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The recent introduction of wind power futures written on the German wind power production index has brought with it new interesting challenges in terms of modeling and pricing. Some particularities of this product are the strong seasonal component embedded in the underlying, the fact that the wind index is bounded from both above and below, and also that the futures are settled against a synthetically generated spot index. Here, we consider the non-Gaussian Ornstein-Uhlenbeck type processes proposed by Barndorff-Nielsen and Shephard (2001) in the context of modeling the wind power production index. We discuss the properties of the model and estimation of the model parameters. Further, the model allows for an analytical formula for pricing wind power futures. We provide an empirical study, where the model is calibrated to 37 years of German wind power production index that is synthetically generated assuming a constant level of installed capacity. Also, based on one year of observed prices for wind power futures with different delivery periods, we study the market price of risk. Generally, we find a negative risk premium whose magnitude decreases as the length of the delivery period increases. To further demonstrate the benefits of our proposed model, we address the pricing of European options written on wind power futures, which can be achieved through Fourier techniques.

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The recent price coupling of many European electricity markets has triggered a fundamental change in the interaction of day-ahead prices, challenging additionally the modeling of the joint behavior of prices in interconnected markets. In this paper we propose a regime-switching AR-GARCH copula to model pairs of day-ahead electricity prices in coupled European markets. While capturing key stylized facts empirically substantiated in the literature, this model easily allows us to 1) deviate from the assumption of normal margins and 2) include a more detailed description of the dependence between prices. We base our empirical study on four pairs of prices, -Western Denmark. We find that the marginal dynamics are better described by the flexible skew t distribution than the benchmark normal distribution. Also, we find significant evidence of tail dependence in all pairs of interconnected areas we consider. As a first application of the proposed empirical model, we consider the pricing of financial transmission rights, and highlight how the choice of marginal distributions and copula impacts prices. As a second application we consider the forecasting of tail quantiles, and evaluate the out-of-sample performance of competing models. to thank Jesper Jung, Esben Høg and Thomas Aalund Fredholm for providing valuable comments and suggestions. Two anonymous referees are also thanked for their constructive criticism and suggestions, which improved the presentation of this paper. † We mention in passing that univariate time series models of the ARMA-GARCH type have been successfully applied to model day-ahead electricity prices previously (see e.g. Keles et al. (2012) and Paraschiv (2013)).