TY  - GEN
T1  - Extreme quantile estimation for dependent data with applications to finance
A1  - Drees,Holger
Y1  - 2011/12/02
N2  - The asymptotic normality of a class of estimators for extreme quantiles is established under mild structural conditions on the observed stationary \beta-mixing time series. Consistent estimators of the asymptotic variance are introduced, which render possible the construction of asymptotic confidence intervals for the extreme quantiles. Moreover, it is shown that many well-known time series models satisfy our conditions. Then the theory is applied to a time series of returns of a stock index. Finally, the finite sample behavior of the proposed confidence intervals is examined in a simulation study. It turns out that for most time series models under consideration the actual coverage probability is pretty close to the nominal level if the sample fraction used for estimation is chosen appropriately. 
CY  - Saarbrücken
PB  - Universitäts- und Landesbibliothek
AD  - Postfach 151141, 66041 Saarbrücken
UR  - http://scidok.sulb.uni-saarland.de/volltexte/2011/4393
ER  -