Dependence properties and Bayesian inference for asymmetric multivariate copulas

Julyan Arbel (1) , Marta Crispino (1) , Stéphane Girard (1)

1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems

We study a broad class of asymmetric copulas introduced by Liebscher (2008) as a combination of multiple-usually symmetric-copulas. The main thrust of the paper is to provide new theoretical properties including exact tail dependence expressions and stability properties. A subclass of Liebscher copulas obtained by combining Fréchet copulas is studied in more details. We establish further dependence properties for copulas of this class and show that they are characterized by an arbitrary number of singular components. Furthermore, we introduce a novel iterative representation for general Liebscher copulas which de facto insures uniform margins, thus relaxing a constraint of Liebscher's original construction. Besides, we show that this iterative construction proves useful for inference by developing an Approximate Bayesian computation sampling scheme. This inferential procedure is demonstrated on simulated data.

Mots clés: Approximate Bayesian computation; asymmetric copulas; dependence properties; singular components

Domaines: Statistiques [math.ST]

Julyan Arbel, Marta Crispino, Stéphane Girard. Dependence properties and Bayesian inference for asymmetric multivariate copulas. 2018. ⟨hal-01963975v1⟩