| dc.description.abstract | This thesis examines the frequency of high-volatility regimes in European energy and carbon markets, and their correspondence with the Russo-Ukrainian war using a Markov Switching Bayesian Vector Autoregressive (MS-BVAR) model. Utilizing monthly price data from January 2015 to February 2024, the study identifies significant shifts in volatility and market dynamics triggered by geopolitical events. The findings reveal pronounced regime-dependent variability in market responses, with high volatility regimes becoming more conspicuous post- invasion. In lower volatility regimes, markets typically stabilize quickly post-disturbance, demonstrating resilience. However, in high-volatility regimes, markets exhibit prolonged deviations from baseline levels, indicating deeper impacts of geopolitical risks. Particularly, the oil, gas, and clean energy sectors show significant sensitivity to changes in the geopolitical landscape. Additionally, the thesis highlights the crucial role of the Geopolitical Risk Index (Caldara & Iacoviello, 2022) in understanding the broader effects of geopolitical tensions on energy markets. By integrating geopolitical risk analysis into the MS-BVAR model, the research provides nuanced insights into how shifts in geopolitical stability resonate through the energy markets over time. This research contributes to the empirical literature by demonstrating the differential impacts of geopolitical events across various states of market volatility. It underscores the importance of robust risk management and energy diversification strategies to enhance the resilience of energy systems. | |
