Bayesian optimal investment and reinsurance with dependent financial and insurance risks

TL;DR

This paper develops a stochastic control model to determine optimal investment and reinsurance strategies considering dependent financial and insurance risks during crises, incorporating learning of claim size distributions.

Contribution

It introduces a novel approach to model dependence between financial and insurance risks during crises and integrates learning of claim size distributions into the control framework.

Findings

Dependence significantly influences optimal strategies, leading to more prudent insurer behavior.

Even minimal dependence in the model greatly impacts control decisions.

The model provides bounds and comparisons for optimal strategies under different assumptions.

Abstract

Major events like natural catastrophes or the COVID-19 crisis have impact both on the financial market and on claim arrival intensities and claim sizes of insurers. Thus, when optimal investment and reinsurance strategies have to be determined it is important to consider models which reflect this dependence. In this paper we make a proposal how to generate dependence between the financial market and claim sizes in times of crisis and determine via a stochastic control approach an optimal investment and reinsurance strategy which maximizes the expected exponential utility of terminal wealth. Moreover, we also allow that the claim size distribution may be learned in the model. We give comparisons and bounds on the optimal strategy using simple models. What turns out to be very surprising is that numerical results indicate that even a minimal dependence which is created in this model has a huge impact on the control in the sense that the insurer is much more prudent then.