Multivariate Tail Estimation: Conditioning on an extreme event

Abstract

We consider regularly varying random vectors. Our goal is to estimate in a non-parametric way some characteristics related to conditioning on an extreme event, like the tail dependence coefficient. We introduce a quasi-spectral decomposition that allow to improve efficiency of estimators. Asymptotic normality of estimators is based on weak convergence of tail empirical processes. Theoretical results are supported by simulation studies.

arXiv comment8 figures

References in corpus (3)

• The tail empirical process for some long memory sequences
• Estimation of conditional laws given an extreme component
• Statistics for Tail Processes of Markov Chains