Optimal Reinsurance under the Mean-Variance Premium Principle to Minimize the Probability of Ruin

TL;DR

This paper derives explicit optimal reinsurance strategies to minimize ruin probability under a mean-variance premium principle, using diffusion approximations and stochastic control methods, with proven convergence results.

Contribution

It provides closed-form solutions for optimal reinsurance and minimum ruin probability under a diffusion approximation, advancing risk management strategies.

Findings

Explicit formulas for optimal reinsurance strategies.

Minimum ruin probability characterized as a viscosity solution.

Convergence of ruin probabilities under scaling.

Abstract

We consider the problem of minimizing the probability of ruin by purchasing reinsurance whose premium is computed according to the mean-variance premium principle, a combination of the expected-value and variance premium principles. We derive closed-form expressions of the optimal reinsurance strategy and the corresponding minimum probability of ruin under the diffusion approximation of the classical Cram\'er-Lundberg risk process perturbed by a diffusion. We find an explicit expression for the reinsurance strategy that maximizes the adjustment coefficient for the classical risk process perturbed by a diffusion. Also, for this risk process, we use stochastic Perron's method to prove that the minimum probability of ruin is the unique viscosity solution of its Hamilton-Jacobi-Bellman equation with appropriate boundary conditions. Finally, we prove that, under an appropriate scaling of the classical risk process, the minimum probability of ruin converges to the minimum probability of ruin under the diffusion approximation.