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Components of Market Risk and Return

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  • John M. Maheu
  • Thomas H. McCurdy

Abstract

This article proposes a flexible but parsimonious specification of the joint dynamics of market risk and return to produce forecasts of a time-varying market equity premium. Our parsimonious volatility model allows components to decay at different rates, generates mean-reverting forecasts, and allows variance targeting. These features contribute to realistic equity premium forecasts for the U.S. market over the 1840-2006 period. For example, the premium forecast was low in the mid-1990s but has recently increased. Although the market's total conditional variance has a positive effect on returns, the smooth long-run component of volatility is more important for capturing the dynamics of the premium. This result is robust to univariate specifications that condition on either levels or logs of past realized volatility (RV), as well as to a new bivariate model of returns and RV. Copyright , Oxford University Press.

Suggested Citation

  • John M. Maheu & Thomas H. McCurdy, 2007. "Components of Market Risk and Return," Journal of Financial Econometrics, Oxford University Press, vol. 5(4), pages 560-590, Fall.
    • Handle: RePEc:oup:jfinec:v:5:y:2007:i:4:p:560-590
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbm012
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    Citations

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    Cited by:

    1. Mathieu Gatumel & Florian Ielpo, 2014. "The Number Of Regimes Across Asset Returns: Identification And Economic Value," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 17(06), pages 1-25.
    2. Mark J. Jensen & John M. Maheu, 2018. "Risk, Return and Volatility Feedback: A Bayesian Nonparametric Analysis," JRFM, MDPI, vol. 11(3), pages 1-29, September.
    3. Maheu, John M. & McCurdy, Thomas H. & Zhao, Xiaofei, 2013. "Do jumps contribute to the dynamics of the equity premium?," Journal of Financial Economics, Elsevier, vol. 110(2), pages 457-477.
    4. Maheu, John M. & McCurdy, Thomas H., 2011. "Do high-frequency measures of volatility improve forecasts of return distributions?," Journal of Econometrics, Elsevier, vol. 160(1), pages 69-76, January.
    5. Giampiero M. Gallo & Edoardo Otranto, 2016. "Combining Markov Switching and Smooth Transition in Modeling Volatility: A Fuzzy Regime MEM," Econometrics Working Papers Archive 2016_02, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    6. Shirota, Shinichiro & Hizu, Takayuki & Omori, Yasuhiro, 2014. "Realized stochastic volatility with leverage and long memory," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 618-641.
    7. Mathieu Gatumel & Florian Ielpo, 2011. "The Number of Regimes Across Asset Returns: Identification and Economic Value," Post-Print halshs-00658540, HAL.
    8. J. Ernstberger & H. Haupt & O. Vogler, 2011. "The role of sorting portfolios in asset-pricing models," Applied Financial Economics, Taylor & Francis Journals, vol. 21(18), pages 1381-1396.
    9. Ayakeme Ebiwarefa Whisky & Chinedu B. Ezirim, 2014. "Do Investors Make Abnormal Returns Consistently? An Econometric Investigation in the Nigerian Capital Market," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 5(2), pages 115-120, April.
    10. Bandi, Federico M. & Perron, Benoît, 2008. "Long-run risk-return trade-offs," Journal of Econometrics, Elsevier, vol. 143(2), pages 349-374, April.
    11. Okou, Cédric & Jacquier, Éric, 2016. "Horizon effect in the term structure of long-run risk-return trade-offs," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 445-466.
    12. Anderson, Evan W. & Ghysels, Eric & Juergens, Jennifer L., 2009. "The impact of risk and uncertainty on expected returns," Journal of Financial Economics, Elsevier, vol. 94(2), pages 233-263, November.
    13. Benoît Sévi & César Baena, 2013. "The explanatory power of signed jumps for the risk-return tradeoff," Economics Bulletin, AccessEcon, vol. 33(2), pages 1029-1046.
    14. Giampiero M. Gallo & Edoardo Otranto, 2014. "Forecasting Realized Volatility with Changes of Regimes," Econometrics Working Papers Archive 2014_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Feb 2014.
    15. Gallo, Giampiero M. & Otranto, Edoardo, 2015. "Forecasting realized volatility with changing average levels," International Journal of Forecasting, Elsevier, vol. 31(3), pages 620-634.
    16. Park, Beum-Jo, 2010. "Surprising information, the MDH, and the relationship between volatility and trading volume," Journal of Financial Markets, Elsevier, vol. 13(3), pages 344-366, August.

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