1. Volatility Chapter 9 Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 1

2. Definition of Volatility Suppose that S i is the value of a variable on day i. The volatility per day is the standard deviation of ln ( S i / S i -1 ) Normally days when markets are closed are ignored in volatility calculations (see Business Snapshot 9.1, page 177) The volatility per year is times the daily volatility Variance rate is the square of volatility Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 2

3. Implied Volatilities Of the variables needed to price an option the one that cannot be observed directly is volatility We can therefore imply volatilities from market prices and vice versa Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 3

4. VIX Index: A Measure of the Implied Volatility of the S&P 500 (Figure 9.1, page 178) Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 4

5. Are Daily Changes in Exchange Rates Normally Distributed? Table 9.2, page 181 Real World (%)Normal Model (%) >1 SD25.0431.73 >2SD5.274.55 >3SD1.340.27 >4SD0.290.01 >5SD0.080.00 >6SD0.030.00 Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 5

6. Heavy Tails Daily exchange rate changes are not normally distributed The distribution has heavier tails than the normal distribution It is more peaked than the normal distribution This means that small changes and large changes are more likely than the normal distribution would suggest Many market variables have this property, known as excess kurtosis Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 6

7. Normal and Heavy-Tailed Distribution Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 7

8. Alternatives to Normal Distributions: The Power Law (See page 182) Prob( v > x ) = Kx -  This seems to fit the behavior of the returns on many market variables better than the normal distribution Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 8

9. Log-Log Test for Exchange Rate Data Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 9

10. Standard Approach to Estimating Volatility Define  n as the volatility per day between day n -1 and day n, as estimated at end of day n -1 Define S i as the value of market variable at end of day i Define u i = ln ( S i /S i-1 ) Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 10

11. Simplifications Usually Made in Risk Management Define u i as (S i −S i-1 )/S i-1 Assume that the mean value of u i is zero Replace m-1 by m This gives Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 11

12. Weighting Scheme Instead of assigning equal weights to the observations we can set Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 12

13. ARCH(m) Model In an ARCH(m) model we also assign some weight to the long-run variance rate, V L : Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 13

14. EWMA Model (page 186) In an exponentially weighted moving average model, the weights assigned to the u 2 decline exponentially as we move back through time This leads to Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 14

15. Attractions of EWMA Relatively little data needs to be stored We need only remember the current estimate of the variance rate and the most recent observation on the market variable Tracks volatility changes RiskMetrics uses = 0.94 for daily volatility forecasting Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 15

16. GARCH (1,1), page 188 In GARCH (1,1) we assign some weight to the long-run average variance rate Since weights must sum to 1  Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 16

17. GARCH (1,1) continued Setting  V L the GARCH (1,1) model is and Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 17

18. Example Suppose The long-run variance rate is 0.0002 so that the long-run volatility per day is 1.4% Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 18

19. Example continued Suppose that the current estimate of the volatility is 1.6% per day and the most recent percentage change in the market variable is 1%. The new variance rate is The new volatility is 1.53% per day Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 19

20. GARCH (p,q) Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 20

21. Other Models Many other GARCH models have been proposed For example, we can design a GARCH models so that the weight given to u i 2 depends on whether u i is positive or negative Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 21

22. Variance Targeting One way of implementing GARCH(1,1) that increases stability is by using variance targeting We set the long-run average volatility equal to the sample variance Only two other parameters then have to be estimated Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 22

23. Maximum Likelihood Methods In maximum likelihood methods we choose parameters that maximize the likelihood of the observations occurring Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 23

24. Example 1 (page 190) We observe that a certain event happens one time in ten trials. What is our estimate of the proportion of the time, p, that it happens? The probability of the outcome is We maximize this to obtain a maximum likelihood estimate: p =0.1 Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 24

25. Example 2 (page 190-191) Estimate the variance of observations from a normal distribution with mean zero Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 25

26. Application to GARCH (1,1) We choose parameters that maximize Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 26

27. Calculations for Yen Exchange Rate Data (Table 9.4, page 192) Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 27 Day SiSi uiui v i =  i 2 -ln v i -u i 2 /v i 10.007728 20.0077790.006599 30.007746-0.0042420.000043559.6283 40.0078160.0090370.000041988.1329 50.0078370.0026870.000044559.8568 …. 24230.0084950.0001440.000084179.3824 22063.5833

28. Daily Volatility of Yen: 1988-1997 Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 28

29. Forecasting Future Volatility (Equation 9.14, page 195) A few lines of algebra shows that To estimate the volatility for an option lasting T days we must integrate this from 0 to T Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 29

30. Forecasting Future Volatility cont The volatility per year for an option lasting T days is Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 30

31. Volatility Term Structures (Equation 9.16, page 197) The GARCH (1,1) model allows us to predict volatility term structures changes When  (0) changes by  (0), GARCH (1,1) predicts that  ( T ) changes by Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull 2009 31