Pricing Energy, Weather and Emission Derivatives under Future Information
The aim of this thesis mainly consists in the computation of risk-neutral option prices for energy, weather, emission and commodity derivatives, whereas we innovatively take future information – which we assume to be available to well-informed market insiders – into account via several customized enlargements of the underlying information filtrations. In this regard, we inter alia derive European as well as exotic option price formulas for electricity derivatives such as traded at the European Energy Exchange EEX, for example, but yet under the incorporation of forward-looking information about possible future electricity spot price behavior. Furthermore, we provide both utility-maximizing anticipating portfolio selection procedures and optimal liquidation strategies for electricity futures portfolios yielding minimal expected trading costs under forward-looking price impact considerations. Moreover, we correlate electricity spot prices with outdoor temperature and treat a related electricity derivatives pricing problem even under additional temperature forecasts. In this insider trading context, we also derive explicit expressions for different types of temperature futures indices such as usually traded at the Chicago Mercantile Exchange CME, for instance, and provide various pricing formulas for options written on the latter. Additionally, we construct optimal positions in a temperature futures portfolio under forecasted weather information in order to hedge against both temporal and spatial temperature risk adequately. Further on, we treat the pricing of carbon emission allowances, such as commonly traded in the European Union Emission Trading Scheme EU ETS, but under supplementary insider information on the future market zone net position. In this context, we propose two improved arithmetic multi-state approaches to model the ‘length of the market net position’ more realistically than in existing models. By the way, throughout this work we frequently discuss customized martingale compensators under enlarged filtrations and related information premia associated to our specific insider trading frameworks. Finally, we invent nonlinear double-jump stochastic filtering techniques for generalized Lévy-type processes in order to (theoretically) calibrate the emerging incomplete market models.