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

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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.

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Notes

  1. More technical details can be found in Hansen et al. (2011).

  2. For more details, refer to https://www.nber.org/research/business-cycle-dating.

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Authors and Affiliations

  1. School of Economics and Management, Southwest Jiaotong University, Chengdu, China

    Feng Ma, **njie Lu & Bo Zhu

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  1. Feng Ma
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  2. **njie Lu
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  3. Bo Zhu
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FM: Methodology, software, investigation, and writing—original draft; XL: Data curation, methodology, software, project administration, and funding acquisition; BZ: Methodology, software, formal analysis, investigation, and writing—review & editing.

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Correspondence to Bo Zhu.

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Ma, F., Lu, X. & Zhu, B. Uncertainty and fluctuation in crude oil price: evidence from machine learning models. Ann Oper Res (2023). https://doi.org/10.1007/s10479-023-05463-7

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