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Policy uncertainty and stock market volatility revisited: The predictive role of signal quality

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  • Afees A. Salisu
  • Riza Demirer
  • Rangan Gupta

Abstract

This paper provides novel insight into the growing literature on the policy uncertainty‐stock market volatility nexus by examining the out‐of‐sample predictive ability of the quality of political signals over stock market volatility at various forecast horizons. Specifically, we examine whether or not accounting for the signal quality in forecasting models within a mixed frequency framework can improve forecast performance and help achieve economic gains for investors. Both in‐ and out‐of‐sample tests, based on a GARCH‐MIDAS framework, show that the quality of the policy signal matters regarding the predictive role of policy uncertainty over subsequent stock market volatility. While high economic policy uncertainty (EPU) predicts high volatility, particularly when the signal quality is high, the positive relationship between EPU and volatility breaks down when the signal quality is low. The improved out‐of‐sample volatility forecasts obtained from the models that account for the quality of policy signals also help typical mean–variance investors achieve improved economic outcomes captured by higher certainty equivalent returns and Sharpe ratios. Although our results indicate clear distinctions between the US and UK stock markets in terms of how market participants process policy signals, they highlight the role of the quality of policy signals as a driver of volatility forecasts with significant economic implications.

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  • Afees A. Salisu & Riza Demirer & Rangan Gupta, 2023. "Policy uncertainty and stock market volatility revisited: The predictive role of signal quality," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2307-2321, December.
  • Handle: RePEc:wly:jforec:v:42:y:2023:i:8:p:2307-2321
    DOI: 10.1002/for.3016
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    Cited by:

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    2. Salisu, Afees A. & Ogbonna, Ahamuefula E. & Gupta, Rangan & Bouri, Elie, 2024. "Energy-related uncertainty and international stock market volatility," The Quarterly Review of Economics and Finance, Elsevier, vol. 95(C), pages 280-293.
    3. Shovon Sengupta & Tanujit Chakraborty & Sunny Kumar Singh, 2023. "Forecasting CPI inflation under economic policy and geopolitical uncertainties," Papers 2401.00249, arXiv.org, revised Jul 2024.
    4. Afees A. Salisu & Ahamuefula E.Oghonna & Rangan Gupta & Oguzhan Cepni, 2024. "Energy Market Uncertainties and US State-Level Stock Market Volatility: A GARCH-MIDAS Approach," Working Papers 202409, University of Pretoria, Department of Economics.
    5. Afees A. Salisu & Ahamuefula E. Ogbonna & Elie Bouri & Rangan Gupta, 2024. "Economic Policy Uncertainty and Bank-Level Stock Returns Volatility of the United States: A Mixed-Frequency Perspective," Working Papers 202444, University of Pretoria, Department of Economics.

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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
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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