Sensitivity Analysis of Insurance Risk Models via Simulation

Søren Asmussen and Reuven Y. Rubinstein
Additional contact information
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

Management Science, 1999, vol. 45, issue 8, 1125-1141

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.

Keywords: derivative estimation; importance sampling; likelihood ratio; premium rule; push-out method; rare event; reinsurance; ruin probability; score function; stochastic optimization; total claims (search for similar items in EconPapers)
Date: 1999
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.45.8.1125 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:45:y:1999:i:8:p:1125-1141

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().