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Presidential cycles and time-varying bond–stock market correlations: Evidence from more than two centuries of data

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  • Demirer, Riza
  • Gupta, Rangan

Abstract

Utilizing a DCC-GARCH model to capture 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 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

  • Demirer, Riza & Gupta, Rangan, 2018. "Presidential cycles and time-varying bond–stock market correlations: Evidence from more than two centuries of data," Economics Letters, Elsevier, vol. 167(C), pages 36-39.
    • Handle: RePEc:eee:ecolet:v:167:y:2018:i:c:p:36-39
    DOI: 10.1016/j.econlet.2018.03.006
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    References listed on IDEAS

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    1. Ľuboš Pástor & Pietro Veronesi, 2020. "Political Cycles and Stock Returns," Journal of Political Economy, University of Chicago Press, vol. 128(11), pages 4011-4045.
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    4. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    5. Demirer, Rıza & Lee, Hsiang-Tai & Lien, Donald, 2015. "Does the stock market drive herd behavior in commodity futures markets?," International Review of Financial Analysis, Elsevier, vol. 39(C), pages 32-44.
    6. Christos Kollias & Stephanos Papadamou & Vangelis Arvanitis, 2013. "Symposium - Does Terrorism Affect the Stock-Bond Covariance? Evidence from European Countries," Southern Economic Journal, John Wiley & Sons, vol. 79(4), pages 832-848, April.
    7. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
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    Citations

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    Cited by:

    1. 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.
    2. Polat, Onur & Gupta, Rangan & Cepni, Oguzhan & Ji, Qiang, 2024. "Can municipal bonds hedge US state-level climate risks?," Finance Research Letters, Elsevier, vol. 67(PB).
    3. Dai, Zhifeng & Kang, Jie, 2021. "Bond yield and crude oil prices predictability," Energy Economics, Elsevier, vol. 97(C).
    4. 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.
    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. 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.
    7. 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.
    8. 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).
    9. 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).

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    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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