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Oil Price Shocks and Stock Market Bubbles

Author

Listed:
  • Elie Bouri

    (School of Business, Lebanese American University, Lebanon)

  • Ufuk Can

    (Central Bank of the Republic of Turkiye, Adana, Turkiye; Centre for Applied Macroeconomic Analysis, Australian National University, Canberra, Australia; Economic Research Forum, Cairo, Egypt)

  • Oguzhan Cepni

    (Ostim Technical University, Ankara, Turkiye; University of Edinburgh Business School, Centre for Business, Climate Change, and Sustainability; Department of Economics, Copenhagen Business School, Denmark)

  • Rangan Gupta

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

Abstract

This paper investigates the impact of real-time structural oil price (forward-looking demand, current demand, and supply) shocks on daily US stock market positive and negative bubbles across short-, medium-, and long-terms from June 2007 to January 2025. The results from local projection models show strong and asymmetric impact, which is generally contingent on the nature of oil price shocks. Forward-looking demand shocks can drive stock market crashes or significant recoveries, by providing a bad or good signal contingent on the initial condition of the stock market. Current demand shocks tend to behave pro-cyclically, reinforcing medium-term positive bubbles while suppressing negative ones. In contrast, oil supply shocks are destabilizers, dampening conditions for positive bubbles and amplifying negative ones. These findings demonstrate that the US stock market’s boom–bust dynamics are significantly affected by the structural source of oil price movements, which offers important implications for policymakers and investors seeking to anticipate and mitigate financial instability.

Suggested Citation

  • Elie Bouri & Ufuk Can & Oguzhan Cepni & Rangan Gupta, 2025. "Oil Price Shocks and Stock Market Bubbles," Working Papers 202546, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202546
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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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