Parametric dependence between random vectors via copula-based divergence measures

Steven De Keyser and Irène Gijbels

Journal of Multivariate Analysis, 2024, vol. 203, issue C

Abstract: This article proposes copula-based dependence quantification between multiple groups of random variables of possibly different sizes via the family of Φ-divergences. An axiomatic framework for this purpose is provided, after which we focus on the absolutely continuous setting assuming copula densities exist. We consider parametric and semi-parametric frameworks, discuss estimation procedures, and report on asymptotic properties of the proposed estimators. In particular, we first concentrate on a Gaussian copula approach yielding explicit and attractive dependence coefficients for specific choices of Φ, which are more amenable for estimation. Next, general parametric copula families are considered, with special attention to nested Archimedean copulas, being a natural choice for dependence modelling of random vectors. The results are illustrated by means of examples. Simulations and a real-world application on financial data are provided as well.

Keywords: Hellinger distance; Mutual information; Nested Archimedean copula; Normal scores rank correlation; ϕ-divergence (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.jmva.2024.105336

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