Optimal reinsurance policy: The adjustment coefficient and the expected utility criteria.

*(English)*Zbl 1152.91583Summary: This paper is concerned with the optimal form of reinsurance from the ceding company point of view, when the cedent seeks to maximize the adjustment coefficient of the retained risk. We deal with the problem by exploring the relationship between maximizing the adjustment coefficient and maximizing the expected utility of wealth for the exponential utility function, both with respect to the retained risk of the insurer.

Assuming that the premium calculation principle is a convex functional and that some other quite general conditions are fulfilled, we prove the existence and uniqueness of solutions and provide a necessary optimal condition. These results are used to find the optimal reinsurance policy when the reinsurance premium calculation principle is the expected value principle or the reinsurance loading is an increasing function of the variance. In the expected value case the optimal form of reinsurance is a stop-loss contract. In the other cases, it is described by a nonlinear function.

Assuming that the premium calculation principle is a convex functional and that some other quite general conditions are fulfilled, we prove the existence and uniqueness of solutions and provide a necessary optimal condition. These results are used to find the optimal reinsurance policy when the reinsurance premium calculation principle is the expected value principle or the reinsurance loading is an increasing function of the variance. In the expected value case the optimal form of reinsurance is a stop-loss contract. In the other cases, it is described by a nonlinear function.

##### MSC:

91B30 | Risk theory, insurance (MSC2010) |

##### Keywords:

optimal reinsurance; risk; stop loss; ruin probability; adjustment coefficient; premium principles; exponential utility function
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\textit{M. Guerra} and \textit{M. L. Centeno}, Insur. Math. Econ. 42, No. 2, 529--539 (2008; Zbl 1152.91583)

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##### References:

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