Adapting extreme value statistics to nancial time series: dealing with bias and serial dependence

We handle two major issues in applying extreme value analysis to nancial time series, bias and serial dependence, jointly. This is achieved by studying bias correction method when observations exhibit weakly serial dependence, namely the β−mixing series. For estimating the extreme value index, we propose an asymptotically unbiased estimator and prove its asymptotic normality under the β−mixing condition. The bias correction procedure and the dependence structure have an interactive impact on the asymptotic variance of the estimator. Then, we construct an asymptotically unbiased estimator of high quantiles. Simulations show that nite sample performance of the estimators re ects their theoretical properties. We apply the new method to estimate the Value-at-Risk of the daily return on the Dow Jones Industrial Average Index.