 different price movements follow a joint normal distribution. BUFN722- Financial Institutions

  38. Drawbacks of VaR • Markets are NOT normal • Portfolios are non-linear • Volatility is NOT constant • Markets move together but no one knows how BUFN722- Financial Institutions

  39. Portfolio Stress Testing • Technique that relies on computer modeling of different scenarios and computation of results of those scenarios on a bank’s portfolio. • E.g., Sept 11 bombing of WTC; political assassination • E.g., Mexican peso devalued by 30%. • All assets in portfolio are revalued using new environment, creating a new estimate for the return on the portfolio • Many such scenarios lead to many such exercise, so that a range of values for return on the portfolio is derived • By specifying the probability for each scenario, mangers can then generate a distribution of portfolio returns, from which VaR can be measured • The advantage of this method is that it allows risk managers to evaluate possible scenarios that may be completely absent from historical data. • Chase management devised an incentive package that reduced compensation if risk taking did not lead to appropriate rewards, helping it create a more conservative risk portfolio overall. BUFN722- Financial Institutions

  40. Flaws of stress testing • Subjective- difficult to brainstorm scenarios that have never occurred • Choice of scenarios may be affected by bank’s portfolio position, itself – where portfolio is invested • Poor handling of correlations – stress testing examines effect of a large movement on one financial variable at a time, so it is not well suited to large, complex portfolios such as those held by international banks. • Stress testing is supplement to VaR, not a replacement BUFN722- Financial Institutions

  41. BIS 2000 Study on Stress Testing • Financial institutions relied mostly on four different techniques in stress testing (technique and “stress test result”) • Simple sensitivity test Change in portfolio value for 1 or more shocks to a single risk factor • Scenario analysis Change in portfolio value if scenario were to occur (historical or hypothetical) • Maximum loss Sum of individual trading units’ worst case scenarios • Extreme value theory Probability distribution of extreme losses BUFN722- Financial Institutions

  42. Operational Risks • Most difficult to quantify • “Rogue trader” losses • Risk of computer or telephone outage disrupting operations systems in critical areas • Best safeguard is internal control. BUFN722- Financial Institutions

  43. Interest Rate Risk • GAP = RSA – RSL • Repricing or funding gap • GAP: the difference between those assets whose interest rates will be repriced or changed over some future period (RSAs) and liabilities whose interest rates will be repriced or changed over some future period (RSLs • Rate Sensitivity • the time to reprice an asset or liability • a measure of an FI’s exposure to interest rate changes in each maturity “bucket” • GAP can be computed for each of an FI’s maturity buckets • Multiply GAP times change in interest rate reveals effect on bank income • Alternative method: Duration gap analysis examines sensitivity of market value of financial institution’s net worth to changes in interest rates; duration measures average lifetime of security’s stream of payments BUFN722- Financial Institutions

  44. Calculating GAP for a Maturity Bucket NIIi = (GAP)j ij = (RSAj - RSLj) ij where NIIj = change in net interest income in the ith maturity bucket GAPj = dollar size of the gap between the book value of rate-sensitive assets and rate- sensitive liabilities in maturity bucket i ij = change in the level of interest rates impacting assets and liabilities in the jth maturity bucket BUFN722- Financial Institutions

  45. Duration Model Duration gap - a measure of overall interest rate risk exposure for an FI D = - % in market value of a security  i/(1 + i) BUFN722- Financial Institutions

  46. Policy Issues - Public Policymakers Disclosure of Financial Exposure • The possibility that individual firms may face substantial exposure to exchange rate changes, as well as the increased trading in financial derivatives in recent years, create a genuine concern among investors and regulators regarding corporate exposure to financial risks. • Note that a firm without a financial position may still face substantial currency and interest rate risk due to its ongoing operations. BUFN722- Financial Institutions

  47. Policy Issues - Public Policymakers Financial Derivatives and Corporate Hedging Policies • The findings of various studies were consistent with the notion that firms used derivatives to lower the variability of their cash flows or earnings. • It was also found that the likelihood of using derivatives was positively related to foreign pretax income, foreign sales, and foreign-denominated debt. BUFN722- Financial Institutions

  48. The Market Value of the Firm • The market value of a firm at time t (MVt) is the summation of the firm’s cash flows (CF) over time discounted back to their present value by an appropriate discount factor (i): • Cash flows in each currency are discounted at their own appropriate interest rate and multiplied by a spot exchange rate. BUFN722- Financial Institutions

  49. The Market Value of the Firm • The sensitivity of the market value of the firm to a change in an exchange rate measures exchange rate exposure. • For the $/€ exchange rate, the sensitivity measure can be expressed as: BUFN722- Financial Institutions

  50. Direct Economic Exposure Home Currency Strengthens Home Currency Weakens Sales Abroad UnfavorableFavorable Revenue worth less in home currency terms Revenue worth more Source Abroad FavorableUnfavorable Inputs cheaper in home currency terms Inputs more expensive Profits Abroad UnfavorableFavorable Profits worth less Profits worth more Channels of Exposure toForeign Exchange Risk BUFN722- Financial Institutions

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