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Nonparametric estimation of the conditional tail copula

Laurent Gardes 1 Stéphane Girard 2
2 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : The tail copula is widely used to describe the dependence in the tail of multivariate distributions. In some situations such as risk management, the dependence structure may 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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Laurent Gardes, Stéphane Girard. Nonparametric estimation of the conditional tail copula. Journal of Multivariate Analysis, Elsevier, 2015, 137, pp.1-16. ⟨10.1016/j.jmva.2015.01.018⟩. ⟨hal-00964514v3⟩

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