The family of multivariate beta copulas revisited

Enagnon Narcisse Agbangla (), Jean-François Quessy () and Louis-Paul Rivest ()
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Enagnon Narcisse Agbangla: Université Laval
Jean-François Quessy: Université du Québec à Trois-Rivières
Louis-Paul Rivest: Université Laval

Annals of the Institute of Statistical Mathematics, 2025, vol. 77, issue 5, No 6, 757-786

Abstract: Abstract This article sheds new lights on the family of multivariate beta copulas that arises as the dependence structures of the multivariate generalized beta distribution of the second type. In particular, simple formulas for the computation of Kendall’s measure of association are derived and the asymmetry properties are investigated. Also, the multivariate extreme-value attractor of the beta copula is identified and it is shown that the beta family is closed under conditioning and belongs to the class of one-factor copulas. The sampling properties of the rank-based maximum-likelihood estimator are investigated with simulations and the usefulness of the beta copulas for the modeling of multivariate datasets is illustrated on triathlon data.

Keywords: Asymmetric dependence; Conditional copula; Extreme-value attractor; Kendall’s tau; One-factor copulas; Vine decomposition (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10463-025-00931-2

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