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Weather Risks, Ratemaking, and Modeling the Tail Distribution: An Application of Extreme Value Theory

J. Hao, A. Bathke, J. R. Skees, H. Dai


Economic analysis of weather risk often depends on accurate assessment of the probability (P) of tail quantiles (Q) and extreme value theory can provide a promising estimation of the tail part risk. In this article, we apply statistical techniques to quantify weather tail risk and compare the results from standard statistical distributions with extreme value models for risk estimation and premium setting. We demonstrate that extreme value models can provide more statistically robust estimation in modeling weather tail risk and premium ratemaking of weather-based contingent claims. Rainfall data across selected regions in India during the 1871–2001 period is used in the empirical analysis.


SWeather risks, Ratemaking, Extreme value theorem.

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