A moving window approach for nonparametric estimation of the conditional tail index
Statistics Theory
2011-04-06 v1 Methodology
Statistics Theory
Abstract
We present a nonparametric family of estimators for the tail index of a Pareto-type distribution when covariate information is available. Our estimators are based on a weighted sum of the log-spacings between some selected observations. This selection is achieved through a moving window approach on the covariate domain and a random threshold on the variable of interest. Asymptotic normality is proved under mild regularity conditions and illustrated for some weight functions. Finite sample performances are presented on a real data study.
Cite
@article{arxiv.1104.0763,
title = {A moving window approach for nonparametric estimation of the conditional tail index},
author = {L. Gardes and S. Girard},
journal= {arXiv preprint arXiv:1104.0763},
year = {2011}
}