Uncertainty and fluctuation in crude oil price: evidence from machine learning models

Feng Ma, Xinjie Lu and Bo Zhu ()
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Feng Ma: Southwest Jiaotong University
Xinjie Lu: Southwest Jiaotong University
Bo Zhu: Southwest Jiaotong University

Annals of Operations Research, 2025, vol. 345, issue 2, No 8, 725-755

Abstract: Abstract This study comprehensively investigates the predictability of uncertainty indices for oil market volatility, employing multiple machine learning models based on a large set of uncertainty indices. Empirical findings demonstrate the efficiency of machine learning models for predicting oil futures volatility using uncertainty indices. The results are consistent across various robustness checks and special circumstances. This study highlights the need to combine the efficiency of machine learning models with as much information from uncertainty indices as possible to capture the dynamics of the oil market, which is essential for energy fields to confront future fierce situations and crises.

Keywords: Machine learning; Oil market; Volatility forecasting; COVID-19 pandemic; Business cycles (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10479-023-05463-7

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