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Crude oil price volatility and short-term predictability of the real U.S. GDP growth rate

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  • Nonejad, Nima

Abstract

The predictive power contained in crude oil price volatility with regards to forecasting the real U.S. GDP growth rate is evaluated. Contrary to models based on the price of crude oil, specifications employing crude oil price volatility tend to afford statistically significant improvements in terms of population level-predictability and finite-sample forecast accuracy relative to the benchmark at the one-quarter ahead horizon.

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  • Nonejad, Nima, 2020. "Crude oil price volatility and short-term predictability of the real U.S. GDP growth rate," Economics Letters, Elsevier, vol. 186(C).
    • Handle: RePEc:eee:ecolet:v:186:y:2020:i:c:s0165176519302514
    DOI: 10.1016/j.econlet.2019.108527
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    References listed on IDEAS

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    Citations

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    Cited by:

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    2. Xu, Kunliang & Wang, Weiqing, 2023. "Limited information limits accuracy: Whether ensemble empirical mode decomposition improves crude oil spot price prediction?," International Review of Financial Analysis, Elsevier, vol. 87(C).
    3. Cui, Lianbiao & Weng, Shimei & Kirikkaleli, Dervis & Bashir, Muhammad Adnan & Rjoub, Husam & Zhou, Yuanxiang, 2021. "Exploring the role of natural resources, natural gas and oil production for economic growth of China," Resources Policy, Elsevier, vol. 74(C).
    4. Nima Nonejad, 2022. "New Findings Regarding the Out-of-Sample Predictive Impact of the Price of Crude Oil on the United States Industrial Production," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 18(1), pages 1-35, March.
    5. Cao, Yanyan & Xiang, Shihui, 2023. "Natural resources volatility and causal associations for BRICS countries: Evidence from Covid-19 data," Resources Policy, Elsevier, vol. 80(C).
    6. Cheng, WeiJin & Ming, Kai & Ullah, Mirzat, 2024. "Oil price volatility prediction using out-of-sample analysis – Prediction efficiency of individual models, combination methods, and machine learning based shrinkage methods," Energy, Elsevier, vol. 300(C).
    7. Elder, John, 2021. "Canadian industry level production and energy prices," Energy Economics, Elsevier, vol. 99(C).
    8. Fernandez-Perez, Adrian & Indriawan, Ivan & Tse, Yiuman & Xu, Yahua, 2023. "Cross-asset time-series momentum: Crude oil volatility and global stock markets," Journal of Banking & Finance, Elsevier, vol. 154(C).
    9. Xiao, Jihong & Wang, Yudong, 2022. "Good oil volatility, bad oil volatility, and stock return predictability," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 953-966.
    10. Liu, Fang & Umair, Muhammad & Gao, Junjun, 2023. "Assessing oil price volatility co-movement with stock market volatility through quantile regression approach," Resources Policy, Elsevier, vol. 81(C).
    11. Zhang, Yonggang & Hyder, Mansoor & Baloch, Zulfiqar Ali & Qian, Chong & Berk Saydaliev, Hayot, 2022. "Nexus between oil price volatility and inflation: Mediating nexus from exchange rate," Resources Policy, Elsevier, vol. 79(C).
    12. Xu, Lan & Wu, Yang, 2023. "Nexus between green finance, renewable energy and carbon emission: Empirical evidence from selected Asian economies," Renewable Energy, Elsevier, vol. 215(C).

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    More about this item

    Keywords

    Crude oil; Forecast evaluation; GDP growth rate; Realized volatility;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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