Forecasting US insured hurricane losses

Abstract

Coastal hurricanes generate huge financial losses within the insurance industry. The relative infrequency of severe coastal hurricanes implies that empirical probability estimates of the next big loss will be unreliable. Hurricane climatologists have recently developed statistical models to forecast the level of coastal hurricane activity based on climate conditions prior to the season. Motivated by the usefulness of such models, in this chapter we analyze and model a catalog of normalized insured losses caused by hurricanes affecting the United States. The catalog of losses dates back through the twentieth century. The purpose of this work is to develop a preseason forecast tool that can be used for insurance applications. Although wind speed is directly related to damage potential, the amount of damage depends on both storm intensity and storm size. As anticipated, we found that climate conditions prior to a hurricane season provide information about possible future insured hurricane losses. The models exploit this information to predict the distribution of likely annual losses and the distribution of a worst-case catastrophic loss aggregated over the entire US coast.

United States hurricane landfalls and damages: Can one- to five-year predictions beat climatology?

“…Elsner and Jagger, 2006;Jagger et al, 2008). Elsner and Jagger (2008) find a relationship between the solar cycle and US hurricane counts, after accounting for SSTs, wind shear and steering currents.…”

Maximum wind radius estimated by the 50 kt radius: improvement of storm surge forecasting over the western North Pacific

“…Of all hurricane damage, 80 % is caused by less than 20 % of the worst events (Jagger et al, 2007). The aim of the present study is to develop an R max estimation model, which is expected to increase the reliability of forecasting of strong storm surges.…”

Climate and solar signals in property damage losses from hurricanes affecting the United States

“…Based on these diagnostics, we discard the first 10,000 samples and analyze the output from the next 10,000 samples. The utility of the Bayesian approach for modeling the mean number of coastal hurricanes is described in and for predicting damage losses is described in Jagger et al (2008). Figure 6 shows the posterior predictive distributions of annually aggregated losses for six different climate scenarios.…”