A stochastic volatility model for the valuation of temperature derivatives

Abstract

Abstract Accepted by: Konstantinos Nikolopoulos This paper develops a new stochastic volatility model for the average daily temperature. It is a natural extension of a Gaussian model in which the temperature returns to a seasonal trend with a deterministic time-dependent volatility. The new 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.