Pricing Weather Derivatives for Extreme Events

TL;DR

This paper presents a novel method for pricing weather derivatives based on spatial statistics and extreme value theory, modeling weather extremes as max-stable processes to accurately simulate payments and assess risk premiums.

Contribution

It introduces a new approach combining spatial statistics and extreme value theory for pricing weather derivatives with spatial dependence considerations.

Findings

The method effectively models spatial dependence of weather extremes.

Simulations enable accurate computation of risk loads and premiums.

Applicable to a broad class of weather derivatives.

Abstract

We consider pricing weather derivatives for use as protection against weather extremes. The method described utilizes results from spatial statistics and extreme value theory to first model extremes in the weather as a max-stable process, and then use these models to simulate payments for a general collection of weather derivatives. These simulations capture the spatial dependence of payments. Incorporating results from catastrophe ratemaking, we show how this method can be used to compute risk loads and premiums for weather derivatives which are renewal-additive.