Three Essays on Energy Markets Using Dynamic Time Series

This dissertation investigates energy markets using dynamic time series and rolling panel approaches, aiming to enhance our understanding of their dynamics and interrelationships. The first essay focuses on the transfer of price bubbles between oil and gas markets. The findings indicate that price bubbles in the oil market have the potential to transfer to the gas market, with the probability of transfer depending on the gas price mechanism. The second essay delves into China’s natural gas market and specifically explores the dynamic relationship between imported liquefied natural gas (LNG) and pipeline prices. This analysis utilizes a price discovery framework to examine the market dynamics. The empirical results reveal that prior to the market reforms in 2014, the two markets exhibited some degree of independence. However, post-2014, both markets have started moving together, with the LNG market leading the imported natural gas markets in China. In the third essay, the focus shifts to examining the relationship between CO2 emissions and income growth in the U.S. This analysis employs the Environmental Kuznets curves (EKCs) framework to assess the decoupling between these two variables. The results indicate that the U.S. is currently experiencing decoupling, meaning that income growth is no longer tightly linked to CO2 emissions. However, caution is warranted as the findings also suggest that CO2 emissions are expected to increase in the near future, highlighting the need to take appropriate measures to avoid such a scenario