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The class of copulas arising from squared distributions: Properties and inference

Jean-François Quessy and Martin Durocher

Econometrics and Statistics, 2019, vol. 12, issue C, 148-166

Abstract: A very general class of multivariate copulas is introduced. These copulas arise as the dependence structures that can be extracted from random vectors whose components are squared. The main theoretical properties of the new models are investigated and general formulas for the Kendall, Spearman and tail dependence measures are derived. The construction gives birth to new dependence models, including radially asymmetric versions of popular bivariate copulas like the Plackett, Frank and Normal dependence structures, as well as to the multivariate copulas of normal variance mixture models; the latter models are suitable in arbitrary dimensions and thus are attractive for multivariate, possibly high-dimensional, asymmetric dependence modeling. Suitably adapted parameter estimation strategies are also proposed and their properties are investigated with simulations. The newly introduced models and inferential tools are illustrated on the Nutrient dataset.

Keywords: Inversion of Kendall’s tau estimator; Multivariate pairwise distributions; Pseudo likelihood estimators; Radial asymmetry; Upper tail dependence (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1016/j.ecosta.2019.02.002

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