Dependence Modeling with Copulas and Their Applications

Dear Colleagues,

In probability and statistics, a copula is a multivariate cumulative distribution function for which the marginal probability distribution of each variable is uniform on the interval [0, 1]. Copulas are used to describe or model the dependence between random variables. They were introduced by Abe Sklar in 1959, and the word comes from the Latin for "link" or "tie", since they relate a multivariate distribution function to its one-dimensional marginals. Copulas have been used widely in quantitative finance to model and minimize tail risk and portfolio-optimization applications.

Copulas are popular in high-dimensional statistical applications as they allow one to easily model and estimate the distribution of random vectors by estimating marginals and copulas separately. There are many parametric copula families available, which usually have parameters that control the strength of dependence.

This Special Issue, ‘Dependence Modeling with Copulas and Their Applications’, aims to collate original research articles as well as comprehensive reviews addressing the theories and applications of copulas in quantitative finance, reliability, hydrology, computer science, etc.

Prof. Dr. Manuel Úbeda-Flores
Prof. Dr. Enrique de Amo
Guest Editors

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