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Sensitivity Analysis of Insurance Risk Models via Simulation

Author

Listed:
  • Søren Asmussen

    (Department of Mathematical Statistics, University of Lund, Box 118, 221 00 Lund, Sweden)

  • Reuven Y. Rubinstein

    (William Davidson Faculty of Industrial Engineering and Management, Technion, Haifa, Israel)

Abstract

We show how, from a single simulation run, to estimate the ruin probabilities and their sensitivities (derivatives) in a classic insurance risk model under various distributions of the number of claims and the claim size. Similar analysis is given for the tail probabilities of the accumulated claims during a fixed period. We perform sensitivity analysis with respect to both distributional and structural parameters of the underlying risk model. In the former case, we use the score function method and in the latter, a combination of the push-out method and the score function. We finally show how, from the same sample path, to derive a consistent estimator of the optimal solution in an optimization problem associated with excess-of-loss reinsurance.

Suggested Citation

  • Søren Asmussen & Reuven Y. Rubinstein, 1999. "Sensitivity Analysis of Insurance Risk Models via Simulation," Management Science, INFORMS, vol. 45(8), pages 1125-1141, August.
  • Handle: RePEc:inm:ormnsc:v:45:y:1999:i:8:p:1125-1141
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    File URL: http://dx.doi.org/10.1287/mnsc.45.8.1125
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    References listed on IDEAS

    as
    1. Peter W. Glynn & Donald L. Iglehart, 1989. "Importance Sampling for Stochastic Simulations," Management Science, INFORMS, vol. 35(11), pages 1367-1392, November.
    2. Philip Heidelberger & Don Towsley, 1989. "Sensitivity Analysis from Sample Paths Using Likelihoods," Management Science, INFORMS, vol. 35(12), pages 1475-1488, December.
    3. Asmussen, S. & Binswanger, K., 1997. "Simulation of Ruin Probabilities for Subexponential Claims," ASTIN Bulletin: The Journal of the International Actuarial Association, Cambridge University Press, vol. 27(02), pages 297-318, November.
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    Cited by:

    1. Ankush Agarwal & Stefano De Marco & Emmanuel Gobet & Gang Liu, 2017. "Rare event simulation related to financial risks: efficient estimation and sensitivity analysis," Working Papers hal-01219616, HAL.

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