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Hierarchical Archimedean copulas through multivariate compound distributions

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  • Cossette, Hélène
  • Gadoury, Simon-Pierre
  • Marceau, Étienne
  • Mtalai, Itre

Abstract

In this paper, we propose a new hierarchical Archimedean copula construction based on multivariate compound distributions. This new imbrication technique is derived via the construction of a multivariate exponential mixture distribution through compounding. The absence of nesting and marginal conditions, contrarily to the nested Archimedean copulas approach, leads to major advantages, such as a flexible range of possible combinations in the choice of distributions, the existence of explicit formulas for the distribution of the sum, and computational ease in high dimensions. A balance between flexibility and parsimony is targeted. After presenting the construction technique, properties of the proposed copulas are investigated and illustrative examples are given. A detailed comparison with other construction methodologies of hierarchical Archimedean copulas is provided. Risk aggregation under this newly proposed dependence structure is also examined.

Suggested Citation

  • Cossette, Hélène & Gadoury, Simon-Pierre & Marceau, Étienne & Mtalai, Itre, 2017. "Hierarchical Archimedean copulas through multivariate compound distributions," Insurance: Mathematics and Economics, Elsevier, vol. 76(C), pages 1-13.
    • Handle: RePEc:eee:insuma:v:76:y:2017:i:c:p:1-13
    DOI: 10.1016/j.insmatheco.2017.06.001
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    References listed on IDEAS

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    1. Segers, Johan & Uyttendaele, Nathan, 2014. "Nonparametric estimation of the tree structure of a nested Archimedean copula," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 190-204.
    2. Hering, Christian & Hofert, Marius & Mai, Jan-Frederik & Scherer, Matthias, 2010. "Constructing hierarchical Archimedean copulas with Lévy subordinators," Journal of Multivariate Analysis, Elsevier, vol. 101(6), pages 1428-1433, July.
    3. Hofert, Marius, 2011. "Efficiently sampling nested Archimedean copulas," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 57-70, January.
    4. Uyttendaele, Nathan, 2016. "On the estimation of nested Archimedean copulas: A theoretical and an experimental comparison," LIDAM Discussion Papers ISBA 2016005, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Zhu, Wenjun & Wang, Chou-Wen & Tan, Ken Seng, 2016. "Structure and estimation of Lévy subordinated hierarchical Archimedean copulas (LSHAC): Theory and empirical tests," Journal of Banking & Finance, Elsevier, vol. 69(C), pages 20-36.
    6. Okhrin, Ostap & Okhrin, Yarema & Schmid, Wolfgang, 2013. "On the structure and estimation of hierarchical Archimedean copulas," Journal of Econometrics, Elsevier, vol. 173(2), pages 189-204.
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    Cited by:

    1. Chaoubi, Ihsan & Cossette, Hélène & Marceau, Etienne & Robert, Christian Y., 2021. "Hierarchical copulas with Archimedean blocks and asymmetric between-block pairs," Computational Statistics & Data Analysis, Elsevier, vol. 154(C).
    2. Ressel Paul, 2018. "A multivariate version of Williamson’s theorem, ℓ-symmetric survival functions, and generalized Archimedean copulas," Dependence Modeling, De Gruyter, vol. 6(1), pages 356-368, December.
    3. Savinov, Evgeniy & Shamraeva, Victoria, 2023. "On a Rosenblatt-type transformation of multivariate copulas," Econometrics and Statistics, Elsevier, vol. 25(C), pages 39-48.
    4. Mai Jan-Frederik, 2019. "Simulation algorithms for hierarchical Archimedean copulas beyond the completely monotone case," Dependence Modeling, De Gruyter, vol. 7(1), pages 202-214, January.
    5. Cossette, Hélène & Marceau, Etienne & Mtalai, Itre, 2019. "Collective risk models with dependence," Insurance: Mathematics and Economics, Elsevier, vol. 87(C), pages 153-168.

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