A regional compound Poisson process for hurricane and tropical storm damage

TL;DR

This paper introduces a Bayesian regional model for hurricane damage that incorporates storm paths and spatial patterns, providing new insights and practical tools for insurance risk assessment.

Contribution

It develops a fully Bayesian model using CAR to analyze regional storm damage, integrating storm path data for improved regional predictions.

Findings

Model confirms previous regional storm damage patterns.

Reveals new regional storm tendency insights.

Enables pricing of regional insurance premiums.

Abstract

In light of intense hurricane activity along the U.S. Atlantic coast, attention has turned to understanding both the economic impact and behaviour of these storms. The compound Poisson-lognormal process has been proposed as a model for aggregate storm damage, but does not shed light on regional analysis since storm path data are not used. In this paper, we propose a fully Bayesian regional prediction model which uses conditional autoregressive (CAR) models to account for both storm paths and spatial patterns for storm damage. When fitted to historical data, the analysis from our model both confirms previous findings and reveals new insights on regional storm tendencies. Posterior predictive samples can also be used for pricing regional insurance premiums, which we illustrate using three different risk measures.