1. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.1 Value at Risk Chapter 14

2. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.2 The Question Being Asked in VaR “What loss level is such that we are X % confident it will not be exceeded in N business days?”

3. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.3 VaR and Regulatory Capital Regulators require banks to keep capital for market risk equal to the average of VaR estimates for past 60 trading days using X=99 and N=10, times a multiplication factor. (Usually the multiplication factor equals 3)

4. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.4 Advantages of VaR It captures an important aspect of risk in a single number It is easy to understand It asks the simple question: “How bad can things get?”

5. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.5 Daily Volatilities In option pricing we express volatility as volatility per year In VaR calculations we express volatility as volatility per day

6. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.6 Daily Volatility continued Strictly speaking we should define  day as the standard deviation of the continuously compounded return in one day In practice we assume that it is the standard deviation of the proportional change in one day

7. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.7 IBM Example (See page 343) We have a position worth $10 million in IBM shares The volatility of IBM is 2% per day (about 32% per year) We use N =10 and X =99

8. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.8 IBM Example continued The standard deviation of the change in the portfolio in 1 day is $200,000 The standard deviation of the change in 10 days is

9. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.9 IBM Example continued We assume that the expected change in the value of the portfolio is zero (This is OK for short time periods) We assume that the change in the value of the portfolio is normally distributed Since N(0.01)=-2.33, the VaR is

10. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.10 AT&T Example Consider a position of $5 million in AT&T The daily volatility of AT&T is 1% (approx 16% per year) The S.D per 10 days is The VaR is

11. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.11 Portfolio (See page 344) Now consider a portfolio consisting of both IBM and AT&T Suppose that the correlation between the returns is 0.7

12. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.12 S.D. of Portfolio A standard result in statistics states that In this case  x = 632,456 and  Y =158,114 and  = 0.7. The standard deviation of the change in the portfolio value is therefore 751,665

13. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.13 VaR for Portfolio The VaR for the portfolio is The benefits of diversification are (1,473,621+368,405)-1,751,379=$90,647 What is the incremental effect of the AT&T holding on VaR?

14. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.14 The Linear Model We assume The change in the value of a portfolio is linearly related to the change in the value of market variables The changes in the values of the market variables are normally distributed

15. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.15 The General Linear Model continued (Equation 14.5)

16. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.16 Handling Interest Rates We do not want to define every interest rate as a different market variable An approach is to use the duration relationship  P=-DP  y so that  P = DPy  y where  y is the volatility of yield changes and  P is as before the standard deviation of the change in the portfolio value

17. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.17 Alternative: Cash Flow Mapping (See page 347) We choose as market variables bond prices with standard maturities (1mm, 3mm, 6mm, 1yr, 2yr, 5yr, 7yr, 10yr, 30yr) Suppose that the 5yr rate is 6% and the 7yr rate is 7% and we will receive a cash flow of $10,000 in 6.5 years. The volatilities per day of the 5yr and 7yr bonds are 0.50% and 0.58% respectively

18. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.18 Example continued We interpolate between the 5yr rate of 6% and the 7yr rate of 7% to get a 6.5yr rate of 6.75% The PV of the $10,000 cash flow is

19. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.19 Example continued We interpolate between the 0.5% volatility for the 5yr bond price and the 0.58% volatility for the 7yr bond price to get 0.56% as the volatility for the 6.5yr bond We allocate  of the PV to the 5yr bond and (1-  ) of the PV to the 7yr bond

20. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.20 Example continued Suppose that the correlation between movement in the 5yr and 7yr bond prices is 0.6 To match variances This gives  =0.074

21. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.21 Example continued The cash flow of 10,000 in 6.5 years is replaced by in 5 years and by in 7 years. This cash flow mapping preserves value and variance

22. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.22 When Linear Model Can be Used Portfolio of stocks Portfolio of bonds Forward contract on foreign currency Interest-rate swap

23. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.23 The Linear Model and Options (See page 350) Consider a portfolio of options dependent on a single stock price, S. Define and

24. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.24 Linear Model and Options continued As an approximation Similar when there are many underlying market variables where  i is the delta of the portfolio with respect to the i th asset

25. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.25 Example Consider an investment in options on IBM and AT&T. Suppose the stock prices are 120 and 30 respectively and the deltas of the portfolio with respect to the two stock prices are 1,000 and 20,000 respectively As an approximation where  x 1 and  x 2 are the proportional changes in the two stock prices

26. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.26 Skewness The linear model fails to capture skewness in the probability distribution of the portfolio value.

27. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.27 Quadratic Model (See page 352) For a portfolio dependent on a single stock price this becomes

28. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.28 Moments of  P (See page 354)

29. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.29 Quadratic Model continued With many market variables and each instrument dependent on only one where  i and  i are the delta and gamma of the portfolio with respect to the i th variable

30. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.30 Quadratic Model continued When the change in the portfolio value has the form we can calculate the moments of  P analytically if the  x i are assumed to be normal

31. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.31 Quadratic Model continued Once we have done this we can use the Cornish Fisher expansion to calculate fractiles of the distribution of  P

32. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.32 Monte Carlo Simulation (See page 355) The stages are as follows Value portfolio today Sample once from the multivariate distributions of the  x i Use the  x i to determine market variables at end of one day Revalue the portfolio at the end of day

33. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.33 Monte Carlo Simulation Calculate  P Repeat many times to build up a probability distribution for  P VaR is the appropriate fractile of the distribution times square root of N For example, with 1,000 trial the 1 percentile is the 10th worst case.

34. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.34 Speeding Up Monte Carlo Use the quadratic approximation to calculate  P

35. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.35 Historical Simulation (See page 356) Create a database of the daily movements in all market variables. The first simulation trial assumes that the percentage changes in all market variables are as on the first day The second simulation trial assumes that the percentage changes in all market variables are as on the second day and so on

36. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.36 Stress Testing (See page 357) This involves testing how well a portfolio performs under some of the most extreme market moves seen in the last 10 to 20 years

37. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.37 Back-Testing (See page 357) Tests how well VaR estimates would have performed in the past We could ask the question: How often was the loss greater than the 99%/10 day VaR?

38. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.38 Principal Components Analysis (See page 357) Suppose that a portfolio depends on a number of related variables (eg interest rates) We define factors as a scenarios where there is a certain movement in each market variable (The movements are known as factor loadings) The observations on the variables can often be largely explained by two or three factors

39. Options, Futures, and Other Derivatives, 4th edition © 1999 by John C. Hull 14.39 Results for Interest Rates (Table 14.4) The first factor is a roughly parallel shift (83.1% of variation explained) The second factor is a twist (10% of variation explained) The third factor is a bowing (2.8% of variation explained)