# Optimal excess-of-loss reinsurance for stochastic factor risk models

## TL;DR

This paper develops a stochastic control model for optimal excess-of-loss reinsurance considering stochastic claim intensities and sizes influenced by environmental factors, incorporating investment and risk premia effects.

## Contribution

It introduces a novel stochastic factor model for reinsurance optimization using Hamilton-Jacobi-Bellman equations with a verification theorem and numerical analysis.

## Key findings

- Optimal reinsurance strategies depend on stochastic environmental factors.
- The model accounts for stochastic risk premia and investment returns.
- Numerical results illustrate the impact of environmental factors on reinsurance decisions.

## Abstract

We study the optimal excess-of-loss reinsurance problem when both the intensity of the claims arrival process and the claim size distribution are influenced by an exogenous stochastic factor. We assume that the insurer's surplus is governed by a marked point process with dual-predictable projection affected by an environmental factor and that the insurance company can borrow and invest money at a constant real-valued risk-free interest rate $r$. Our model allows for stochastic risk premia, which take into account risk fluctuations. Using stochastic control theory based on the Hamilton-Jacobi-Bellman equation, we analyze the optimal reinsurance strategy under the criterion of maximizing the expected exponential utility of the terminal wealth. A verification theorem for the value function in terms of classical solutions of a backward partial differential equation is provided. Finally, some numerical results are discussed.