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On a Family of Log-Gamma-Generated Archimedean Copulas

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  • Yaming Yang
  • Shuanming Li

Abstract

Modeling dependence structure among various risks, especially the measure of tail dependence and the aggregation of risks, is crucial for risk management. In this article, we present an extension to the traditional one-parameter Archimedean copulas by integrating the log-gamma-generated (LGG) margins. This class of novel multivariate distribution can better capture the tail dependence. The distortion effect on the classic one-parameter Archimedean copulas is well exhibited and the analytical expression of the sum of bivariate margins is proposed. The model provides a flexible way to capture tail risks and aggregate portfolio losses. Sufficient conditions for constructing a legitimate d-dimensional LGG Archimedean copula as well as the simulation framework are also proposed. Furthermore, two applications of this model are presented using concrete insurance datasets.

Suggested Citation

  • Yaming Yang & Shuanming Li, 2022. "On a Family of Log-Gamma-Generated Archimedean Copulas," North American Actuarial Journal, Taylor & Francis Journals, vol. 26(1), pages 123-142, January.
    • Handle: RePEc:taf:uaajxx:v:26:y:2022:i:1:p:123-142
    DOI: 10.1080/10920277.2020.1856687
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    Cited by:

    1. Morris, Heather & Blewitt, Claire & Savaglio, Melissa & Halfpenny, Nick & Carolan, Erin & Miller, Robyn & Skouteris, Helen, 2022. "Using a realist lens to understand the Victorian Family Preservation and Reunification Response in the first year of implementation — Towards a better understanding of practice," Children and Youth Services Review, Elsevier, vol. 143(C).
    2. Dasgupta, Kabir & Diegmann, André & Kirchmaier, Tom & Plum, Alexander, 2022. "The gender reveal: The effect of sons on young fathers’ criminal behavior and labor market activities," Labour Economics, Elsevier, vol. 78(C).

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