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On some multivariate Sarmanov mixed Erlang reinsurance risks: Aggregation and capital allocation

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  • Ratovomirija, Gildas
  • Tamraz, Maissa
  • Vernic, Raluca

Abstract

Following some recent works on risk aggregation and capital allocation for mixed Erlang risks joined by Sarmanov’s multivariate distribution, in this paper we present some closed-form formulas for the same topic by considering, however, a different kernel function for Sarmanov’s distribution, not previously studied in this context. The risk aggregation and capital allocation formulas are derived and numerically illustrated in the general framework of stop-loss reinsurance, and then in the particular case with no stop-loss reinsurance. A discussion of the dependency structure of the considered distribution, based on Pearson’s correlation coefficient, is also presented for different kernel functions and illustrated in the bivariate case.

Suggested Citation

  • Ratovomirija, Gildas & Tamraz, Maissa & Vernic, Raluca, 2017. "On some multivariate Sarmanov mixed Erlang reinsurance risks: Aggregation and capital allocation," Insurance: Mathematics and Economics, Elsevier, vol. 74(C), pages 197-209.
  • Handle: RePEc:eee:insuma:v:74:y:2017:i:c:p:197-209
    DOI: 10.1016/j.insmatheco.2017.03.009
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    References listed on IDEAS

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    5. Simon Lee & X. Lin, 2010. "Modeling and Evaluating Insurance Losses Via Mixtures of Erlang Distributions," North American Actuarial Journal, Taylor & Francis Journals, vol. 14(1), pages 107-130.
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    8. Jan Dhaene & Andreas Tsanakas & Emiliano A. Valdez & Steven Vanduffel, 2012. "Optimal Capital Allocation Principles," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 79(1), pages 1-28, March.
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    Cited by:

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    4. Mohammed, Nawaf & Furman, Edward & Su, Jianxi, 2021. "Can a regulatory risk measure induce profit-maximizing risk capital allocations? The case of conditional tail expectation," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 425-436.
    5. Eric C. K. Cheung & Oscar Peralta & Jae-Kyung Woo, 2021. "Multivariate matrix-exponential affine mixtures and their applications in risk theory," Papers 2201.11122, arXiv.org.
    6. Nadezhda Gribkova & Ričardas Zitikis, 2019. "Weighted allocations, their concomitant-based estimators, and asymptotics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(4), pages 811-835, August.
    7. Nawaf Mohammed & Edward Furman & Jianxi Su, 2021. "Can a regulatory risk measure induce profit-maximizing risk capital allocations? The case of Conditional Tail Expectation," Papers 2102.05003, arXiv.org, revised Aug 2021.
    8. Ignatieva, Katja & Landsman, Zinoviy, 2021. "A class of generalised hyper-elliptical distributions and their applications in computing conditional tail risk measures," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 437-465.
    9. Vernic, Raluca, 2018. "On the evaluation of some multivariate compound distributions with Sarmanov’s counting distribution," Insurance: Mathematics and Economics, Elsevier, vol. 79(C), pages 184-193.
    10. Cheung, Eric C.K. & Peralta, Oscar & Woo, Jae-Kyung, 2022. "Multivariate matrix-exponential affine mixtures and their applications in risk theory," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 364-389.

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