Estimating Climate Risk Exposure in the U.S. Insurance Sector Using Factor Model and EVT

Olanrewaju Oluwadamilare Olaniyan ()
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Olanrewaju Oluwadamilare Olaniyan: Department of Mathematics, Western Michigan University, 1903 W Michigan Ave, Kalamazoo, MI 49008, USA

Mathematics, 2025, vol. 13, issue 21, 1-20

Abstract: This study examines the exposure of the U.S. insurance sector to climate-related risks using a two-step approach combining factor modeling and Extreme Value Theory. The analysis first constructs a climate risk factor from transition-sensitive sectors and estimates its impact on the SPDR S&P Insurance ETF using a standard factor model. The resulting residual, termed Insurance Climate Risk, isolates climate-driven excess returns by controlling for market-wide effects. To assess the sector’s sensitivity to extreme events, the study applies both the Peaks Over Threshold method using the Generalized Pareto Distribution and the Block Maxima Method using the Generalized Extreme Value distribution. The findings reveal statistically significant climate sensitivity, especially in daily and weekly data, and confirm the presence of heavy tails in the loss distribution. VaR and CVaR estimates indicate heightened risk over longer horizons and under block maxima modeling. Notably, peak over threshold daily returns yield a 95% VaR of 1.33% and CVaR of 2.28%, while block maxima CVaR exceeds 5%. These results show the importance of incorporating tail-risk-aware metrics in insurance risk management and highlight the growing influence of climate-related financial shocks.

Keywords: Extreme Value Theorem; factor model; Value at Risk; Conditional Value at Risk; Generalized Pareto Distribution; Generalized Extreme Value; peak over threshold; Block Maxima (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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