1. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 1 Chapter 15: Financial Risk Management: Techniques and Applications Risk management is akin to a dialysis machine. It it doesn’t work, you might have a noble obituary, but you’re dead. Ben Golub Derivatives Strategy, October, 2000, p. 26

2. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 2 Important Concepts in Chapter 15 n The concept and practice of risk management n The benefits of risk management n The difference between market and credit risk n How market risk is managed using delta, gamma, vega, and Value-at-Risk n How credit risk is managed, including credit derivatives n Risks other than market and credit risk

3. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 3 n Definition of risk management: the practice of defining the risk level a firm desires, identifying the risk level it currently has, and using derivatives or other financial instruments to adjust the actual risk level to the desired risk level.

4. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 4 Why Practice Risk Management? n The Impetus for Risk Management u Firms practice risk management for several reasons: F Interest rates, exchange rates and stock prices are more volatile today than in the past. F Significant losses incurred by firms that did not practice risk management F Improvements in information technology F Favorable regulatory environment u Sometimes we call this activity financial risk management.

5. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 5 Why Practice Risk Management? (continued) n The Benefits of Risk Management u What are the benefits of risk management, in light of the Modigliani-Miller principle that corporate financial decisions provide no value because shareholders can execute these transactions themselves? F Firms can practice risk management more effectively. F There may tax advantages from the progressive tax system. F Risk management reduces bankruptcy costs. F Managers are trying to reduce their own risk.

6. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 6 Why Practice Risk Management? (continued) n The Benefits of Risk Management (continued) u By protecting a firm’s cash flow, it increases the likelihood that the firm will generate enough cash to allow it to engage in profitable investments. u Some firms use risk management as an excuse to speculate. u Some firms believe that there are arbitrage opportunities in the financial markets. u Note: The desire to lower risk is not a sufficient reason to practice risk management.

7. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 7 Managing Market Risk n Market risk: the uncertainty associated with interest rates, foreign exchange rates, stock prices, or commodity prices. u Example: A dealer with the following positions: F A four-year interest rate swap with $10 million notional principal in which it pays a fixed rate and receives a floating rate. F A 3-year interest rate call with $8 million notional principal. The dealer is short and the exercise rate is 12%. u See Table 15.1, p. 546 for current term structure and forward rates. We obtain the call price as $73,745 and the swap rate is 11.85%. Table 15.1, p. 546Table 15.1, p. 546

8. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 8 Managing Market Risk (continued) n Delta Hedging u We estimate the delta by repricing the swap and option with a one basis point move in all spot rates and average the price change. F See Table 15.2, p. 547 for estimated swap and option deltas. Table 15.2, p. 547Table 15.2, p. 547 We are long the swap so we have a delta of $2,130.5, round to $2,131.We are long the swap so we have a delta of $2,130.5, round to $2,131. We are short the option so we have a delta of -$244.We are short the option so we have a delta of -$244. Our overall delta is $1,887.Our overall delta is $1,887.

9. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 9 Managing Market Risk (continued) n Delta Hedging (continued) u We need a Eurodollar futures position that gains $1,887 if rates move down and loses that amount if rates move up. Thus, we require a long position of $1,887/$25 = 75.48 contracts. Round to 75. Overall delta: F $2,131 (from swap) F -$244 (from option) F 75(-$25) (from futures) F = $12 (overall)

10. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 10 Managing Market Risk (continued) n Gamma Hedging u Here we deal with the risk of large price moves, which are not fully captured by the delta. u See Table 15.3, p. 549 for the estimation of swap and option gammas. Swap gamma is -$12,500, and option gamma is $5,000. Being short the option, the total gamma is -$17,500. Table 15.3, p. 549Table 15.3, p. 549 u Eurodollar futures have zero gamma so we must add another option position to offset the gamma. We assume the availability of a one-year call with delta of $43 and gamma of $2,500.

11. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 11 Managing Market Risk (continued) n Gamma Hedging (continued) u We use x 1 Eurodollar futures and x 2 of the one-year calls. The swap and option have a delta of $1,887 and gamma of -$17,500. We solve the following equations: F $1,887 + x 1 (-$25) + x 2 ($43) = $0 (zero delta) F -$17,500 + x 1 ($0) + x 2 ($2,500) = $0 (zero gamma) F Solving these gives x 1 = 87.52 (go long 88 Eurodollar futures) and x 2 = 7 (go long 7 times $1,000,000 notional principal of one-year option)

12. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 12 Managing Market Risk (continued) n Vega Hedging u Swaps, futures, and FRAs do not have vegas. u We estimate the vegas of the options F On our 3-year option, if volatility increases (decreases) by.01, option will increase (decrease) by $42 (-$42). Average is $42. We are short this option, so vega = -$42. F One-year option has estimated vega of $3.50. F Overall portfolio has vega of ($3.50)(7 million) - $42 = -$17.50.

13. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 13 Managing Market Risk (continued) n Vega Hedging (continued) u We add a Eurodollar futures option, which has delta of -$12.75, gamma of -$500, and vega of $2.50 per $1MM. u Solve the following equations F $1,887 + x 1 (-$25) + x 2 ($43) + x 3 (-$12.75) = 0 (delta) F -$17,500 + x 1 ($0) + x 2 ($2,500) + x 3 (-$500) = 0 (gamma) F -$42 + x 1 ($0) + x 2 ($3.50) + x 3 ($2.50) = 0 (vega) u The coefficients are the multiples of $1,000,000 notional principal we need. u Solutions are x 1 = 86.61, x 2 = 8.09375, x 3 = 5.46875.

14. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 14 Managing Market Risk (continued) n Vega Hedging (continued) u Any type of hedge (delta, delta-gamma, or delta- gamma-vega) must be periodically adjusted. u Virtually impossible to have a perfect hedge.

15. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 15 Managing Market Risk (continued) n Value-at-Risk (VAR) u A dollar measure of the minimum loss that would be expected over a given time with a given probability. Example: F VAR of $1 million for one day at.05 means that the firm could expect to lose at least $1 million over a one day period 5% of the time. u Widely used by dealers and increasingly by end users. u See Table 15.4, p. 553 for example of discrete probability distribution of change in value. VAR at 5 % is $3 million loss. Table 15.4, p. 553Table 15.4, p. 553 u See Figure 15.1, p. 554 for continuous distribution. Figure 15.1, p. 554Figure 15.1, p. 554

16. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 16 Managing Market Risk (continued) n Value-at-Risk (VAR) (continued) u VAR calculations require use of formulas for expected return and standard deviation of a portfolio: u where F E(R 1 ), E(R 2 ) = expected returns of assets 1 and 2 F  1,  2 = standard deviations of assets 1 and 2 F  = correlation between assets 1 and 2 F w 1, w 2 = % of one’s wealth invested in asset 1 or 2

17. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 17 Managing Market Risk (continued) n Value-at-Risk (VAR) (continued) u Three methods of estimating VAR F Analytical method: Uses knowledge of the parameters (expected return and standard deviation) of the probability distribution and assumes a normal distribution. Example: $20 million of S&P 500 with expected return of.12 and volatility of.15 and $12 million of Nikkei 300 with expected return of.105 and volatility of.18. Correlation is.55. Using the above formulas, the overall portfolio expected return is.1144 and volatility is.1425.Example: $20 million of S&P 500 with expected return of.12 and volatility of.15 and $12 million of Nikkei 300 with expected return of.105 and volatility of.18. Correlation is.55. Using the above formulas, the overall portfolio expected return is.1144 and volatility is.1425.

18. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 18 Managing Market Risk (continued) n Value-at-Risk (VAR) (continued) For a weekly VAR, convert these to weekly figures.For a weekly VAR, convert these to weekly figures. Expected return =.1144/52 =.0022 Volatility =.1425/.0198. Volatility =.1425/  52 =.0198. With a normal distribution, we haveWith a normal distribution, we have VAR =.0022 - 1.65(.0198) = -.0305 So the VAR is $32,000,000(.0305) = $976,000.So the VAR is $32,000,000(.0305) = $976,000.

19. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 19 Managing Market Risk (continued) n Value-at-Risk (VAR) (continued) Example using options: 200 short 12-month calls on S&P 500, which has volatility of.15 and price of $14.21. Total value of $1,421,000.Example using options: 200 short 12-month calls on S&P 500, which has volatility of.15 and price of $14.21. Total value of $1,421,000. –Based on monthly data, expected return is.0095 and volatility is.0412. –Upside 5 % is.0095 + 1.65(.0412) =.0775, which is 720(1.0775) = 775.80. –Option would be worth 775.80 - 720 = 55.80 so loss is 55.80 - 14.21= 41.59 per option. –Total loss = 200(500)(41.59) = $4.159 million. This is the VAR.

20. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 20 Managing Market Risk (continued) n Value-at-Risk (VAR) (continued) One assumption often made is that the expected return is zero. This is not likely to be true.One assumption often made is that the expected return is zero. This is not likely to be true. Sometimes rather than use the precise option price from a model, a delta is used to estimate the price. This makes the analytical method be sometimes called the delta-normal method.Sometimes rather than use the precise option price from a model, a delta is used to estimate the price. This makes the analytical method be sometimes called the delta-normal method. Volatility and correlation information is necessary. See the web site www.riskmetrics.com, where data of this sort are provided free.Volatility and correlation information is necessary. See the web site www.riskmetrics.com, where data of this sort are provided free.

21. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 21 Managing Market Risk (continued) n Value-at-Risk (VAR) (continued) F Historical method: Uses historical information on the user’s portfolio to obtain the distribution. Example: See Figure 15.2, p. 557. For portfolio of $15 million, VAR at 5% is approximately a loss of 10% or $15,000,000(.10) = -$1,500,000.Example: See Figure 15.2, p. 557. For portfolio of $15 million, VAR at 5% is approximately a loss of 10% or $15,000,000(.10) = -$1,500,000.Figure 15.2, p. 557Figure 15.2, p. 557 Historical method is subject to limitation that the past holdings of the portfolio may not have the same distributional properties as the future holdings. It also is limited by the results of the chosen time period, which might not be representative of the future.Historical method is subject to limitation that the past holdings of the portfolio may not have the same distributional properties as the future holdings. It also is limited by the results of the chosen time period, which might not be representative of the future.

22. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 22 Managing Market Risk (continued) n Value-at-Risk (VAR) (continued) F Monte Carlo Simulation method: Uses Monte Carlo method, as described in Appendix 14.B, to generate random outcomes on the portfolio’s components.

23. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 23 Managing Market Risk (continued) n Value-at-Risk (VAR) (continued) u A Comprehensive Calculation of VAR F We do an example of a portfolio of $25 million in the S&P 500. We want a 5% 1-day VAR using each method. We collect a sample of daily returns on the S&P 500 for the past year and obtain the following parameter estimates: Average daily return =.0457% and daily standard deviation = 1.3327%. These result in annual figures of

24. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 24 Managing Market Risk (continued) n Value-at-Risk (VAR) (continued) u A Comprehensive Calculation of VAR (continued) F Analytical method: We have 0.0457% - (1.65)1.3327% = -2.1533%. So the VAR is.021533($25,000,000) = $538,325.021533($25,000,000) = $538,325 The.21 standard deviation is historically a bit high. Re-estimating with a standard deviation of.15 gives us a daily standard deviation of 0.9430. Then we obtain 0.0474% - 1.65(0.9430) = -1.5086% and a VAR ofThe.21 standard deviation is historically a bit high. Re-estimating with a standard deviation of.15 gives us a daily standard deviation of 0.9430. Then we obtain 0.0474% - 1.65(0.9430) = -1.5086% and a VAR of.015086($25,000,000) = $377,150.015086($25,000,000) = $377,150 Are our data normally distributed? Observe Figure 15.3, p. 559.Are our data normally distributed? Observe Figure 15.3, p. 559. Figure 15.3, p. 559 Figure 15.3, p. 559

25. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 25 Managing Market Risk (continued) n Value-at-Risk (VAR) (continued) u A Comprehensive Calculation of VAR (continued) F Historical method: Here we rank the returns from worst to best. For 253 returns we obtain the 5% worst by observing the.05(253) = 12.65 worst return. We shall make it the 13 th worst. This would be -2.0969%. Thus, the VAR is.020969($25,000,000) = $524,225.020969($25,000,000) = $524,225

26. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 26 Managing Market Risk (continued) n Value-at-Risk (VAR) (continued) u A Comprehensive Calculation of VAR (continued) F Monte Carlo simulation method: We shall use an expected return of 12%, a standard deviation of 15% and a normal distribution. We generate 253 random returns (this number is arbitrary and should actually be much larger) by the following method:We generate 253 random returns (this number is arbitrary and should actually be much larger) by the following method: where  is a standard normal random number.where  is a standard normal random number.

27. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 27 Managing Market Risk (continued) n Value-at-Risk (VAR) (continued) u A Comprehensive Calculation of VAR (continued) F Monte Carlo simulation method (continued): We do this 253 times, rank the returns from worst to best and obtain the 13th worst return, which is -1.3942%. Then the VAR is.013942($25,000,000) = $348,550.013942($25,000,000) = $348,550

28. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 28 Managing Market Risk (continued) n Value-at-Risk (VAR) (continued) u A Comprehensive Calculation of VAR (continued) F So VAR is estimated to be either $538,325$538,325 $377,150$377,150 $524,225$524,225 $348,550$348,550 F Key considerations: wide ranges such as this are common, real-world portfolios are more complicated than this, ex post evaluation should be done

29. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 29 Managing Market Risk (continued) n Value-at-Risk (VAR) (continued) u Benefits and Criticisms of VAR F Widely used F Facilitates communication with senior management F Acceptable in banking regulation F Used to allocate capital within firms F Used in performance evaluation

30. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 30 Managing Market Risk (continued) n Value-at-Risk (VAR) (continued) u Extensions of VAR F Stress testing F Conditional VAR: expected loss, given that loss exceeds VAR. In S&P example, this is $719,450. F Cash Flow at Risk (CAR or CFAR), which is more appropriate for firms with assets that generate cash but for which a market value cannot easily be assigned. Example: expected cash flow of $10 million with standard deviation of $2 million. CAR at.05 would be 1.65($2 million) = $3.3 million becauseExample: expected cash flow of $10 million with standard deviation of $2 million. CAR at.05 would be 1.65($2 million) = $3.3 million because Prob[$10 million - $3.3 million > Actual Cash Flow] =.05. –Note: CAR is a shortfall measure. F Earnings at Risk (EAR): measures earnings shortfall.

31. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 31 Managing Credit Risk n Credit risk or default risk is the risk that the counterparty will not pay off in a financial transaction. n Credit ratings are widely used to assess credit risk.

32. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 32 Managing Credit Risk (continued) n Option Pricing Theory and Credit Risk u Here we use option pricing theory to understand the nature of credit risk. Consider a firm with assets with market value A 0, and debt with face value of F due at T. The market value of the debt is B 0. The market value of the stock is S 0. u See Table 15.5, p. 563 for the payoffs to the suppliers of capital. Note how the stock is like a call option on the assets. Its payoff is Table 15.5, p. 563Table 15.5, p. 563 F Max(0,A T – F) u Using put-call parity, we have F P 0 = S 0 – A 0 + F(1 + r) -T

33. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 33 Managing Credit Risk (continued) n Option Pricing Theory and Credit Risk (continued) u This put is the value of limited liability to the stockholders. Rearranging, we obtain S 0 = A 0 + P 0 – F(1 + r) -T. But, by definition, S 0 = A 0 – B 0. Setting these equal, we obtain F B 0 = F(1 + r) -T – P 0. u Thus, the bond subject to default risk is equivalent to a risk-free bond and a put option written by the bondholders to the stockholders. u The Black-Scholes formula adapted to price the stock as a call would be u Using B 0 = A 0 – S 0, we can obtain the value of the bonds.

34. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 34 Managing Credit Risk (continued) n The Credit Risk of Derivatives u Current credit risk is the risk to one party that the other will be unable to make payments that are currently due. u Potential credit risk is the risk to one party that the counterparty will default in the future. u In options, only the buyer faces credit risk. u FRAs and swaps have two-way credit risk but at a given point in time, the risk is faced by only one of the two parties.

35. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 35 Managing Credit Risk (continued) n The Credit Risk of Derivatives (continued) u Potential credit risk is largest during the middle of an interest rate swap’s life but due to principal repayment, potential credit risk is largest during the latter part of a currency swap’s life. u Typically all parties pay the same price on a derivative, regardless of their credit standing. Credit risk is managed through F limiting exposure to any one party (primary method) F collateral F periodic marking-to-market F (by dealers) captive derivatives subsidiaries F netting (see next)

36. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 36 Managing Credit Risk (continued) n Netting u Netting: several similar processes in which the amount of cash owed by one party to the other is reduced by the amount owed by the latter to the former. F Bilateral netting: netting between two parties. F Multilateral netting: netting between more than two parties; essentially the same as a clearinghouse. F Payment netting: Only the net amount of a payment owed from one party to the other is paid. F Cross-product netting: payments from one type of transaction are netted against payments for another type of transaction.

37. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 37 Managing Credit Risk (continued) n Netting (continued) F Netting by novation: net value of two parties’ mutual obligations is replaced by a single transaction; often used in FX markets. F Closeout netting: netting in the event of default, where all transactions between two parties are netted against each other; see example in text. F The OTC derivatives market has an excellent record of default. Note the Hammersmith and Fulham default where it was found that a town had no legal authority to engage in swaps. The town was able to get out of paying its losses.

38. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 38 Managing Credit Risk (continued) n Credit Derivatives: A family of derivative instruments that have payoffs contingent on the credit quality of a particular party. Types include u Total return swaps: See Figure 15.4, p. 570. Credit derivative buyer purchases swap from credit derivative seller in which it pays the total return on a specific bond. If that return is reduced by some credit event, this loss is passed through automatically in the swap. Figure 15.4, p. 570Figure 15.4, p. 570

39. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 39 Managing Credit Risk (continued) n Credit Derivatives (continued) u Credit swap: A swap in which the credit derivatives buyer pays a periodic fee to the credit derivatives seller. If the buyer sustains a credit loss from a third party, it then receives payments from the credit derivatives seller to compensate. See Figure 15.5, p. 571. This is really more like an option. Figure 15.5, p. 571Figure 15.5, p. 571 u Credit spread option: An option in which the underlying is the yield spread on a bond.

40. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 40 Managing Credit Risk (continued) n Credit Derivatives (continued) u Credit linked security: This is a bond or note that pays off less than its face value if a credit event occurs on a third party. u The credit derivatives market is small but growing rapidly. The notional principal of credit derivatives at U. S. banks was estimated at about $573 billion in 2002.

41. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 41 Other Types of Risks n operational risk (including inadequate controls) n model risk n liquidity risk n accounting risk n legal risk n tax risk n regulatory risk n settlement risk n systemic risk

42. D. M. ChanceAn Introduction to Derivatives and Risk Management, 6th ed.Ch. 15: 42 Summary

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