Weather Derivatives Pricing: Modeling the Seasonal Residual Variance of an Ornstein-Uhlenbeck Temperature Process with Neural Networks

10th International Conference on Engineering Applications of Neural Networks

Achilleas Zapranis ; Antonis Alexandridis

Others  :  http://CEUR-WS.org/Vol-284/page178.pdf
PID  :  21450

来源: CEUR

【 摘 要 】

In this paper, we use neural networks in order to model the seasonal component of the residual varianceof 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 collectedfrom 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 temperatureprocess, which can be used in the context of Monte-Carlo simulations for pricing weather derivatives.