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Alternative estimates of the presidential premium

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  • Sean D. Campbell
  • Canlin Li
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    Abstract

    Since the early 1980s much research, including the most recent contribution of Santa-Clara and Valkanov (2003), has concluded that there is a stable, robust and significant relationship between Democratic presidential administrations and robust stock returns. Moreover, the difference in returns does not appear to be accompanied by any significant differences in risk across the presidential cycle. These conclusions are largely based on OLS estimates of the difference in returns across the presidential cycle. We re-examine this issue using more efficient estimators of the presidential premium. Specifically, we exploit the considerable and persistent heteroskedasticity in stock returns to construct more efficient weighted least squares (WLS) and generalized autoregressive conditional heteroskedasticity (GARCH) estimators of the difference in expected excess stock returns across the presidential cycle. Our findings provide considerable contrast to the findings of previous research. Across the different WLS and GARCH estimates we find that the point estimates are considerably smaller than the OLS estimates and fluctuate considerably across different sub samples. We show that the large difference between the WLS, GARCH and OLS estimates is driven by differing stock market performance during very volatile market environments. During periods of elevated market volatility, excess stock returns have been markedly higher under Democratic than Republican administrations. Accordingly, the WLS and GARCH estimators are less sensitive to these episodes than the OLS estimator. Ultimately, these results are consistent with the conclusion that neither risk nor return varies significantly across the presidential cycle.

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    Bibliographic Info

    Paper provided by Board of Governors of the Federal Reserve System (U.S.) in its series Finance and Economics Discussion Series with number 2004-69.

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    Date of creation: 2004
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    Handle: RePEc:fip:fedgfe:2004-69

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    Keywords: Stock market ; Rate of return;

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    References

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    1. Whitney K. Newey & Kenneth D. West, 1986. "A Simple, Positive Semi-Definite, Heteroskedasticity and AutocorrelationConsistent Covariance Matrix," NBER Technical Working Papers 0055, National Bureau of Economic Research, Inc.
    2. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    3. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    4. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2002. "Parametric and Nonparametric Volatility Measurement," Center for Financial Institutions Working Papers 02-27, Wharton School Center for Financial Institutions, University of Pennsylvania.
    5. Pagan, A.R. & Schwert, G.W., 1989. "Alternative Models For Conditional Stock Volatility," Papers 89-02, Rochester, Business - General.
    6. John Y. Campbell & Ludger Hentschel, 1991. "No News is Good News: An Asymmetric Model of Changing Volatility in Stock Returns," NBER Working Papers 3742, National Bureau of Economic Research, Inc.
    7. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
    8. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
    9. Sassan Alizadeh & Michael W. Brandt & Francis X. Diebold, 2002. "Range-Based Estimation of Stochastic Volatility Models," Journal of Finance, American Finance Association, vol. 57(3), pages 1047-1091, 06.
    10. French, Kenneth R. & Schwert, G. William & Stambaugh, Robert F., 1987. "Expected stock returns and volatility," Journal of Financial Economics, Elsevier, vol. 19(1), pages 3-29, September.
    11. 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.
    12. Hsieh, David A & Miller, Merton H, 1990. " Margin Regulation and Stock Market Volatility," Journal of Finance, American Finance Association, vol. 45(1), pages 3-29, March.
    13. Schwert, G William, 1989. " Why Does Stock Market Volatility Change over Time?," Journal of Finance, American Finance Association, vol. 44(5), pages 1115-53, December.
    14. Neil Shephard, 2005. "Stochastic volatility," Economics Series Working Papers 2005-W17, University of Oxford, Department of Economics.
    15. Tim Bollerslev & Jeffrey M. Wooldridge, 1988. "Quasi-Maximum Likelihood Estimation of Dynamic Models with Time-Varying Covariances," Working papers 505, Massachusetts Institute of Technology (MIT), Department of Economics.
    16. Shleifer, Andrei, 1986. " Do Demand Curves for Stocks Slope Down?," Journal of Finance, American Finance Association, vol. 41(3), pages 579-90, July.
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    Cited by:
    1. Roman Kraussl & Andre Lucas & David R. Rijsbergen & Pieter Jelle van der Sluis & Evert B. Vrugt, 2013. "Washington Meets Wall Street: A Closer Examination of the Presidential Cylce Puzzle," CREA Discussion Paper Series 13-4, Center for Research in Economic Analysis, University of Luxembourg.
    2. R. Kraeussl & A. Lucas & D. Rijsbergen & P.J. van der Sluis & E. Vrugt, 2008. "Washington meets Wall Street: A Closer Examination of the Presidential Cycle Puzzle," Tinbergen Institute Discussion Papers 08-101/2, Tinbergen Institute.

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