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Distortion measures and homogeneous financial derivatives

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  • Major, John A.

Abstract

This paper extends the evaluation and allocation of distortion risk measures to apply to arbitrary homogeneous operators (“financial derivatives,” e.g. reinsurance recovery) of primitive portfolio elements (e.g. line of business losses). Previous literature argues that the allocation of the portfolio measure to the financial derivative should take the usual special-case form of Aumann–Shapley, being a distortion-weighted “co-measure” expectation. This is taken here as the definition of the “distorted” measure of the derivative “with respect to” the underlying portfolio. Due to homogeneity, the subsequent allocation of the derivative’s value to the primitive elements of the portfolio again follows Aumann–Shapley, in the form of the exposure gradient of the distorted measure. However, the gradient in this case is seen to consist of two terms. The first is the familiar distorted expectation of the gradient of the financial derivative with respect to exposure to the element. The second term involves the conditional covariance of the financial derivative with the element. Sufficient conditions for this second term to vanish are provided. A method for estimating the second term in a simulation framework is proposed. Examples are provided.

Suggested Citation

  • Major, John A., 2018. "Distortion measures and homogeneous financial derivatives," Insurance: Mathematics and Economics, Elsevier, vol. 79(C), pages 82-91.
  • Handle: RePEc:eee:insuma:v:79:y:2018:i:c:p:82-91
    DOI: 10.1016/j.insmatheco.2017.12.006
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    References listed on IDEAS

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    Cited by:

    1. Pesenti, Silvana M. & Tsanakas, Andreas & Millossovich, Pietro, 2018. "Euler allocations in the presence of non-linear reinsurance: Comment on Major (2018)," Insurance: Mathematics and Economics, Elsevier, vol. 83(C), pages 29-31.
    2. Bauer, Daniel & Kamiya, Shinichi & Ping, Xiaohu & Zanjani, George, 2019. "Dynamic capital allocation with irreversible investments," Insurance: Mathematics and Economics, Elsevier, vol. 85(C), pages 138-152.
    3. Boonen, Tim J. & De Waegenaere, Anja & Norde, Henk, 2020. "A generalization of the Aumann–Shapley value for risk capital allocation problems," European Journal of Operational Research, Elsevier, vol. 282(1), pages 277-287.
    4. John A. Major & Stephen J. Mildenhall, 2020. "Pricing and Capital Allocation for Multiline Insurance Firms With Finite Assets in an Imperfect Market," Papers 2008.12427, arXiv.org.
    5. Akif Ince & Ilaria Peri & Silvana Pesenti, 2021. "Risk contributions of lambda quantiles," Papers 2106.14824, arXiv.org, revised Nov 2022.

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    More about this item

    Keywords

    Distortion measures; Financial derivatives; Capital allocation; Aumann–Shapley; Reinsurance;
    All these keywords.

    JEL classification:

    • C71 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Cooperative Games
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

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