Weather derivatives pricing: Modeling the seasonal residual variance of an Ornstein-Uhlenbeck temperature process with neural networks

In this paper, we use neural networks in order to model the seasonal component of the residual variance of a mean-reverting Ornstein-Uhlenbeck temperature process, with seasonality in the level and volatility. We also use wavelet analysis to identify the seasonality component in the temperature process as well as in the volatility of the temperature anomalies. Our model is validated on more than 100 years of data collected from Paris, one of the European cities traded at Chicago Mercantile Exchange. Our results show a significant improvement over more traditional alternatives, regarding the statistical properties of the temperature process, which can be used in the context of MonteCarlo simulations for pricing weather derivatives.