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Dependence properties and Bayesian inference for asymmetric multivariate copulas

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  • Arbel, Julyan
  • Crispino, Marta
  • Girard, Stéphane

Abstract

We study a broad class of asymmetric copulas introduced by Liebscher (2008) as a combination of multiple – usually symmetric – copulas. The main thrust of the paper is to provide new theoretical properties including exact tail dependence expressions and stability properties. A subclass of Liebscher copulas obtained by combining comonotonic copulas is studied in more detail.We establish further dependence properties for copulas of this class and show that they are characterized by an arbitrary number of singular components. Furthermore, we introduce a novel iterative representation for general Liebscher copulas which de facto insures uniform margins, thus relaxing a constraint of Liebscher’s original construction. Besides, we show that this iterative construction proves useful for inference by developing an Approximate Bayesian computation sampling scheme. This inferential procedure is demonstrated on simulated data and is compared to a likelihood-based approach in a setting where the latter is available.

Suggested Citation

  • Arbel, Julyan & Crispino, Marta & Girard, Stéphane, 2019. "Dependence properties and Bayesian inference for asymmetric multivariate copulas," Journal of Multivariate Analysis, Elsevier, vol. 174(C).
    • Handle: RePEc:eee:jmvana:v:174:y:2019:i:c:s0047259x18306547
    DOI: 10.1016/j.jmva.2019.06.008
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    References listed on IDEAS

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    1. Mazo, Gildas & Girard, Stéphane & Forbes, Florence, 2015. "A class of multivariate copulas based on products of bivariate copulas," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 363-376.
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    1. Savinov, Evgeniy & Shamraeva, Victoria, 2023. "On a Rosenblatt-type transformation of multivariate copulas," Econometrics and Statistics, Elsevier, vol. 25(C), pages 39-48.

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