The official history of copulas begins in 1959 with Sklar [1959]; but, as is often the case in
Mathematics, for groundbreaking results there are forerunners and precedents. These latter …

Copulas are used to specify dependence between two or more random variables. The last
few years have seen a surge of developments of parametric models for copulas. Here, we …

Looking at bivariate copulas from the perspective of conditional distributions and
considering weak convergence of almost all conditional distributions yields the notion of …

Tail dependence refers to clustering of extreme events. In the context of financial risk
management, the clustering of high-severity risks has a devastating effect on the well-being …

A way to transform a given copula by means of a univariate function is presented. The
resulting copula can be interpreted as the result of a global shock affecting all the …

The Marshall-Olkin (MO) copula model has emerged as the standard tool for capturing
dependence between components in failure analysis in reliability. In this model, shocks …

This paper introduces a new class of copulas and shows its relevance for applications. In
particular, a stochastic interpretation in terms of a system of dependence components …

The omnipotence of copulas when modeling dependence given marginal distributions in a
multivariate stochastic situation is assured by the Sklar's theorem. Montes et al.(2015) …

In this article, we show a limit result for the reliability function of a system—that is, the
probability that the whole system is still operational after a certain given time—when the …

Copulas have become an important tool in the modern best practice Enterprise Risk
Management, often supplanting other approaches to modelling stochastic dependence …