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The increasing exposure to renewable energy has amplified the need for risk management in electricity markets. Electricity price risk poses a major challenge to market participants. We propose an approach to model and forecast electricity prices taking into account information on renewable energy production. While most literature focuses on point forecasting, our methodology forecasts the whole distribution of electricity prices and incorporates spike risk, which is of great value for risk management. It is based on functional principal component analysis and time-adaptive nonparametric density estimation techniques. The methodology is applied to electricity market data from Germany. We find that renewable infeed effects both, the location and the shape of spot price densities. A comparison with benchmark methods and an application to risk management are provided.

Electricity load forecasts are an integral part of many decision-making pro- cesses in the electricity market. However, most literature on electricity load forecasting concentrates on deterministic forecasts, neglecting possibly impor- tant information about uncertainty. A more complete picture of future demand can be obtained by using distributional forecasts, allowing for a more effcient decision-making. A predictive density can be fully characterized by tail mea- sures such as quantiles and expectiles. Furthermore, interest often lies in the accurate estimation of tail events rather than in the mean or median. We pro- pose a new methodology to obtain probabilistic forecasts of electricity load, that is based on functional data analysis of generalized quantile curves. The core of the methodology is dimension reduction based on functional principal components of tail curves with dependence structure. The approach has sev- eral advantages, such as flexible inclusion of explanatory variables including meteorological forecasts and no distributional assumptions. The methodol- ogy is applied to load data from a transmission system operator (TSO) and a balancing unit in Germany. Our forecast method is evaluated against other models including the TSO forecast model. It outperforms them in terms of mean absolute percentage error (MAPE) and achieves a MAPE of 2:7% for the TSO.

In this paper we investigate price and volatility risk originating in linkages between energy and agricultural commodity prices in Germany and study their dynamics over time. We propose an econometric approach to quantify the volatility and correlation risk structure, which has a large impact for in- vestment and hedging strategies of market participants as well as for policy makers. Volatilities and their short and long run linkages (spillovers) are analyzed using a dynamic conditional correlation GARCH model as well as a multivariate multiplicative volatility model. Our approach provides a flexible and accurate fitting procedure for volatility and correlation risk.

Ziel der Arbeit ist es, den Einfluss einer verstärkten Nutzung von Windenergie als Stromquelle auf Strompreise in Deutschland zu untersuchen. Auf eine kurze Einführung in die Mechanismen des deutschen Strommarkts, folgt die Vorstellung einer erweiterten Version des "two-regime Markov-Switching Model", das in der einschlägigen Literatur herkömmlich zur Beschreibung von Strompreisen auf liberalisierten Strommärkten verwendet wird, dessen "Switching-Probabilites" die "Wind-Penetration" des Markts bei der Vorhersage von Strompreisen berücksichtigt. Anschließend folgen ein empirischer Vergleich des Standardmodells und des erweiterten Modells anhand von Daten des deutschen Strommarkts, und eine Einordnung der Arbeit in die aktuelle Literatur. The thesis aims to investigate the relationship between the increased use of wind as a power source and electricity prices in Germany. After giving a short introduction on the functioning of the German electricity market, we propose an augmented version of a standard two-regime Markov-Switching model used to describe electricity prices on liberalized markets, whose switching probabilities take into account the wind-penetration of the market when forecasting prices. We then show that our model is able to describe prices agreed upon on the German electricity market better than the standard model and conclude by giving a short outlook into further potential research.