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The Impact of Macroeconomic News Surprises and Uncertainty of Major Economies on Returns and Volatility of Oil Futures

Author

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  • Walid Bahloul

    (Governance, Finance and Accounting Laboratory, Faculty of Business and Economics, University of Sfax, Sfax, Tunisia)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Pretoria, South Africa)

Abstract

Unlike the literature on macroeconomic news surprises and oil markets, which concentrates on spot prices and US news primarily, we analyze the impact of macroeconomic news surprises of Canada, Euro area, Japan, and UK (besides the US) on returns and volatility of oil futures for the West Texas Intermediate and Brent crude. We look at futures markets, since it is widely believe to predict the spot market movements. In addition, we also analyze possibility of asymmetric impact due to good and bad macroeconomic news surprises, as well as, the role of economic uncertainty of these economies in affecting the oil futures markets movements. We can draw two major conclusions: (a) Macroeconomic surprises, as well as uncertainties, of other economies (over and above that of the US) are found to be important in driving oil futures, with the effect of these other economies being relatively stronger than the US in some instances, and; (b) There is strong evidence of asymmetric effects, especially for volatility.

Suggested Citation

  • Walid Bahloul & Rangan Gupta, 2017. "The Impact of Macroeconomic News Surprises and Uncertainty of Major Economies on Returns and Volatility of Oil Futures," Working Papers 201715, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201715
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    7. Ecenur Ugurlu‐Yildirim & Baris Kocaarslan & Beyza M. Ordu‐Akkaya, 2021. "Monetary policy uncertainty, investor sentiment, and US stock market performance: New evidence from nonlinear cointegration analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 1724-1738, April.
    8. Maghyereh, Aktham & Abdoh, Hussein, 2020. "The tail dependence structure between investor sentiment and commodity markets," Resources Policy, Elsevier, vol. 68(C).
    9. Rangan Gupta & Christian Pierdzioch, 2021. "Forecasting the Volatility of Crude Oil: The Role of Uncertainty and Spillovers," Energies, MDPI, vol. 14(14), pages 1-15, July.
    10. Wang, Jiqiang & Dai, Peng-Fei & Zhang, Xuewen, 2024. "Untangling the entanglement of US monetary policy uncertainty and European natural gas and carbon prices," Energy Economics, Elsevier, vol. 133(C).
    11. Taicir Mezghani & Mouna Boujelbène Abbes, 2023. "Forecast the Role of GCC Financial Stress on Oil Market and GCC Financial Markets Using Convolutional Neural Networks," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 30(3), pages 505-530, September.
    12. Wang, Gaoshan & Yu, Guangjin & Shen, Xiaohong, 2021. "The effect of online environmental news on green industry stocks: The mediating role of investor sentiment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    13. Bouzgarrou, Houssam & Ftiti, Zied & Louhichi, Waël & Yousfi, Mohamed, 2023. "What can we learn about the market reaction to macroeconomic surprise? Evidence from the COVID-19 crisis," Research in International Business and Finance, Elsevier, vol. 64(C).
    14. Konstantinos Gkillas & Dimitrios Vortelinos & Christos Floros & Athanasios Tsagkanos, 2019. "Economic News Releases and Financial Markets in South Africa," Economies, MDPI, vol. 7(4), pages 1-13, November.
    15. Bonnier, Jean-Baptiste, 2022. "Forecasting crude oil volatility with exogenous predictors: As good as it GETS?," Energy Economics, Elsevier, vol. 111(C).
    16. Soriano, Pilar & Torró, Hipòlit, 2022. "The response of Brent crude oil to the European central bank monetary policy," Finance Research Letters, Elsevier, vol. 46(PA).

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    More about this item

    Keywords

    Macroeconomic news surprises; Uncertainty; Oil Futures; Returns and 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
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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