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Statistical Inference For Copulas In High Dimensions: A Simulation Study

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  • Embrechts, Paul
  • Hofert, Marius

Abstract

Statistical inference for copulas has been addressed in various research papers. Due to the complicated theoretical results, studies have been carried out mainly in the bivariate case, be it properties of estimators or goodness-of-fit tests. However, from a practical point of view, higher dimensions are of interest. This work presents the results of large-scale simulation studies with particular focus on the question to what extent dimensionality influences point and interval estimators.

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  • Embrechts, Paul & Hofert, Marius, 2013. "Statistical Inference For Copulas In High Dimensions: A Simulation Study," ASTIN Bulletin, Cambridge University Press, vol. 43(2), pages 81-95, May.
    • Handle: RePEc:cup:astinb:v:43:y:2013:i:02:p:81-95_00
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    Cited by:

    1. Di Bernardino Elena & Rullière Didier, 2013. "On certain transformations of Archimedean copulas: Application to the non-parametric estimation of their generators," Dependence Modeling, De Gruyter, vol. 1, pages 1-36, October.
    2. Eling, Martin & Jung, Kwangmin, 2018. "Copula approaches for modeling cross-sectional dependence of data breach losses," Insurance: Mathematics and Economics, Elsevier, vol. 82(C), pages 167-180.
    3. Vadim Semenikhine & Edward Furman & Jianxi Su, 2018. "On a Multiplicative Multivariate Gamma Distribution with Applications in Insurance," Risks, MDPI, vol. 6(3), pages 1-20, August.

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