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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

    as
    1. Cheng, Chak Hung Jack & Hankins, William B. & Chiu, Ching-Wai (Jeremy), 2016. "Does US partisan conflict matter for the Euro area?," Economics Letters, Elsevier, vol. 138(C), pages 64-67.
    2. Lubos Pástor & Pietro Veronesi, 2012. "Uncertainty about Government Policy and Stock Prices," Journal of Finance, American Finance Association, vol. 67(4), pages 1219-1264, August.
    3. repec:eee:finlet:v:25:y:2018:i:c:p:131-136 is not listed on IDEAS
    4. Azzimonti, Marina, 2018. "Partisan conflict and private investment," Journal of Monetary Economics, Elsevier, vol. 93(C), pages 114-131.
    5. Ma, Lingjie & Koenker, Roger, 2006. "Quantile regression methods for recursive structural equation models," Journal of Econometrics, Elsevier, vol. 134(2), pages 471-506, October.
    6. Pástor, Ľuboš & Veronesi, Pietro, 2013. "Political uncertainty and risk premia," Journal of Financial Economics, Elsevier, vol. 110(3), pages 520-545.
    7. Dopke, Jorg & Pierdzioch, Christian, 2006. "Politics and the stock market: Evidence from Germany," European Journal of Political Economy, Elsevier, vol. 22(4), pages 925-943, December.
    8. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    9. Pedro Santa-Clara & Rossen Valkanov, 2003. "The Presidential Puzzle: Political Cycles and the Stock Market," Journal of Finance, American Finance Association, vol. 58(5), pages 1841-1872, October.
    10. Sim, Nicholas & Zhou, Hongtao, 2015. "Oil prices, US stock return, and the dependence between their quantiles," Journal of Banking & Finance, Elsevier, vol. 55(C), pages 1-8.
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    12. 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.
    13. Martin T. Bohl & Jörg Döpke & Christian Pierdzioch, 2008. "Real-Time Forecasting and Political Stock Market Anomalies: Evidence for the United States," The Financial Review, Eastern Finance Association, vol. 43(3), pages 323-335, August.
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    Cited by:

    1. 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.
    2. Selmi, Refk & Mensi, Walid & Hammoudeh, Shawkat & Bouoiyour, Jamal, 2018. "Is Bitcoin a hedge, a safe haven or a diversifier for oil price movements? A comparison with gold," Energy Economics, Elsevier, vol. 74(C), pages 787-801.

    More about this item

    Keywords

    Partisan Conflict; Realized Volatility; Quantile Regressions;

    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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