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On the Identification of the Riskiest Directional Components from Multivariate Heavy-Tailed Data

Author

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  • Miriam Hägele

    (Department of Mathematics and Statistics, University of Helsinki, 00014 Helsinki, Finland
    These authors contributed equally to this work.)

  • Jaakko Lehtomaa

    (Department of Mathematics and Statistics, University of Helsinki, 00014 Helsinki, Finland
    These authors contributed equally to this work.)

Abstract

In univariate data, there exist standard procedures for identifying dominating features that produce the largest number of observations. However, in the multivariate setting, the situation is quite different. This paper aims to provide tools and methods for detecting dominating directional components in multivariate data. We study general heavy-tailed multivariate random vectors in dimension d ≥ 2 and present procedures that can be used to explain why the data are heavy-tailed. This is achieved by identifying the set of the riskiest directional components. The results are of particular interest in insurance when setting reinsurance policies, and in finance when hedging a portfolio of multiple assets.

Suggested Citation

  • Miriam Hägele & Jaakko Lehtomaa, 2023. "On the Identification of the Riskiest Directional Components from Multivariate Heavy-Tailed Data," Risks, MDPI, vol. 11(7), pages 1-18, July.
    • Handle: RePEc:gam:jrisks:v:11:y:2023:i:7:p:130-:d:1193344
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    References listed on IDEAS

    as
    1. Cline, Daren B. H. & Resnick, Sidney I., 1992. "Multivariate subexponential distributions," Stochastic Processes and their Applications, Elsevier, vol. 42(1), pages 49-72, August.
    2. Søren Asmussen & Jaakko Lehtomaa, 2017. "Distinguishing Log-Concavity from Heavy Tails," Risks, MDPI, vol. 5(1), pages 1-14, February.
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