Single Factor Stochastic Models with Seasonality Applied to Underlying Weather Derivatives Variables

This paper estimates single factor stochastic models describing daily air temperature behaviour. We modify classical financial models to reflect temperature seasonality and fit them to a time series representing temperatures in Spain. The estimated models are used in Monte Carlo simulations to obtain heating and cooling degree-days, which are used as an underlying reference in weather derivatives. The final goal of this work is to obtain an insight into weather derivative valuation, and so making it easier to manage economic activity risks closely related to temperature (i.e. oil, gas and electricity prices and volumes)