Ceded Loss Functions Research Articles

Optimal reinsurance subject to Vajda condition

In this paper, we study optimal reinsurance design by minimizing the risk-adjusted value of an insurer’s liability, where the valuation is carried out by a cost-of-capital approach based either on the value at risk or the conditional value at risk. To prevent moral hazard and to be consistent with the spirit of reinsurance, we follow Vajda (1962) and assume that both the insurer’s retained loss and the proportion paid by a reinsurer are increasing in indemnity. We analyze the optimal solutions for a wide class of reinsurance premium principles which satisfy three axioms (law invariance, risk loading and preserving convex order) and encompass ten of the eleven widely used premium principles listed in Young (2004). Our results show that the optimal ceded loss functions are in the form of three interconnected line segments. Further simplified forms of the optimal reinsurance are obtained for the premium principles under an additional mild constraint. Finally, to illustrate the applicability of our results, we derive the optimal reinsurance explicitly for both the expected value principle and Wang’s principle.

Optimal reinsurance with concave ceded loss functions under VaR and CTE risk measures

Most of the studies on optimal reinsurance are from the viewpoint of the insurer and the optimal ceded functions always turn out to be convex. However reinsurance contracts always involve a limit on the ceded loss function in practice, thus it may not be enough to confine the analysis to the class of convex functions only. In this paper, we study the problem of optimal reinsurance under VaR and CTE optimization criteria when the ceded loss functions are in the class of increasing concave functions. By using a simple geometric approach, we prove that under the VaR optimization criterion, the quota-share reinsurance with a policy limit is always optimal, while the full reinsurance with a policy limit is optimal under the CTE optimization criterion. Some illustrative examples are presented.

Enhancing Insurer Value Using Reinsurance and Value-at-Risk Criterion

The quest for optimal reinsurance design has remained an interesting problem among insurers, reinsurers, and academicians. An appropriate use of reinsurance could reduce the underwriting risk of an insurer and thereby enhance its value. This paper complements the existing research on optimal reinsurance by proposing another model for the determination of the optimal reinsurance design. The problem is formulated as a constrained optimization problem with the objective of minimizing the value-at-risk of the net risk of the insurer while subjecting to a profitability constraint. The proposed optimal reinsurance model, therefore, has the advantage of exploiting the classical tradeoff between risk and reward. Under the additional assumptions that the reinsurance premium is determined by the expectation premium principle and the ceded loss function is confined to a class of increasing and convex functions, explicit solutions are derived. Depending on the risk measure's level of confidence, the safety loading for the reinsurance premium, and the expected profit guaranteed for the insurer, we establish conditions for the existence of reinsurance. When it is optimal to cede the insurer's risk, the optimal reinsurance design could be in the form of pure stop-loss reinsurance, quota-share reinsurance, or a combination of stop-loss and quota-share reinsurance.

Optimality of general reinsurance contracts under CTE risk measure

By formulating a constrained optimization model, we address the problem of optimal reinsurance design using the criterion of minimizing the conditional tail expectation (CTE) risk measure of the insurer’s total risk. For completeness, we analyze the optimal reinsurance model under both binding and unbinding reinsurance premium constraints. By resorting to the Lagrangian approach based on the concept of directional derivative, explicit and analytical optimal solutions are obtained in each case under some mild conditions. We show that pure stop-loss ceded loss function is always optimal. More interestingly, we demonstrate that ceded loss functions, that are not always non-decreasing, could be optimal. We also show that, in some cases, it is optimal to exhaust the entire reinsurance premium budget to determine the optimal reinsurance, while in other cases, it is rational to spend less than the prescribed reinsurance premium budget.

Optimal reinsurance under VaR and CTE risk measures

Let X denote the loss initially assumed by an insurer. In a reinsurance design, the insurer cedes part of its loss, say f ( X ) , to a reinsurer, and thus the insurer retains a loss I f ( X ) = X − f ( X ) . In return, the insurer is obligated to compensate the reinsurer for undertaking the risk by paying the reinsurance premium. Hence, the sum of the retained loss and the reinsurance premium can be interpreted as the total cost of managing the risk in the presence of reinsurance. Based on a technique used in [Müller, A., Stoyan, D., 2002. Comparison Methods for Stochastic Models and Risks. In: Willey Series in Probability and Statistics] and motivated by [Cai J., Tan K.S., 2007. Optimal retention for a stop-loss reinsurance under the VaR and CTE risk measure. Astin Bull. 37 (1), 93–112] on using the value-at-risk (VaR) and the conditional tail expectation (CTE) of an insurer’s total cost as the criteria for determining the optimal reinsurance, this paper derives the optimal ceded loss functions in a class of increasing convex ceded loss functions. The results indicate that depending on the risk measure’s level of confidence and the safety loading for the reinsurance premium, the optimal reinsurance can be in the forms of stop-loss, quota-share, or change-loss.