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Role of oil shocks in US stock market volatility: A new insight from GARCH-MIDAS perspective

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  • Ghani, Usman
  • Zhu, Bo
  • Ghani, Maria
  • Khan, Nasir
  • khan, Raja Danish Akbar

Abstract

For both local and foreign investors, the equity market and oil price shocks have massive repercussions. In this research, we examine the essential role of the oil shock in predicting the U.S. stock market volatility. The oil shock measures include NPI (net price increase), ANP (asymmetric net prices change), SNP (symmetric net price change), LPI (large price increase), and NPI2 (net price increase) indicators. We select the GARCH-MIDAS model to estimate the volatility. The study provides several notable outcomes. First, in all five oil shocks, symmetric net price change (SNP) information is more useful for forecasting the volatility of the U.S. stock market. Further, we find some evidence for the net price increase (NPI) and asymmetric net price change information (ANP) in some estimation windows. Also, the Covid-19 epidemic provides proof. Our results are robust in the alternative valuation methods, MCS (model confidence set) test, and alternative estimation windows.

Suggested Citation

  • Ghani, Usman & Zhu, Bo & Ghani, Maria & Khan, Nasir & khan, Raja Danish Akbar, 2023. "Role of oil shocks in US stock market volatility: A new insight from GARCH-MIDAS perspective," Resources Policy, Elsevier, vol. 85(PB).
  • Handle: RePEc:eee:jrpoli:v:85:y:2023:i:pb:s030142072300644x
    DOI: 10.1016/j.resourpol.2023.103933
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    1. Umar, Zaghum & Trabelsi, Nader & Zaremba, Adam, 2021. "Oil shocks and equity markets: The case of GCC and BRICS economies," Energy Economics, Elsevier, vol. 96(C).
    2. Hamilton, James D., 1996. "This is what happened to the oil price-macroeconomy relationship," Journal of Monetary Economics, Elsevier, vol. 38(2), pages 215-220, October.
    3. Inoue, Atsushi & Jin, Lu & Rossi, Barbara, 2017. "Rolling window selection for out-of-sample forecasting with time-varying parameters," Journal of Econometrics, Elsevier, vol. 196(1), pages 55-67.
    4. Campbell, John Y, 1991. "A Variance Decomposition for Stock Returns," Economic Journal, Royal Economic Society, vol. 101(405), pages 157-179, March.
    5. Basher, Syed A. & Sadorsky, Perry, 2006. "Oil price risk and emerging stock markets," Global Finance Journal, Elsevier, vol. 17(2), pages 224-251, December.
    6. Hamilton, James D., 2011. "Nonlinearities And The Macroeconomic Effects Of Oil Prices," Macroeconomic Dynamics, Cambridge University Press, vol. 15(S3), pages 364-378, November.
    7. Pindyck, Robert S, 1991. "Irreversibility, Uncertainty, and Investment," Journal of Economic Literature, American Economic Association, vol. 29(3), pages 1110-1148, September.
    8. Bastianin, Andrea & Conti, Francesca & Manera, Matteo, 2016. "The impacts of oil price shocks on stock market volatility: Evidence from the G7 countries," Energy Policy, Elsevier, vol. 98(C), pages 160-169.
    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. Robert F. Engle & Eric Ghysels & Bumjean Sohn, 2013. "Stock Market Volatility and Macroeconomic Fundamentals," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 776-797, July.
    11. Ben S. Bernanke, 1983. "Irreversibility, Uncertainty, and Cyclical Investment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 98(1), pages 85-106.
    12. Ding, Yi & Kambouroudis, Dimos & McMillan, David G., 2021. "Forecasting realised volatility: Does the LASSO approach outperform HAR?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    13. Antonakakis, Nikolaos & Chatziantoniou, Ioannis & Filis, George, 2017. "Oil shocks and stock markets: Dynamic connectedness under the prism of recent geopolitical and economic unrest," International Review of Financial Analysis, Elsevier, vol. 50(C), pages 1-26.
    14. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    15. Lutz Kilian & Cheolbeom Park, 2009. "The Impact Of Oil Price Shocks On The U.S. Stock Market," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(4), pages 1267-1287, November.
    16. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    17. Mork, Knut Anton, 1989. "Oil and Macroeconomy When Prices Go Up and Down: An Extension of Hamilton's Results," Journal of Political Economy, University of Chicago Press, vol. 97(3), pages 740-744, June.
    18. David E. Rapach & Jack K. Strauss & Guofu Zhou, 2010. "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy," The Review of Financial Studies, Society for Financial Studies, vol. 23(2), pages 821-862, February.
    19. Jiqian Wang & Yisu Huang & Feng Ma & Julien Chevallier, 2020. "Does high-frequency crude oil futures data contain useful information for predicting volatility in the US stock market? New evidence," Post-Print halshs-04250251, HAL.
    20. Salisu, Afees A. & Gupta, Rangan, 2021. "Oil shocks and stock market volatility of the BRICS: A GARCH-MIDAS approach," Global Finance Journal, Elsevier, vol. 48(C).
    21. Zhang, Dayong & Shi, Min & Shi, Xunpeng, 2018. "Oil indexation, market fundamentals, and natural gas prices: An investigation of the Asian premium in natural gas trade," Energy Economics, Elsevier, vol. 69(C), pages 33-41.
    22. Zhang, Yaojie & Wei, Yu & Zhang, Yi & Jin, Daxiang, 2019. "Forecasting oil price volatility: Forecast combination versus shrinkage method," Energy Economics, Elsevier, vol. 80(C), pages 423-433.
    23. Robert F. Engle & Jose Gonzalo Rangel, 2008. "The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes," The Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1187-1222, May.
    24. Lu, Xinjie & Ma, Feng & Wang, Jiqian & Zhu, Bo, 2021. "Oil shocks and stock market volatility: New evidence," Energy Economics, Elsevier, vol. 103(C).
    25. Xu, Weiju & Ma, Feng & Chen, Wang & Zhang, Bing, 2019. "Asymmetric volatility spillovers between oil and stock markets: Evidence from China and the United States," Energy Economics, Elsevier, vol. 80(C), pages 310-320.
    26. Basher, Syed Abul & Haug, Alfred A. & Sadorsky, Perry, 2018. "The impact of oil-market shocks on stock returns in major oil-exporting countries," Journal of International Money and Finance, Elsevier, vol. 86(C), pages 264-280.
    27. Wang, Jiqian & Huang, Yisu & Ma, Feng & Chevallier, Julien, 2020. "Does high-frequency crude oil futures data contain useful information for predicting volatility in the US stock market? New evidence," Energy Economics, Elsevier, vol. 91(C).
    28. Nusair, Salah A., 2016. "The effects of oil price shocks on the economies of the Gulf Co-operation Council countries: Nonlinear analysis," Energy Policy, Elsevier, vol. 91(C), pages 256-267.
    29. Bouri, Elie, 2015. "Return and volatility linkages between oil prices and the Lebanese stock market in crisis periods," Energy, Elsevier, vol. 89(C), pages 365-371.
    30. 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.
    31. Ma, Feng & Zhang, Yaojie & Huang, Dengshi & Lai, Xiaodong, 2018. "Forecasting oil futures price volatility: New evidence from realized range-based volatility," Energy Economics, Elsevier, vol. 75(C), pages 400-409.
    32. Wang, Yudong & Ma, Feng & Wei, Yu & Wu, Chongfeng, 2016. "Forecasting realized volatility in a changing world: A dynamic model averaging approach," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 136-149.
    33. Li, Tao & Ma, Feng & Zhang, Xuehua & Zhang, Yaojie, 2020. "Economic policy uncertainty and the Chinese stock market volatility: Novel evidence," Economic Modelling, Elsevier, vol. 87(C), pages 24-33.
    34. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
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