Crude Oil Prices Forecasting in the Energy Transition Era: Evidence from Geopolitical and Technological Drivers

This study examines crude oil return dynamics in the context of the global energy transition, where decarbonization policies, technological innovation, and shifting energy demand increasingly influence market behavior. We propose a heavy-tailed distributional LSTM framework to jointly model the conditional mean, volatility, and tail risk of West Texas Intermediate (WTI) returns, incorporating key transition-related drivers: carbon allowance returns (ETS), artificial intelligence (AI) activity, electric vehicle (EV) market returns (SPKS), and geopolitical risk (GPR). Granger causality results show that ETS significantly predicts mean returns, reflecting the growing impact of climate policy signals, while AI and EV markets primarily affect volatility, indicating transmission through uncertainty channels. The model adopts a Student-t specification to capture heavy-tailed behavior and extreme price movements. Out-of-sample results reveal limited mean predictability but improved forecasting of return magnitude and tail risk. These findings highlight that, under energy transition dynamics, oil market predictability is increasingly concentrated in the risk dimension rather than in average returns.