Downward Counterfactual Analysis in Insurance Tropical Cyclone Models: A Miami Case Study

Abstract

AbstractThe insurance industry uses catastrophe models to assess and manage the risk from natural disasters such as tropical cyclones, floods, and wildfires. However, despite being designed to consider a credible range of future events, catastrophe models are ultimately calibrated on historical experience. This means that unexpected things can happen, either because risks that were overlooked or deemed immaterial turn out to be meaningful, or because black swans occur that scientists and insurers were not yet aware of. When faced with these types of extreme uncertainty, insurers can use downward counterfactual analysis to explore how historical events could have had more severe consequences (and help identify previously unknown or overlooked risks). In this chapter, we present a methodology for insurers to operationalise downward counterfactuals using tropical cyclone catastrophe models. The methodology is applied to three recent major hurricanes that were near misses for Miami—Matthew (2016), Irma (2017), and Dorian (2019). The results reveal downward counterfactuals that produce insured losses many times greater than what transpired, at up to 300x greater for Matthew, 25x for Irma, and 250x for Dorian. We argue that it is increasingly important for insurers to examine such near-miss events in a changing climate, particularly in disaster prone regions, like Miami, that might not have seen a large loss in recent years. By operationalising downward counterfactuals, insurers can increase risk awareness, stress-test risk management frameworks, and inform decision-making.