Wavelet Analysis and Weather Derivatives Pricing
نویسندگان
چکیده
In this paper, we use wavelet analysis to localize in Paris, France, a mean-reverting Ornstein-Uhlenbeck process with seasonality in the level and volatility. Wavelet analysis is an extension of the Fourier transform, which is very well suited to the analysis of non-stationary signals. We use wavelet analysis to identify the seasonality component in the temperature process as well as in the volatility of the temperature anomalies (residuals). Our model is validated on more than 100 years of data collected from Paris, one of the European cities traded at Chicago Mercantile Exchange. We also study the effect of replacing the original AR(1) process with ARMA, ARFIMA and ARFIMA-FIGARCH models, and the impact of the temperature outliers on the normality of the temperature anomalies.
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