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Price Conflict and US Stock Return Volatility Forecasting: Insights from over 150 Years with a Mixed-Frequency Framework

Author

Listed:
  • Afees A. Salisu

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

  • Ahamuefula E. Ogbonna

    (Centre for Econometrics and 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

This paper employs the generalized autoregressive conditional heteroscedasticity-mixed data sampling (GARCH-MIDAS) framework to forecast monthly and daily stock return volatility in the United States (US), based on a quarterly news-based Price Conflict Index (PCI) that signals ``bad macroeconomic news". An analysis of historical monthly (1860-2023) and daily (1885-2023) data demonstrates that the GARCH-MIDAS model incorporating PCI outperforms both the benchmark GARCH-MIDAS model with realized volatility (GARCH-MIDAS-RV) and models with macroeconomic variables such as output growth, inflation, unemployment, and interest rates. Furthermore, the inclusion of the PCI in modeling stock return volatility provides higher utility gains compared to models that exclude it. These findings have important implications for both investors and policymakers.

Suggested Citation

  • Afees A. Salisu & Ahamuefula E. Ogbonna & Rangan Gupta & Elie Bouri, 2026. "Price Conflict and US Stock Return Volatility Forecasting: Insights from over 150 Years with a Mixed-Frequency Framework," Working Papers 202620, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202620
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    References listed on IDEAS

    as
    1. Afees A. S alisu & Wenting Liao & Rangan Gupta & Oguzhan Cepni, 2025. "Economic Conditions and Predictability of US Stock Returns Volatility: Local Factor Versus National Factor in a GARCH‐MIDAS Model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(4), pages 1441-1466, July.
    2. Tim Bollerslev & Benjamin Hood & John Huss & Lasse Heje Pedersen, 2018. "Risk Everywhere: Modeling and Managing Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2729-2773.
    3. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    4. Nadav Ben Zeev & Valerie A. Ramey & Sarah Zubairy, 2023. "Do Government Spending Multipliers Depend on the Sign of the Shock?," AEA Papers and Proceedings, American Economic Association, vol. 113, pages 382-387, May.
    5. Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2015. "Measuring Uncertainty," American Economic Review, American Economic Association, vol. 105(3), pages 1177-1216, March.
    6. Sydney C. Ludvigson & Sai Ma & Serena Ng, 2021. "Uncertainty and Business Cycles: Exogenous Impulse or Endogenous Response?," American Economic Journal: Macroeconomics, American Economic Association, vol. 13(4), pages 369-410, October.
    7. Salisu, Afees A. & Demirer, Riza & Gupta, Rangan, 2024. "Technological shocks and stock market volatility over a century," Journal of Empirical Finance, Elsevier, vol. 79(C).
    8. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    9. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    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. Robert Rowthorn, 2024. "The Conflict Theory of Inflation Revisited," Review of Political Economy, Taylor & Francis Journals, vol. 36(4), pages 1302-1313, October.
    12. Eric Ghysels & Alberto Plazzi & Rossen Valkanov & Antonio Rubia & Asad Dossani, 2019. "Direct Versus Iterated Multiperiod Volatility Forecasts," Annual Review of Financial Economics, Annual Reviews, vol. 11(1), pages 173-195, December.
    Full references (including those not matched with items on IDEAS)

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    Keywords

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    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
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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