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Conference papers

A parsimonious multivariate copula for tail dependence modeling

Gildas Mazo 1 Stéphane Girard 1 Florence Forbes 1
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology, LJK - Laboratoire Jean Kuntzmann, Inria Grenoble - Rhône-Alpes
Abstract : Copulas are increasingly studied both in theory and practice as they are a convenient tool to construct multivariate distribution functions. However the material essentially covers the bi-variate case while in applications the number of variables is much higher. Furthermore, when one wants to take into account tail dependence, a desirable property is to have enough flexibility in the tails while avoiding the exponential growth of the number of parameters. We propose in this communication a one-factor model which exhibits this feature.
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Submitted on : Thursday, September 19, 2013 - 10:36:54 AM
Last modification on : Tuesday, October 19, 2021 - 11:13:06 PM
Long-term archiving on: : Friday, December 20, 2013 - 3:06:34 PM


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  • HAL Id : hal-00863540, version 1



Gildas Mazo, Stéphane Girard, Florence Forbes. A parsimonious multivariate copula for tail dependence modeling. EVT 2013 - Extremes in Vimeiro Today, Sep 2013, Vimeiro, Portugal. ⟨hal-00863540⟩



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