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Sensitivity of the Stability Bound for Ruin Probabilities to Claim Distributions

Author

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  • Aicha Bareche

    (University of Bejaia)

  • Mouloud Cherfaoui

    (University of Bejaia
    University of Biskra)

Abstract

We are interested in the approximation of the ruin probability of a classical risk model using the strong stability method. Particularly, we study the sensitivity of the stability bound for ruin probabilities of two risk models to approach (a simpler ideal model and a complex real one, which must be close in some sense) regarding to different large claims (heavy-tailed distributions). In a first case, we study the impact of the tail of some claim distributions on the quality of this approximation using the strong stability of a Markov chain. In a second case, we look at the sensitivity of the stability bound for the ruin probability regarding to different large claims, using two versions of the strong stability method: strong stability of a Markov chain and strong stability of a Lindley process. In both cases, comparative studies based on numerical examples and simulation results, involving different heavy-tailed distributions, are performed.

Suggested Citation

  • Aicha Bareche & Mouloud Cherfaoui, 2019. "Sensitivity of the Stability Bound for Ruin Probabilities to Claim Distributions," Methodology and Computing in Applied Probability, Springer, vol. 21(4), pages 1259-1281, December.
    • Handle: RePEc:spr:metcap:v:21:y:2019:i:4:d:10.1007_s11009-018-9675-7
    DOI: 10.1007/s11009-018-9675-7
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    References listed on IDEAS

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    1. Vladimir Kalashnikov, 1999. "Bounds for Ruin Probabilities in the Presence of Large Claims and their Comparison," North American Actuarial Journal, Taylor & Francis Journals, vol. 3(2), pages 116-128.
    2. Bolance, Catalina & Guillen, Montserrat & Nielsen, Jens Perch, 2003. "Kernel density estimation of actuarial loss functions," Insurance: Mathematics and Economics, Elsevier, vol. 32(1), pages 19-36, February.
    3. Touazi, A. & Benouaret, Z. & Aissani, D. & Adjabi, S., 2017. "Nonparametric estimation of the claim amount in the strong stability analysis of the classical risk model," Insurance: Mathematics and Economics, Elsevier, vol. 74(C), pages 78-83.
    4. Beirlant, J. & Rachev, S. T., 1987. "The problem of stability in insurance mathematics," Insurance: Mathematics and Economics, Elsevier, vol. 6(3), pages 179-188, July.
    5. Chen, Song Xi, 1999. "Beta kernel estimators for density functions," Computational Statistics & Data Analysis, Elsevier, vol. 31(2), pages 131-145, August.
    6. Embrechts, P. & Veraverbeke, N., 1982. "Estimates for the probability of ruin with special emphasis on the possibility of large claims," Insurance: Mathematics and Economics, Elsevier, vol. 1(1), pages 55-72, January.
    7. Lanpeng Ji & Chunsheng Zhang, 2014. "A Duality Result for the Generalized Erlang Risk Model," Risks, MDPI, vol. 2(4), pages 1-11, November.
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    Cited by:

    1. Jorge Wilson Euphasio Junior & João Vinícius França Carvalho, 2022. "Resseguro e Capital de Solvência: Atenuantes da Probabilidade de Ruína de SeguradorasReinsurance and Solvency Capital: Mitigating Insurance Companies’ Ruin Probability," RAC - Revista de Administração Contemporânea (Journal of Contemporary Administration), ANPAD - Associação Nacional de Pós-Graduação e Pesquisa em Administração, vol. 26(1), pages 200191-2001.

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