Presentation on theme: "2015-4-111 Estimating Volatilities and Correlations Chapter 21."— Presentation transcript:
2015-4-111 Estimating Volatilities and Correlations Chapter 21
2015-4-112 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
2015-4-113 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
2015-4-114 Weighting Scheme Instead of assigning equal weights to the observations we can set
2015-4-115 ARCH(m) Model In an ARCH(m) model we also assign some weight to the long-run variance rate, V:
2015-4-116 EWMA Model In an exponentially weighted moving average model, the weights assigned to the u 2 decline exponentially as we move back through time.
2015-4-117 Especially, if
2015-4-118 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
2015-4-119 GARCH (1,1) In GARCH (1,1) we assign some weight to the long-run average variance rate Since weights must sum to 1
2015-4-1110 GARCH (1,1) continued Setting V the GARCH (1,1) model is and
2015-4-1111 Example Suppose the long-run variance rate is 0.0002 so that the long-run volatility per day is 1.4%
2015-4-1112 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
2015-4-1113 GARCH (p,q)
2015-4-1114 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.
2015-4-1115 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.
2015-4-1116 Maximum Likelihood Methods In maximum likelihood methods we choose parameters that maximize the likelihood of the observations occurring
2015-4-1117 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
2015-4-1118 Example 2 （ Suppose the variance is constant) Estimate the variance of observations from a normal distribution with mean zero
2015-4-1119 Application to GARCH We choose parameters that maximize
2015-4-1120 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)
2015-4-1121 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.
2015-4-1122 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
2015-4-1123 Forecasting Future Volatility A few lines of algebra shows that The variance rate for an option expiring on day m is
2015-4-1124 Forecasting Future Volatility continued (equation 19.4, page 473)
2015-4-1125 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) 103050100500 Volatility increase (%) 0.840.610.460.270.06
2015-4-1126 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
2015-4-1127 Correlations continued Under GARCH (1,1) cov n = + u n-1 v n-1 + cov n -1
2015-4-1128 Positive semi-definite Condition A variance-covariance matrix, is internally consistent if the positive semi-definite condition for all vectors w is satisfied.
2015-4-1129 Example The variance covariance matrix is not internally consistent