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Energy-Related Uncertainty and International Stock Market Volatility

Author

Listed:
  • Afees A. Salisu

    (Centre for Econometrics & Applied Research, Ibadan, Nigeria; Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

  • Ahamuefula E. Ogbonna

    (Centre for Econometrics & Applied Research, Ibadan, Nigeria)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

  • Elie Bouri

    (School of Business, Lebanese American University, Lebanon)

Abstract

The aim of this paper is to predict the daily return volatility of 28 developed and developing stock markets based on the monthly metrics of corresponding country and global energy-related uncertainty indexes (EUIs) recently proposed in the literature. Using the generalized autoregressive conditional heteroscedasticity-mixed data sampling (GARCH-MIDAS) framework, the results show that country-specific and global EUIs have predictive powers for stock returns volatility for the in-sample periods, with increased levels of EUIs exhibiting the tendency to heighten volatility. This predictability also withstands various out-of-sample forecast horizons, implying that EUI is a statistically relevant predictor of stock returns volatility in the out-of-sample analysis. Moreover, the forecast precision of the GARCH-MIDAS model is improved by incorporating global EUIs relatively more than country-specific EUIs. Our findings are robust to the choice of EUI proxies and sample definition. They have important implications for investors and policymakers concerned with stability in the global financial system and economy.

Suggested Citation

  • Afees A. Salisu & Ahamuefula E. Ogbonna & Rangan Gupta & Elie Bouri, 2023. "Energy-Related Uncertainty and International Stock Market Volatility," Working Papers 202336, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202336
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    More about this item

    Keywords

    Monthly energy-related uncertainty index; daily stock returns volatility; developed and developing economies; GARCH-MIDAS; predictions;
    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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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