Calculating quantile risk measures for financial time series using extreme value theory

We consider the estimation of quantiles in the tail of the marginal distribution of nancial return series, using extreme value statistical methods based on the limiting distribution for block maxima of stationary time series. A simple methodology for quanti cation of worst case scenarios, such as ten or twenty year losses is proposed. We validate methods on a simulated series from an ARCH(1) process showing some of the features of real nancial data, such as fat tails and clustered extreme values; we then analyse daily log returns on a share price