Please use this identifier to cite or link to this item: doi:10.22028/D291-26218
Title: Extreme quantile estimation for dependent data with applications to finance
Author(s): Drees, Holger
Language: English
Year of Publication: 2002
Free key words: confidence interval
GARCH model
tail empirical quantile function
DDC notations: 510 Mathematics
Publikation type: Other
Abstract: 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.
Link to this record: urn:nbn:de:bsz:291-scidok-43932
Series name: Preprint / Fachrichtung Mathematik, Universität des Saarlandes
Series volume: 68
Date of registration: 2-Dec-2011
Faculty: MI - Fakultät für Mathematik und Informatik
Department: MI - Mathematik
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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