The field of energy finance is currently confronted with new challenges in the ever-evolving global landscape, characterized by dynamic changes in fundamental factors and shifting economic conditions. Understanding the information that influences energy product prices continues to be a promising yet challenging aspect within financial research in the 2020s.

This dissertation addresses key challenges in energy finance studies in the current era. It examines the impact of price uncertainty on oil investments, focusing on decision-making under tail risks. Additionally, it investigates the information spread in natural gas markets, including the motivations and accuracy of forecasters. Furthermore, it explores the shift from pit trading to electronic trading in oil markets, analyzing price discovery in the US and Europe. The research aims to provide valuable insights for investors, decision-makers, and market participants in navigating the complexities of energy finance.

Chapter 1 emphasizes the significance of incorporating non-normality assumptions in analyzing real option investments. It assesses the impact of price change tail risks - skewness, kurtosis, and volatility on the likelihood of exercising real options by scrutinizing oil well operators' investment decisions. The results demonstrate a significant influence of these factors, revealing the substantial implications of tail risks on real investments: a one standard deviation increase in skewness escalates the closing probability by 13%, while a comparable rise in kurtosis augments the drilling probability by 11%. In contrast, volatility diminishes these probabilities, with reductions of 44% and 33% in closing and drilling respectively. These robust findings dismiss the theory of personal preferences as an explanatory factor for these impacts.

Chapter 2 undertakes an investigation into the relations between the decision-making attributes, herding and timing choices, and forecast accuracy in the realm of natural gas storage change forecasts. The empirical evidence reveals a prevalence of anti-herding, despite the negative association between anti-herding and accuracy. Forecasts issued later in each forecasting cycle demonstrate higher accuracy. Decision-making with respect to herding and timing contributes to approximately 50% of the cross-sectional variation in accuracy. We discern an enhancement in accuracy with a moderate anti-herding decline since 2013; however, these developments are contingent upon forecasters' average performance or timing choices. The results suggest that forecasters make trade-offs between forecast accuracy with signaling considerations and early dissemination advantages.

Chapter 3 studies the impact on price discovery when commodity futures move to electronic trading, using data from the West Texas Intermediate (WTI) and Brent oil markets. Electronic trading resulted in explosive increases in trading in both futures markets. After the shift, WTI futures became significantly more important for (indeed, the sole contributor to) price discovery but Brent futures became significantly less important. Our findings show that liquidity increases are not necessarily accompanied by price discovery improvements and suggest that the benefits of electronic trading need to be assessed from the standpoint of liquidity improvements as well as price discovery changes.