Sensitivity analysis of insurance risk models via simulation

We show how, from a single simulation run, to estimate the run 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.