A copula to handle tail dependence in high dimension

Gildas Mazo 1, * Stephane Girard 1, * Florence Forbes 1, *
* Corresponding author
1 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 concept of copula is a useful tool to model multivariate distributions but the construction of tail dependent high dimensional copulas remains a challenging problem. We propose a new copula constructed by introducing a latent factor. Conditional independence with respect to this factor and the use of a nonparametric class of bivariate copulas lead to interesting properties like explicitness, flexibility and parsimony. We propose a pairwise moment-based inference procedure and prove asymptotic normality of our estimator. Finally we illustrate our model on simulated and real data.
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Conference papers
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https://hal.inria.fr/hal-00915690
Contributor : Stephane Girard <>
Submitted on : Monday, December 9, 2013 - 11:36:05 AM
Last modification on : Wednesday, April 11, 2018 - 1:57:49 AM

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Gildas Mazo, Stephane Girard, Florence Forbes. A copula to handle tail dependence in high dimension. ERCIM 2013 - 6th International Conference of the ERCIM WG on Computational and Methodological Statistics, Dec 2013, London, United Kingdom. ⟨hal-00915690⟩

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