This dissertation considers different aspects of crude oil research, primarily based on four independent empirical analyses, interconnected through a common denominator: Time-series analysis methods applied to global oil prices.

The first three chapters are of introductory nature. They present the developments on global oil markets since the end of World War II and review the literature on crude oil. More importantly, they show how to estimate global models using vector autoregressive (VAR) and structural vector autoregressive (SVAR) models. The latter allow for the disentanglement and estimation of unexpected oil price shocks required for later analyses.

The first analysis reviews the question, originally at the center of economic research on crude oil: How are macroeconomic performance and oil price shocks interrelated? New insights based on longer sample series as well as developments in SVAR models allow to complement the existing literature by estimating global models of oil. Based on a broad set of monthly macroeconomic variables for the United States and Germany, the analysis shows that these two industrialized economies react differently to oil price shocks. The disentanglement of the underlying causes of unexpected oil price movements is crucial.

The second empirical analysis concerns the effects of oil embargoes against oil producing countries The same SVAR models are applied in the framework of the sanctions that were imposed on Iran by the international community late 2011 and early 2012. The estimation results show that the direct effects of the Iran sanctions on global oil prices were limited and temporary. By estimating and analyzing the unexpected oil price changes before the implementation of sanctions, we find evidence that sanctions might have important price increasing effects through market expectations long before their official implementation.

Departing from the same global model that includes the real price of crude oil as an endogenous variable, the third analysis is concerned with its oil price forecasting properties. We are able to improve the forecasting accuracy by applying regularization methods for variable selection. Originating from the machine learning literature, these methods are now widely used in economic research, especially in cases, where a large number of variables are included in the model. Furthermore, typical lag selection methods, used in the estimation of global models of oil are compared. Finally, the core variable set is augmented by a wide range of possibly relevant regressors as suggested by the literature.

The fourth and final analysis concerns another aspect of oil price forecasting when using crude oil futures as forecasts for the spot price of oil. We estimate whether forecasting preferences are asymmetric in a sense that a positive forecast error has a different cost than a negative forecast error of the same magnitude. Using different model specifications and a wide range of instrument sets inspired by the literature on futures, we find robust evidence for asymmetric loss. The market has a preference to underestimate the spot price of crude oil through futures pricing. This indicates the existence of a risk premium on crude oil futures.