Demand for catastrophe insurance under the path-dependent effects

TL;DR

This paper develops explicit solutions for optimal investment and insurance strategies considering path-dependent market effects, highlighting the importance of these effects in catastrophe insurance pricing.

Contribution

It introduces a novel approach to solve a non-Markovian, path-dependent problem using auxiliary variables and extends the Functional Ito formula for fractional Brownian motion.

Findings

Risk aversion increases with rough volatility.

Demand for catastrophe insurance rises with path dependence.

Ignoring path dependence leads to underinsurance.

Abstract

This paper investigates optimal investment and insurance strategies under a mean-variance criterion with path-dependent effects. We use a rough volatility model and a Hawkes process with a power kernel to capture the path dependence of the market. By adding auxiliary state variables, we degenerate a non-Markovian problem to a Markovian problem. Next, an explicit solution is derived for a path-dependent extended Hamilton-Jacobi-Bellman (HJB) equation. Then, we derive the explicit solutions of the problem by extending the Functional Ito formula for fractional Brownian motion to the general path-dependent processes, which includes the Hawkes process. In addition, we use earthquake data from Sichuan Province, China, to estimate parameters for the Hawkes process. Our numerical results show that the individual becomes more risk-averse in trading when stock volatility is rough, while more risk-seeking when considering catastrophic shocks. Moreover, an individual's demand for catastrophe insurance increases with path-dependent effects. Our findings indicate that ignoring the path-dependent effect would lead to a significant underinsurance phenomenon and highlight the importance of the path-dependent effect in the catastrophe insurance pricing.