Abstract
In this paper, we use wavelet neural networks in order to model a mean-reverting Ornstein-Uhlenbeck temperature process, with seasonality in the level and volatility. We forecast up to two months ahead out of sample daily temperatures and we simulate the corresponding Cumulative Average Temperature and Heating Degree Day indices. The proposed model is validated in 8 European and 5 USA cities all traded in Chicago Mercantile Exchange. Our results suggest that the proposed method outperforms alternative pricing methods proposed in prior studies in most cases. Our findings suggest that wavelet networks can model the temperature process very well and consequently they constitute a very accurate and efficient tool for weather derivatives pricing. Finally, we provide the pricing equations for temperature futures on Heating Degree Day index.KeywordsWeather DerivativesPricingForecastingWavelet Networks
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