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2015-4-111 Estimating Volatilities and Correlations Chapter 21.

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Presentation on theme: "2015-4-111 Estimating Volatilities and Correlations Chapter 21."— Presentation transcript:

1 Estimating Volatilities and Correlations Chapter 21

2 Standard Approach to Estimating Volatility  Define  n as the volatility per day on 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 ), an unbiased estimate is

3 Simplifications Usually Made  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

4 Weighting Scheme Instead of assigning equal weights to the observations we can set

5 ARCH(m) Model In an ARCH(m) model we also assign some weight to the long-run variance rate, V:

6 EWMA Model  In an exponentially weighted moving average model, the weights assigned to the u 2 decline exponentially as we move back through time.

7  Especially, if

8 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  JP Morgan use = 0.94 for daily volatility forecasting

9 GARCH (1,1) In GARCH (1,1) we assign some weight to the long-run average variance rate Since weights must sum to 1 

10 GARCH (1,1) continued Setting  V the GARCH (1,1) model is and

11 Example  Suppose  the long-run variance rate is so that the long-run volatility per day is 1.4%

12 Example continued  Suppose that the current estimate of the volatility is 1.6% per day and the most recent proportional change in the market variable is 1%.  The new variance rate is The new volatility is 1.53% per day

13 GARCH (p,q)

14 Mean Reversion  The GARCH(1,1) is equivalent to a model where the variance V follows the stochastic process  GARCH(1,1) incorporates mean reversion, EWMA does not. When is negative, GARCH(1,1) is not stable, and we should use EWMA.

15 Other Models  We can design GARCH models so that the weight given to u i 2 depends on whether u i is positive or negative.

16 Maximum Likelihood Methods  In maximum likelihood methods we choose parameters that maximize the likelihood of the observations occurring

17 Example 1  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

18 Example 2 ( Suppose the variance is constant) Estimate the variance of observations from a normal distribution with mean zero

19 Application to GARCH We choose parameters that maximize

20 Excel Application (Table 21.1, page 477)  Start with trial values of , , and   Update variances  Calculate  Use solver to search for values of , , and  that maximize this objective function  Important note: set up spreadsheet so that you are searching for three numbers that are the same order of magnitude (See page 478)

21 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.

22 How Good is the Model?  We compare the autocorrelation of the u i ’s with the autocorrelation of the u i /  i  The Ljung-Box statistic tests for autocorrelation

23 Forecasting Future Volatility A few lines of algebra shows that The variance rate for an option expiring on day m is

24 Forecasting Future Volatility continued (equation 19.4, page 473)

25 Volatility Term Structures (Table 21.4)  The GARCH (1,1) suggests that, when calculating vega, we should shift the long maturity volatilities less than the short maturity volatilities  Impact of 1% change in instantaneous volatility for Japanese yen example: Option Life (days) Volatility increase (%)

26 Correlations  Define u i =(U i -U i-1 )/U i-1 and v i =(V i -V i-1 )/V i-1  Also  u,n : daily vol of U calculated on day n -1  v,n : daily vol of V calculated on day n -1 cov n : covariance calculated on day n -1

27 Correlations continued Under GARCH (1,1) cov n =  +  u n-1 v n-1 +  cov n -1

28 Positive semi-definite Condition A variance-covariance matrix,  is internally consistent if the positive semi-definite condition for all vectors w is satisfied.

29 Example The variance covariance matrix is not internally consistent


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