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The literature on optimal reinsurance does not deal with how much theeffectiveness of such solutions is degraded by errors in parameters and models.The issue is investigated through both asymptotics and numerical studies. It isshown that the rate of degradation is often $O(1/n)$ as the sample size $n$ ofhistorical observations becomes infinite. Criteria based on Value at Risk areexceptions that may achieve only $O(1/\sqrt{n})$. These theoretical results aresupported by numerical studies. A Bayesian perspective on how to integrate riskcaused by parameter error is offered as well.

How much is optimal reinsurance degraded by error?