A class of multivariate copulas based on products of bivariate copulas

Gildas Mazo, Stéphane Girard and Florence Forbes

Journal of Multivariate Analysis, 2015, vol. 140, issue C, 363-376

Abstract: Copulas are a useful tool to model multivariate distributions. While there exist various families of bivariate copulas, much less work has been done when the dimension is higher. We propose a class of multivariate copulas based on products of transformed bivariate copulas. The analytical forms of the copulas within this class allow to naturally associate a graphical structure which helps to visualize the dependencies and to compute the full joint likelihood even in high dimension. Numerical experiments are conducted both on simulated and real data thanks to a dedicated R package.

Keywords: Maximum-likelihood inference; Graphical models; Message-passing algorithm; Multivariate; Copula (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (7)

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DOI: 10.1016/j.jmva.2015.06.001

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