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Uncertainties and oil price volatility: Can lasso help?

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Listed:
  • Li, Xinyu
  • Wu, Meng
  • Yuan, Luqi
  • Xiao, Meng
  • Zhong, Ronghao
  • Yu, Miao

Abstract

In this study, we examine the predictive ability of G7 economic policy uncertainties (EPU) on oil market volatility using simple autoregressive and LASSO models. The out-of-sample empirical results show that the EPU of France is helpful for predicting crude oil volatility (WTI and Brent). More importantly, the LASSO model including the G7 uncertainties can exhibit the best predictive performance compared to other competing models based on different tests and during COVID-19.

Suggested Citation

  • Li, Xinyu & Wu, Meng & Yuan, Luqi & Xiao, Meng & Zhong, Ronghao & Yu, Miao, 2024. "Uncertainties and oil price volatility: Can lasso help?," Finance Research Letters, Elsevier, vol. 61(C).
  • Handle: RePEc:eee:finlet:v:61:y:2024:i:c:s1544612323013351
    DOI: 10.1016/j.frl.2023.104963
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    References listed on IDEAS

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    1. Zhang, Dayong & Ji, Qiang & Kutan, Ali M., 2019. "Dynamic transmission mechanisms in global crude oil prices: Estimation and implications," Energy, Elsevier, vol. 175(C), pages 1181-1193.
    2. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    3. Wang, Xiong & Li, Jingyao & Ren, Xiaohang, 2022. "Asymmetric causality of economic policy uncertainty and oil volatility index on time-varying nexus of the clean energy, carbon and green bond," International Review of Financial Analysis, Elsevier, vol. 83(C).
    4. Chao Liang & Yu Wei & Xiafei Li & Xuhui Zhang & Yifeng Zhang, 2020. "Uncertainty and crude oil market volatility: new evidence," Applied Economics, Taylor & Francis Journals, vol. 52(27), pages 2945-2959, May.
    5. Mohammed, Kamel Si & Obeid, Hassan & Oueslati, Karim & Kaabia, Olfa, 2023. "Investor sentiments, economic policy uncertainty, US interest rates, and financial assets: Examining their interdependence over time," Finance Research Letters, Elsevier, vol. 57(C).
    6. Assaf, Ata & Charif, Husni & Mokni, Khaled, 2021. "Dynamic connectedness between uncertainty and energy markets: Do investor sentiments matter?," Resources Policy, Elsevier, vol. 72(C).
    7. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    8. Zhang, Yaojie & He, Mengxi & Wang, Yudong & Liang, Chao, 2023. "Global economic policy uncertainty aligned: An informative predictor for crude oil market volatility," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1318-1332.
    9. Ma, Feng & Liu, Jing & Wahab, M.I.M. & Zhang, Yaojie, 2018. "Forecasting the aggregate oil price volatility in a data-rich environment," Economic Modelling, Elsevier, vol. 72(C), pages 320-332.
    10. Wei, Yu & Liu, Jing & Lai, Xiaodong & Hu, Yang, 2017. "Which determinant is the most informative in forecasting crude oil market volatility: Fundamental, speculation, or uncertainty?," Energy Economics, Elsevier, vol. 68(C), pages 141-150.
    11. Xiafei Li & Yu Wei & Xiaodan Chen & Feng Ma & Chao Liang & Wang Chen, 2022. "Which uncertainty is powerful to forecast crude oil market volatility? New evidence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4279-4297, October.
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