A0824
Title: A de-randomization argument for estimating extreme value parameters of heavy tails
Authors: Joseph Hachem - Toulouse School of Economics (France) [presenting]
Gilles Stupfler - University of Angers (France)
Abdelaati Daouia - Toulouse School of Economics (France)
Abstract: In extreme value analysis, it has recently been shown that one can use a de-randomization trick to replace a random threshold in the estimator of interest with its deterministic counterpart to estimate several extreme risks simultaneously, but only in an i.i.d. context. The aim is to show how this method can be used to handle the estimation of several tail quantities (tail index, expected shortfall, distortion risk measures...) in general dependence/heteroskedasticity/heterogeneity settings under a weighted $L^1$ assumption on the gap between the average distribution of the data and the prevailing distribution. Particularly interesting examples of application include serially dependent but heteroskedastic frameworks.
