shortcoming in two ways. First, to introduce copula models and aspects of copula theory that are especially relevant for a …Copula models have become one of the most widely used tools in the applied modelling of multivariate data. Similarly … limited use of Bayesian approaches in the formulation and estimation of copula models. This article aims to address this …

for the marginal distributions while introducing non-normal dependence using copulas. In an application of the copula … the Frank copula outperforms the standard bivariate probit model …

copula generators by construction. This makes sampling straightforward. Moreover, I provide details on a particular …

Mixture Copula whose parameters and weights follow well defined dynamic processes. Both approaches improve the Copulas …

, statistical theory proposes a powerful tool, named copula function, to model flexible multivariate distributions in order to …

-tailed phenomena. This paper characterizes the MRV distribution through the upper tail dependence index of the copula associated with … of the copula is necessary and sufficient for a random vector with regularly varying univariate marginals to have a MRV …

We introduce a statistical test for comparing the predictive accuracy of competing copula specifications in … copula is favored over Gaussian, Gumbel and Clayton copulas. This suggests that these exchange rate returns are characterized …

This paper presents the role of copula functions in the theory of aggregation operators. In this context we are … also a decomposition of supermodular binary aggregation operators and copulae …

, and a copula for their interaction. Our decomposition model is able to incorporate important nonlinearities in excess …

This paper presents a flexible new family of copula-based multivariate models designed for handling large numbers of …