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Macroeconomic attention and oil futures volatility prediction

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  • Liu, Shan
  • Li, Ziwei

Abstract

This paper mainly checks whether the macroeconomic attention indices have valuable information to predict the oil futures volatility. Results show that macroeconomic attention indices are able to predict the oil futures volatility. In addition, based on several dimensionality reduction methods, we find that the scaled principal component analysis (SPCA) model has better predictive performances than other dimensionality reduction methods. Especially, the least absolute shrinkage and selection operator method (LASSO) has the best predictive performance. During the COVID-19 period, LASSO model with the macroeconomic attention indices can still have superior performances. This paper tries to show new evidence based on macroeconomic attention indices for oil market volatility.

Suggested Citation

  • Liu, Shan & Li, Ziwei, 2023. "Macroeconomic attention and oil futures volatility prediction," Finance Research Letters, Elsevier, vol. 57(C).
    • Handle: RePEc:eee:finlet:v:57:y:2023:i:c:s1544612323005391
    DOI: 10.1016/j.frl.2023.104167
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    References listed on IDEAS

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    1. Chao Liang & Yanran Hong & Luu Duc Toan Huynh & Feng Ma, 2023. "Asymmetric dynamic risk transmission between financial stress and monetary policy uncertainty: thinking in the post-covid-19 world," Review of Quantitative Finance and Accounting, Springer, vol. 60(4), pages 1543-1567, May.
    2. Guo, Yangli & He, Feng & Liang, Chao & Ma, Feng, 2022. "Oil price volatility predictability: New evidence from a scaled PCA approach," Energy Economics, Elsevier, vol. 105(C).
    3. 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.
    4. Zhang, Yaojie & Ma, Feng & Wang, Yudong, 2019. "Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors?," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 97-117.
    5. Adlai Fisher & Charles Martineau & Jinfei Sheng, 2022. "Macroeconomic Attention and Announcement Risk Premia," The Review of Financial Studies, Society for Financial Studies, vol. 35(11), pages 5057-5093.
    6. 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.
    7. Lu, Xinjie & Ma, Feng & Wang, Jiqian & Zhu, Bo, 2021. "Oil shocks and stock market volatility: New evidence," Energy Economics, Elsevier, vol. 103(C).
    8. Ma, Feng & Lu, Xinjie & Liu, Jia & Huang, Dengshi, 2022. "Macroeconomic attention and stock market return predictability," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    9. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    10. Dashan Huang & Fuwei Jiang & Kunpeng Li & Guoshi Tong & Guofu Zhou, 2022. "Scaled PCA: A New Approach to Dimension Reduction," Management Science, INFORMS, vol. 68(3), pages 1678-1695, March.
    11. Nima Nonejad, 2020. "A detailed look at crude oil price volatility prediction using macroeconomic variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1119-1141, November.
    12. Liang, Chao & Umar, Muhammad & Ma, Feng & Huynh, Toan L.D., 2022. "Climate policy uncertainty and world renewable energy index volatility forecasting," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    13. Feng Ma & Chao Liang & Qing Zeng & Haibo Li, 2021. "Jumps and oil futures volatility forecasting: a new insight," Quantitative Finance, Taylor & Francis Journals, vol. 21(5), pages 853-863, May.
    Full references (including those not matched with items on IDEAS)

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