A stochastic volatility model for the valuation of temperature derivatives

TL;DR

This paper introduces a new stochastic volatility model for temperature derivatives that improves risk assessment of temperature volatility, offering better handling of extreme events while maintaining computational tractability.

Contribution

The paper extends the Ornstein-Uhlenbeck model to include stochastic volatility, providing a new approach for more accurate temperature derivative valuation and risk management.

Findings

Model effectively captures temperature volatility dynamics.

Estimation method performs well on European city data.

Enhanced techniques for pricing weather derivatives.

Abstract

This paper develops a new stochastic volatility model for the temperature that is a natural extension of the Ornstein-Uhlenbeck model proposed by Benth and Benth (2007). This model allows to be more conservative regarding extreme events while keeping tractability. We give a method based on Conditional Least Squares to estimate the parameters on daily data and estimate our model on eight major European cities. We then show how to calculate efficiently the average payoff of weather derivatives both by Monte-Carlo and Fourier transform techniques. This new model allows to better assess the risk related to temperature volatility.