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Does Partisan Conflict Predict a Reduction in US Stock Market (Realized) Volatility? Evidence from a Quantile-on-Quantile Regression Model

Author

Listed:
  • Rangan Gupta

    (Department of Economics, University of Pretoria, Pretoria, South Africa)

  • Christian Pierdzioch

    (Department of Economics, Helmut Schmidt University, Hamburg, Germany)

  • Refk Selmi

    (University of Tunis, Campus Universitaire, Tunis, Tunisia and University of Pau, France)

  • Mark E. Wohar

    (College of Business Administration, University of Nebraska at Omaha, Omaha, USA and School of Business and Economics, Loughborough University, Leicestershire, UK)

Abstract

Theory suggests that partisan conflict negatively affects the possibility of economic policy change, implying that financial markets tend to operate under lower policy risk. Given that stock-return volatility measures risk, if the gridlock argument holds, stock–market volatility should be lower under divided than under a unified government. Using a partisan conflict index (PCI), we empirically confirm this theoretical argument for the U.S. stock market based on quantiles-based regressions. In particular, quantile-on-quantile regressions show that PCI tends to predict reduced volatility, with the effect being stronger at levels of volatility that are moderately high (i.e., beyond the median, but not at its extreme) for an increase in the predictor, especially with lower initial values (i.e., when PCI is at its lower quantiles).

Suggested Citation

  • Rangan Gupta & Christian Pierdzioch & Refk Selmi & Mark E. Wohar, 2017. "Does Partisan Conflict Predict a Reduction in US Stock Market (Realized) Volatility? Evidence from a Quantile-on-Quantile Regression Model," Working Papers 201744, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201744
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    References listed on IDEAS

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    9. Gupta, Rangan & Mwamba, John W. Muteba & Wohar, Mark E., 2018. "The role of partisan conflict in forecasting the U.S. equity premium: A nonparametric approach," Finance Research Letters, Elsevier, vol. 25(C), pages 131-136.
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    2. Bouoiyour, Jamal & Selmi, Refk & Wohar, Mark E., 2018. "Measuring the response of gold prices to uncertainty: An analysis beyond the mean," Economic Modelling, Elsevier, vol. 75(C), pages 105-116.

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    More about this item

    Keywords

    Partisan Conflict; Realized Volatility; Quantile Regressions;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E60 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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