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Sensitivity Analysis of Insurance Risk Models via Simulation

Sensitivity Analysis of Insurance Risk Models via Simulation 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Management Science INFORMS

Sensitivity Analysis of Insurance Risk Models via Simulation

Asmussen, Søren; Rubinstein, Reuven Y.
Management Science , Volume 45 (8): 17 – Aug 1, 1999
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Publisher
INFORMS
Copyright
Copyright © INFORMS
Subject
Research Article
ISSN
0025-1909
eISSN
1526-5501
DOI
10.1287/mnsc.45.8.1125
Publisher site
See Article on Publisher Site

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.

Journal

Management Science – INFORMS

Published: Aug 1, 1999

Keywords: Keywords : derivative estimation ; importance sampling ; likelihood ratio ; premium rule ; push-out method ; rare event ; reinsurance ; ruin probability ; score function ; stochastic optimization ; total claims

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