The Optimal Time to Merge Two First-Line Insurers with Proportional Reinsurance Policies

We examine the optimal time to merge two first-line insurers with proportional reinsurance policies. The problem is considered in a diffusion approximation model. The objective is to maximize the survival probability of the two insurers. First, the verification theorem is verified. Then, we divide the problem into two cases. In case 1, never merging is optimal and the two insurers follow the optimal reinsurance policies that maximize their survival probability. In case 2, the two insurers follow the same reinsurance policies as those in case 1 until the sum of their surplus processes reaches a boundary. Then, they merge and apply the merged company’s optimal reinsurance strategy.

The Reduction of Initial Reserves Using the Optimal Reinsurance Chains in Non-Life Insurance

The aim of the paper is to propose, and give an example of, a strategy for managing insurance risk in continuous time to protect a portfolio of non-life insurance contracts against unwelcome surplus fluctuations. The strategy combines the characteristics of the ruin probability and the values VaR and CVaR. It also proposes an approach for reducing the required initial reserves by means of capital injections when the surplus is tending towards negative values, which, if used, would protect a portfolio of insurance contracts against unwelcome fluctuations of that surplus. The proposed approach enables the insurer to analyse the surplus by developing a number of scenarios for the progress of the surplus for a given reinsurance protection over a particular time period. It allows one to observe the differences in the reduction of risk obtained with different types of reinsurance chains. In addition, one can compare the differences with the results obtained, using optimally chosen parameters for each type of proportional reinsurance making up the reinsurance chain.

Application of Convex Optimization Results of De Finetti's problem for Proportional Reinsurance (Study case CAARAMA insurance campany in Algiers)

The objective of this research is to find the optimal retention level for a proportional reinsurance treaty based on the results of the convex optimization developed in De Finetti’s model. The latter makes it possible to determine the level of retention that achieves the expected profit by the insurer, while minimizing claims volatility. The convex functions appear abundantly in economics and finance. They have remarkable specificities that allows actuaries to minimize financial risks to which some institutions are exposed, especially insurance companies. Therefore, the use of mathematical tools to manage the various risks is paramount.In order to remedy the optimization problem, we have combined the probability of failure method with the "De Finetti" model for proportional reinsurance, which proposed a retention optimization process that minimizes claim volatility for a fixed expected profit based on the results of the non-linear optimization. JEL Codes: C02, C25, C61, G22.

Exposure to catastrophe risk and use of reinsurance: an empirical evaluation for the U.S.

Reinsurance has long been used for tail risk protection. There is ample anecdotal information from practitioners about this dimension of reinsurance. The subject, however, remains largely unexplored in the academic literature given the lack of data about non-proportional reinsurance contracts. We develop a novel approach to measure the use of non-proportional reinsurance and use it to disentangle reinsurance used for catastrophe risk protection from reinsurance used for other motivations, for example regulatory capital relief. Our findings rely on a new measure of catastrophe risk that has strong explanatory power about insurers’ behaviour towards risk beyond what has been captured by existing measures.

Cascading Losses in Reinsurance Networks

We develop a model for contagion in reinsurance networks by which primary insurers’ losses are spread through the network. Our model handles general reinsurance contracts, such as typical excess of loss contracts. We show that simpler models existing in the literature—namely proportional reinsurance—greatly underestimate contagion risk. We characterize the fixed points of our model and develop efficient algorithms to compute contagion with guarantees on convergence and speed under conditions on network structure. We characterize exotic cases of problematic graph structure and nonlinearities, which cause network effects to dominate the overall payments in the system. Last, we apply our model to data on real-world reinsurance networks. Our simulations demonstrate the following. (1) Reinsurance networks face extreme sensitivity to parameters. A firm can be wildly uncertain about its losses even under small network uncertainty. (2) Our sensitivity results reveal a new incentive for firms to cooperate to prevent fraud, because even small cases of fraud can have outsized effect on the losses across the network. (3) Nonlinearities from excess of loss contracts obfuscate risks and can cause excess costs in a real-world system. This paper was accepted by Baris Ata, stochastic models and simulation.