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Nonparametric estimation of the conditional tail copula

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  • Gardes, Laurent
  • Girard, Stéphane

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.

Suggested Citation

  • Gardes, Laurent & Girard, Stéphane, 2015. "Nonparametric estimation of the conditional tail copula," Journal of Multivariate Analysis, Elsevier, vol. 137(C), pages 1-16.
    • Handle: RePEc:eee:jmvana:v:137:y:2015:i:c:p:1-16
    DOI: 10.1016/j.jmva.2015.01.018
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    References listed on IDEAS

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    Cited by:

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    2. Yuri Goegebeur & Armelle Guillou & Jing Qin, 2023. "Robust estimation of the conditional stable tail dependence function," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(2), pages 201-231, April.

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