Volatility forecasting of crude oil futures based on Bi-LSTM-Attention model: The dynamic role of the COVID-19 pandemic and the Russian-Ukrainian conflict

Yan Xu, Tianli Liu and Pei Du

Resources Policy, 2024, vol. 88, issue C

Abstract: The COVID-19 epidemic and the Russian-Ukrainian conflict have created significant uncertainty in the crude oil market, greatly increasing the difficulty of crude oil futures price volatility forecasting. The objective of this study is to investigate the time-varying nature of the impact of major events on the crude oil market and to examine how to accurately predict crude oil futures price volatility during major shocks. To this end, we propose a novel crude oil futures price volatility forecasting framework based on the Bidirectional Long and Short-Term Memory Neural Network-Attention Mechanism Model (Bi-LSTM-Attention), which contains period division, variable screening, model prediction, and assessment indicators, to further analyze in-depth the impacts of COVID-19 and the Russia-Ukraine conflict on the volatility of crude oil futures price. In addition, we also investigate the impact of external factors on the crude oil market, including economic status, geopolitical events, and the COVID-19 pandemic. Empirical evidence demonstrates that the impact of major events on crude oil futures price volatility is significantly time-varying and differentiated. Particularly during high shock periods, the Fake News Index plays an extremely influential role in driving crude oil futures price volatility. Moreover, our proposed model reliably captures the trend of crude oil futures price volatility during these violent shocks. The contributions of this study are to provide valuable insights into the impact of significant events on crude oil price volatility and contribute to a deeper understanding of crude oil market dynamics.

Keywords: Volatility forecasting; Crude oil price; COVID-19; Russian-Ukrainian conflict; Bi-LSTM-Attention (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jrpoli:v:88:y:2024:i:c:s0301420723010309

DOI: 10.1016/j.resourpol.2023.104319

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