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Three Methods to Calculate the Probability of Ruin

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  • Dufresne, François
  • Gerber, Hans U.

Abstract

The first method, essentially due to GOOVAERTS and DE VYLDER, uses the connection between the probability of ruin and the maximal aggregate loss random variable, and the fact that the latter has a compound geometric distribution. For the second method, the claim amount distribution is supposed to be a combination of exponential or translated exponential distributions. Then the probability of ruin can be calculated in a transparent fashion; the main problem is to determine the nontrivial roots of the equation that defines the adjustment coefficient. For the third method one observes that the probability, of ruin is related to the stationary distribution of a certain associated process. Thus it can be determined by a single simulation of the latter. For the second and third methods the assumption of only proper (positive) claims is not needed.

Suggested Citation

  • Dufresne, François & Gerber, Hans U., 1989. "Three Methods to Calculate the Probability of Ruin," ASTIN Bulletin, Cambridge University Press, vol. 19(1), pages 71-90, April.
    • Handle: RePEc:cup:astinb:v:19:y:1989:i:01:p:71-90_00
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    Citations

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    Cited by:

    1. Yacine Koucha & Alfredo D. Egidio dos Reis, 2021. "Approximations to ultimate ruin probabilities with a Wienner process perturbation," Papers 2107.02537, arXiv.org.
    2. Tamturk, Muhsin & Utev, Sergey, 2018. "Ruin probability via Quantum Mechanics Approach," Insurance: Mathematics and Economics, Elsevier, vol. 79(C), pages 69-74.
    3. Riccardo Gatto & Benjamin Baumgartner, 2016. "Saddlepoint Approximations to the Probability of Ruin in Finite Time for the Compound Poisson Risk Process Perturbed by Diffusion," Methodology and Computing in Applied Probability, Springer, vol. 18(1), pages 217-235, March.
    4. Gatto, Riccardo, 2008. "A saddlepoint approximation to the probability of ruin in the compound Poisson process with diffusion," Statistics & Probability Letters, Elsevier, vol. 78(13), pages 1948-1954, September.
    5. Olena Ragulina & Jonas Šiaulys, 2020. "Upper Bounds and Explicit Formulas for the Ruin Probability in the Risk Model with Stochastic Premiums and a Multi-Layer Dividend Strategy," Mathematics, MDPI, vol. 8(11), pages 1-35, October.
    6. Mine Cinar & Colton Burns & Nathalie Hilmi & Alain Safa, 2020. "Risk Assessments of Impacts of Climate Changeand Tourism: Lessons for the Mediterranean and Middle East and North African Countries," International Journal of Environmental Sciences & Natural Resources, Juniper Publishers Inc., vol. 24(5), pages 176-187, June.
    7. Claude Lefèvre & Stéphane Loisel & Muhsin Tamturk & Sergey Utev, 2018. "A Quantum-Type Approach to Non-Life Insurance Risk Modelling," Risks, MDPI, vol. 6(3), pages 1-17, September.
    8. J. Vazquez-Abad, Felisa, 2000. "RPA pathwise derivative estimation of ruin probabilities," Insurance: Mathematics and Economics, Elsevier, vol. 26(2-3), pages 269-288, May.
    9. Dickson, David C. M. & Hipp, Christian, 1998. "Ruin probabilities for Erlang(2) risk processes," Insurance: Mathematics and Economics, Elsevier, vol. 22(3), pages 251-262, July.
    10. Pawel Mista, 2006. "Analytical and numerical approach to corporate operational risk modelling," HSC Research Reports HSC/06/03, Hugo Steinhaus Center, Wroclaw University of Technology.
    11. Marceau, Etienne & Rioux, Jacques, 2001. "On robustness in risk theory," Insurance: Mathematics and Economics, Elsevier, vol. 29(2), pages 167-185, October.
    12. Gerber, Hans U. & Shiu, Elias S.W. & Yang, Hailiang, 2013. "Valuing equity-linked death benefits in jump diffusion models," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 615-623.
    13. Thampi K. K. & Jacob M. J. & Raju N., 2007. "Ruin Probabilities under Generalized Exponential Distribution," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 2(1), pages 1-12, May.
    14. Riccardo Gatto & Benjamin Baumgartner, 2014. "Value at Ruin and Tail Value at Ruin of the Compound Poisson Process with Diffusion and Efficient Computational Methods," Methodology and Computing in Applied Probability, Springer, vol. 16(3), pages 561-582, September.

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