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