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Presidential Cycles and Time-Varying Bond-Stock Correlations: Evidence from More than Two Centuries of Data

Author

Listed:
  • Riza Demirer

    (Department of Economics & Finance, Southern Illinois University Edwardsville, Edwardsville, USA)

  • Rangan Gupta

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

Abstract

This paper examines the effect of presidential cycles on financial market correlations using monthly data for the U.S. stock and government bond returns over the historical period of 1791:09-2017:12. Utilizing a dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (DCC-GARCH) model to capture the time-varying correlations, we show that Democratic administrations are generally associated with lower degree of co-movement between the stock and government bond returns. The observed negative presidential cycle effect is robust over various sub-samples identified by structural break tests. The findings are in line with the documented presidential cycle effect on stock market returns and corroborate recent evidence that, when risk aversion is high, agents tend to elect the Democratic Party.

Suggested Citation

  • Riza Demirer & Rangan Gupta, 2018. "Presidential Cycles and Time-Varying Bond-Stock Correlations: Evidence from More than Two Centuries of Data," Working Papers 201811, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201811
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    Citations

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    Cited by:

    1. Oguzhan Cepni & Riza Demirer & Rangan Gupta & Christian Pierdzioch, 2024. "Political Geography and Stock Market Volatility: The Role of Political Alignment across Sentiment Regimes," Working Papers 202414, University of Pretoria, Department of Economics.
    2. Chang, Kai & Ye, Zhifang & Wang, Weihong, 2019. "Volatility spillover effect and dynamic correlation between regional emissions allowances and fossil energy markets: New evidence from China’s emissions trading scheme pilots," Energy, Elsevier, vol. 185(C), pages 1314-1324.
    3. Dai, Zhifeng & Kang, Jie, 2021. "Bond yield and crude oil prices predictability," Energy Economics, Elsevier, vol. 97(C).
    4. Semei Coronado & Rangan Gupta & Saban Nazlioglu & Omar Rojas, 2023. "Time‐varying causality between bond and oil markets of the United States: Evidence from over one and half centuries of data," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 2239-2247, July.
    5. Çepni, Oğguzhan & Demirer, Riza & Gupta, Rangan & Pierdzioch, Christian, 2020. "Time-varying risk aversion and the predictability of bond premia," Finance Research Letters, Elsevier, vol. 34(C).
    6. Oğuzhan Çepni & Rangan Gupta & Mark E. Wohar, 2021. "Variants of consumption‐wealth ratios and predictability of U.S. government bond risk premia," International Review of Finance, International Review of Finance Ltd., vol. 21(2), pages 661-674, June.
    7. Bouri, Elie & Demirer, Riza & Gupta, Rangan & Wohar, Mark E., 2021. "Gold, platinum and the predictability of bond risk premia," Finance Research Letters, Elsevier, vol. 38(C).
    8. Çepni, Oğuzhan & Guney, I. Ethem & Gupta, Rangan & Wohar, Mark E., 2020. "The role of an aligned investor sentiment index in predicting bond risk premia of the U.S," Journal of Financial Markets, Elsevier, vol. 51(C).
    9. Oguzhan Cepni & Rangan Gupta & Mark E. Wohar, 2019. "Variants of Consumption-Wealth Ratios and Predictability of U.S. Government Bond Risk Premia: Old is still Gold," Working Papers 201912, University of Pretoria, Department of Economics.

    More about this item

    Keywords

    Conditional correlation; GARCH; Bond and Stock Returns Comovement; US Presidential Cycles;
    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
    • 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
    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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