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Nonparametric estimation of the conditional tail copula

Laurent Gardes 1 Stephane Girard 2
2 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : The tail copula is widely used to describe the amount of extremal dependence of a multivariate distribution. In some situations such as risk management, the dependence structure can be linked with some covariate. The tail copula thus depends on this covariate and is referred to as the conditional tail copula. The aim of this paper is to propose a nonparametric estimator of the conditional tail copula and to establish its asymptotic normality. Some illustrations are presented both on simulated and real datasets.
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Contributor : Laurent Gardes <>
Submitted on : Tuesday, September 16, 2014 - 9:26:23 AM
Last modification on : Tuesday, February 9, 2021 - 3:20:37 PM
Long-term archiving on: : Wednesday, December 17, 2014 - 10:26:04 AM


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  • HAL Id : hal-00964514, version 2


Laurent Gardes, Stephane Girard. Nonparametric estimation of the conditional tail copula. 2014. ⟨hal-00964514v2⟩



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