A flexible and tractable class of one-factor copulas

Gildas Mazo 1 Stephane Girard 1 Florence Forbes 1
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 : Copulas are a useful tool to model multivariate distributions. While there exist various families of bivariate copulas, the construction of flexible and yet tractable copulas suitable for high-dimensional applications is much more challenging. This is even more true if one is concerned with the analysis of extreme values. In this paper, we construct a class of one-factor copulas and a family of extreme-value copulas well suited for high-dimensional applications and exhibiting a good balance between tractability and flexibility. The inference for these copulas is performed by using a least-squares estimator based on dependence coefficients. The modeling capabilities of the copulas are illustrated on simulated and real datasets.
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Statistics and Computing, Springer Verlag (Germany), 2016, 26 (5), pp.965-979. 〈10.1007/s11222-015-9580-7〉
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Gildas Mazo, Stephane Girard, Florence Forbes. A flexible and tractable class of one-factor copulas. Statistics and Computing, Springer Verlag (Germany), 2016, 26 (5), pp.965-979. 〈10.1007/s11222-015-9580-7〉. 〈hal-00979147v3〉

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