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The equity risk premium: a review of models

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Abstract

The authors estimate the equity risk premium (ERP)?the expected return on stocks in excess of the risk-free rate?by combining information from twenty models for the period 1960-2013. They begin their analysis by categorizing the models into five classes: trailing historical mean, dividend discount, cross-sectional estimation, regression analysis using valuation ratios or macroeconomic variables, and surveys. They find that an optimal weighted average of all models places the one-year-ahead ERP in June 2012 at 12.2 percent, close to levels reached in the mid- and late 1970s, when the ERP was highest in the study sample. The authors note, however, that there is considerable uncertainty in ERP point estimates. The interquartile range across models is 11.6 percent on average, although it reached 6.8 percent in 2012, the lowest level in the study sample. By employing differences across models, the authors argue that the ERP in 2012 is elevated mainly because Treasury yields are low, not because the expected future cash flows from stocks are high.

Suggested Citation

  • Fernando M. Duarte & Carlo Rosa, 2015. "The equity risk premium: a review of models," Economic Policy Review, Federal Reserve Bank of New York, issue 2, pages 39-57.
  • Handle: RePEc:fip:fednep:00027
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    1. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    2. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    3. Adrian, Tobias & Crump, Richard K. & Moench, Emanuel, 2015. "Regression-based estimation of dynamic asset pricing models," Journal of Financial Economics, Elsevier, vol. 118(2), pages 211-244.
    4. Kuehn Lars-Alexander & Petrosky-Nadeau Nicolas & Zhang Lu, "undated". "An Equilibrium Asset Pricing Model with Labor Market Search," GSIA Working Papers 2010-E63, Carnegie Mellon University, Tepper School of Business.
    5. Stephen A. Ross, 2013. "The Arbitrage Theory of Capital Asset Pricing," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 1, pages 11-30, World Scientific Publishing Co. Pte. Ltd..
    6. Andrew Ang & Geert Bekaert, 2007. "Stock Return Predictability: Is it There?," The Review of Financial Studies, Society for Financial Studies, vol. 20(3), pages 651-707.
    7. Peter D. Easton & Gregory A. Sommers, 2007. "Effect of Analysts' Optimism on Estimates of the Expected Rate of Return Implied by Earnings Forecasts," Journal of Accounting Research, Wiley Blackwell, vol. 45(5), pages 983-1015, December.
    8. Mariano M. Croce & Martin Lettau & Sydney C. Ludvigson, 2015. "Investor Information, Long-Run Risk, and the Term Structure of Equity," The Review of Financial Studies, Society for Financial Studies, vol. 28(3), pages 706-742.
    9. Nicholas Bloom, 2009. "The Impact of Uncertainty Shocks," Econometrica, Econometric Society, vol. 77(3), pages 623-685, May.
    10. Polk, Christopher & Thompson, Samuel & Vuolteenaho, Tuomo, 2006. "Cross-sectional forecasts of the equity premium," Journal of Financial Economics, Elsevier, vol. 81(1), pages 101-141, July.
    11. Lettau, Martin & Wachter, Jessica A., 2011. "The term structures of equity and interest rates," Journal of Financial Economics, Elsevier, vol. 101(1), pages 90-113, July.
    12. Malcolm Baker & Jeffrey Wurgler, 2007. "Investor Sentiment in the Stock Market," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 129-152, Spring.
    13. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    14. Eugene F. Fama & Kenneth R. French, 2002. "The Equity Premium," Journal of Finance, American Finance Association, vol. 57(2), pages 637-659, April.
    15. Boons, Martijn & Duarte, Fernando & de Roon, Frans & Szymanowska, Marta, 2020. "Time-varying inflation risk and stock returns," Journal of Financial Economics, Elsevier, vol. 136(2), pages 444-470.
    16. Timmermann, Allan, 2006. "Forecast Combinations," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 4, pages 135-196, Elsevier.
    17. James H. Stock & Mark W.Watson, 2003. "Forecasting Output and Inflation: The Role of Asset Prices," Journal of Economic Literature, American Economic Association, vol. 41(3), pages 788-829, September.
    18. Capistrán, Carlos & Timmermann, Allan, 2009. "Forecast Combination With Entry and Exit of Experts," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 428-440.
    19. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
    20. Fama, Eugene F. & French, Kenneth R., 1988. "Dividend yields and expected stock returns," Journal of Financial Economics, Elsevier, vol. 22(1), pages 3-25, October.
    21. Long Chen & Zhi Da & Xinlei Zhao, 2013. "What Drives Stock Price Movements?," The Review of Financial Studies, Society for Financial Studies, vol. 26(4), pages 841-876.
    22. Merton, Robert C, 1973. "An Intertemporal Capital Asset Pricing Model," Econometrica, Econometric Society, vol. 41(5), pages 867-887, September.
    23. Werner, Thomas & Lemke, Wolfgang, 2009. "The term structure of equity premia in an affine arbitrage-free model of bond and stock market dynamics," Working Paper Series 1045, European Central Bank.
    24. Bryan Kelly & Seth Pruitt, 2013. "Market Expectations in the Cross-Section of Present Values," Journal of Finance, American Finance Association, vol. 68(5), pages 1721-1756, October.
    25. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    26. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    27. Robin Greenwood & Andrei Shleifer, 2014. "Expectations of Returns and Expected Returns," The Review of Financial Studies, Society for Financial Studies, vol. 27(3), pages 714-746.
    28. J. Benson Durham, 2013. "Arbitrage-free models of stocks and bonds," Staff Reports 656, Federal Reserve Bank of New York.
    29. Refet S. Gürkaynak & Brian Sack & Jonathan H. Wright, 2010. "The TIPS Yield Curve and Inflation Compensation," American Economic Journal: Macroeconomics, American Economic Association, vol. 2(1), pages 70-92, January.
    30. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
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    More about this item

    Keywords

    stock returns; Equity premium;

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G00 - Financial Economics - - General - - - General
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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