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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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    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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