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Nonparametric Estimation of the Probability of Ruin

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  • Frees, Edward W.

Abstract

The finite and infinite horizon time probability of ruin are important parameters in the study of actuarial risk theory. This paper introduces procedures for directly estimating these key parameters from a random sample of observations without assumptions as to the parametric form of the distribution from which the observations arise. The estimators introduced apply to most of the classical models in which ruin probabilities are used and also apply to a much broader class of models. The procedures are based on the concept of sample reuse, an old idea in statistics which is becoming more popular due to the widespread availability of high speed computers. In this paper, the almost sure consistency of the estimators is established. Further, finite sample properties of the estimators are investigated in a simulation study.

Suggested Citation

  • Frees, Edward W., 1986. "Nonparametric Estimation of the Probability of Ruin," ASTIN Bulletin, Cambridge University Press, vol. 16(S1), pages 81-90, April.
    • Handle: RePEc:cup:astinb:v:16:y:1986:i:s1:p:s81-s90_01
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    Citations

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    Cited by:

    1. Loisel, Stéphane & Mazza, Christian & Rullière, Didier, 2008. "Robustness analysis and convergence of empirical finite-time ruin probabilities and estimation risk solvency margin," Insurance: Mathematics and Economics, Elsevier, vol. 42(2), pages 746-762, April.
    2. Honglong You & Yuan Gao, 2019. "Non-Parametric Threshold Estimation for the Wiener–Poisson Risk Model," Mathematics, MDPI, vol. 7(6), pages 1-11, June.
    3. Loisel, Stéphane & Mazza, Christian & Rullière, Didier, 2009. "Convergence and asymptotic variance of bootstrapped finite-time ruin probabilities with partly shifted risk processes," Insurance: Mathematics and Economics, Elsevier, vol. 45(3), pages 374-381, December.
    4. You, Honglong & Guo, Junyi & Jiang, Jiancheng, 2020. "Interval estimation of the ruin probability in the classical compound Poisson risk model," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    5. Yuan Gao & Honglong You, 2021. "The Speed of Convergence of the Threshold Estimator of Ruin Probability under the Tempered α -Stable Lévy Subordinator," Mathematics, MDPI, vol. 9(21), pages 1-9, October.
    6. Yuan Gao & Lingju Chen & Jiancheng Jiang & Honglong You, 2020. "Nonparametric Estimation of the Ruin Probability in the Classical Compound Poisson Risk Model," JRFM, MDPI, vol. 13(12), pages 1-12, November.
    7. S. Pitts, 1994. "Nonparametric estimation of compound distributions with applications in insurance," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 46(3), pages 537-555, September.
    8. Zhang, Zhimin & Yang, Hailiang, 2013. "Nonparametric estimate of the ruin probability in a pure-jump Lévy risk model," Insurance: Mathematics and Economics, Elsevier, vol. 53(1), pages 24-35.
    9. Zhang, Zhimin & Yang, Hailiang, 2014. "Nonparametric estimation for the ruin probability in a Lévy risk model under low-frequency observation," Insurance: Mathematics and Economics, Elsevier, vol. 59(C), pages 168-177.
    10. Li Qin & Susan M. Pitts, 2012. "Nonparametric Estimation of the Finite-Time Survival Probability with Zero Initial Capital in the Classical Risk Model," Methodology and Computing in Applied Probability, Springer, vol. 14(4), pages 919-936, December.
    11. Oshime, Takayoshi & Shimizu, Yasutaka, 2018. "Parametric inference for ruin probability in the classical risk model," Statistics & Probability Letters, Elsevier, vol. 133(C), pages 28-37.

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