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Global economic conditions index and oil price predictability

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  • Lv, Wendai
  • Wu, Qian

Abstract

The numerous academics rely on exogenous drivers to improve the accuracy of oil price forecasting. This study mainly explores whether a new indicator of global economic conditions proposed by Baumeister et al. (2020) can successfully predict the oil price. Our empirical results reveal that the global economic conditions index can extremely improve the accuracy in forecasting oil price in terms of univariate and bivariate analysis. In addition, compared with 14 traditional macroeconomic variables, global economic conditions index exhibits incremental predictive content in forecasting oil price. The longer forecasting horizons analysis confirms the superior forecasting performance of the global economic condition index for short-term (h = 2 and h = 3). Our findings can provide important implications to market participants in crude oil market.

Suggested Citation

  • Lv, Wendai & Wu, Qian, 2022. "Global economic conditions index and oil price predictability," Finance Research Letters, Elsevier, vol. 48(C).
    • Handle: RePEc:eee:finlet:v:48:y:2022:i:c:s1544612322001891
    DOI: 10.1016/j.frl.2022.102919
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    Cited by:

    1. Zouhaier Dhifaoui & Sami Ben Jabeur & Rabeh Khalfaoui & Muhammad Ali Nasir, 2023. "Time‐varying partial‐directed coherence approach to forecast global energy prices with stochastic volatility model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2292-2306, December.
    2. Gozgor, Giray & Khalfaoui, Rabeh & Yarovaya, Larisa, 2023. "Global supply chain pressure and commodity markets: Evidence from multiple wavelet and quantile connectedness analyses," Finance Research Letters, Elsevier, vol. 54(C).
    3. Rangan Gupta & Christian Pierdzioch, 2023. "Do U.S. economic conditions at the state level predict the realized volatility of oil-price returns? A quantile machine-learning approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-22, December.
    4. Wang, Lu & Ruan, Hang & Lai, Xiaodong & Li, Dongxin, 2024. "Economic extremes steering renewable energy trajectories: A time-frequency dissection of global shocks," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    5. Kliber, Agata & Łęt, Blanka & Řezáč, Pavel, 2024. "Can a boost in oil prices suspend the evolution of the green transportation market? Relationships between green indices and Brent oil," Energy, Elsevier, vol. 295(C).
    6. Liang, Xuedong & Luo, Peng & Li, Xiaoyan & Wang, Xia & Shu, Lingli, 2023. "Crude oil price prediction using deep reinforcement learning," Resources Policy, Elsevier, vol. 81(C).
    7. Zhang, Lixia & Bai, Jiancheng & Zhang, Yueyan & Cui, Can, 2023. "Global economic uncertainty and the Chinese stock market: Assessing the impacts of global indicators," Research in International Business and Finance, Elsevier, vol. 65(C).
    8. Bouteska, Ahmed & Hajek, Petr & Fisher, Ben & Abedin, Mohammad Zoynul, 2023. "Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network," Research in International Business and Finance, Elsevier, vol. 64(C).

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