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Asymptotic independence and support detection techniques for heavy-tailed multivariate data

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  • Lehtomaa, Jaakko
  • Resnick, Sidney I.

Abstract

One of the central objectives of modern risk management is to find a set of risks where the probability of multiple simultaneous catastrophic events is negligible. That is, risks are taken only when their joint behavior seems sufficiently independent. This paper aims to identify asymptotically independent risks by providing tools for describing dependence structures of multiple risks when the individual risks can obtain very large values.

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  • Lehtomaa, Jaakko & Resnick, Sidney I., 2020. "Asymptotic independence and support detection techniques for heavy-tailed multivariate data," Insurance: Mathematics and Economics, Elsevier, vol. 93(C), pages 262-277.
    • Handle: RePEc:eee:insuma:v:93:y:2020:i:c:p:262-277
    DOI: 10.1016/j.insmatheco.2020.05.002
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    References listed on IDEAS

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    Cited by:

    1. Miriam Hägele & Jaakko Lehtomaa, 2021. "Large Deviations for a Class of Multivariate Heavy-Tailed Risk Processes Used in Insurance and Finance," JRFM, MDPI, vol. 14(5), pages 1-18, May.
    2. Bikramjit Das & Vicky Fasen-Hartmann, 2023. "Measuring risk contagion in financial networks with CoVaR," Papers 2309.15511, arXiv.org, revised May 2025.
    3. Qian Hui & Sidney I. Resnick & Tiandong Wang, 2025. "Classification of Extremal Dependence in Financial Markets via Bootstrap Inference," Papers 2506.04656, arXiv.org.

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