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How useful are historical data for forecasting the long-run equity return distribution?

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  • John M Maheu
  • Thomas H McCurdy

Abstract

We provide an approach to forecasting the long-run (unconditional) distribution of equity returns making optimal use of historical data in the presence of structural breaks. Our focus is on learning about breaks in real time and assessing their impact on out-of-sample density forecasts. Forecasts use a probability-weighted average of submodels, each of which is estimated over a different history of data. The paper illustrates the importance of uncertainty about structural breaks and the value of modeling higher-order moments of excess returns when forecasting the return distribution and its moments. The shape of the long-run distribution and the dynamics of the higher-order moments are quite different from those generated by forecasts which cannot capture structural breaks. The empirical results strongly reject ignoring structural change in favor of our forecasts which weight historical data to accommodate uncertainty about structural breaks. We also strongly reject the common practice of using a fixed-length moving window. These differences in long-run forecasts have implications for many financial decisions, particularly for risk management and long-run investment decisions.

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Bibliographic Info

Paper provided by University of Toronto, Department of Economics in its series Working Papers with number tecipa-293.

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Length: 45 pages
Date of creation: 28 Jun 2007
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Handle: RePEc:tor:tecipa:tecipa-293

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Keywords: density forecasts; structural change; model risk; parameter uncertainty; Bayesian learning; market returns;

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References

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  1. Sydney Ludvigson & Martin Lettau, 1999. "Consumption, aggregate wealth and expected stock returns," Staff Reports, Federal Reserve Bank of New York 77, Federal Reserve Bank of New York.
  2. Geweke, John & Whiteman, Charles, 2006. "Bayesian Forecasting," Handbook of Economic Forecasting, Elsevier, Elsevier.
  3. M. Hashem Pesaran & Allan Timmermann, 2002. "Market timing and return prediction under model instability," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library 24932, London School of Economics and Political Science, LSE Library.
  4. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," Review of Financial Studies, Society for Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
  5. M. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2004. "Forecasting Time Series Subject to Multiple Structural Breaks," CESifo Working Paper Series 1237, CESifo Group Munich.
  6. Gary Koop & Simon M. Potter, 2007. "Estimation and Forecasting in Models with Multiple Breaks," Review of Economic Studies, Oxford University Press, vol. 74(3), pages 763-789.
  7. Graham, John R. & Harvey, Campbell R., 2005. "The long-run equity risk premium," Finance Research Letters, Elsevier, Elsevier, vol. 2(4), pages 185-194, December.
  8. Jonathan H. Wright, 2003. "Forecasting U.S. inflation by Bayesian Model Averaging," International Finance Discussion Papers, Board of Governors of the Federal Reserve System (U.S.) 780, Board of Governors of the Federal Reserve System (U.S.).
  9. Lubos Pastor & Robert F. Stambaugh, 2000. "The Equity Premium and Structural Breaks," NBER Working Papers 7778, National Bureau of Economic Research, Inc.
  10. Mehra, Rajnish & Prescott, Edward C., 2003. "The equity premium in retrospect," Handbook of the Economics of Finance, Elsevier, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, edition 1, volume 1, chapter 14, pages 889-938 Elsevier.
  11. Eugene Fama & F. & Kenneth R. French, . "The Equity Premium."," CRSP working papers, Center for Research in Security Prices, Graduate School of Business, University of Chicago 522, Center for Research in Security Prices, Graduate School of Business, University of Chicago.
  12. Todd E. Clark & Michael W. McCracken, 2006. "Averaging forecasts from VARs with uncertain instabilities," Research Working Paper, Federal Reserve Bank of Kansas City RWP 06-12, Federal Reserve Bank of Kansas City.
  13. Elena Andreou & Eric Ghysels, 2002. "Detecting multiple breaks in financial market volatility dynamics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 17(5), pages 579-600.
  14. Scott Mayfield, E., 2004. "Estimating the market risk premium," Journal of Financial Economics, Elsevier, Elsevier, vol. 73(3), pages 465-496, September.
  15. John M. Maheu & Stephen Gordon, 2008. "Learning, forecasting and structural breaks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 23(5), pages 553-583.
  16. John Y. Campbell & Samuel B. Thompson, 2005. "Predicting the Equity Premium Out of Sample: Can Anything Beat the Historical Average?," Harvard Institute of Economic Research Working Papers, Harvard - Institute of Economic Research 2084, Harvard - Institute of Economic Research.
  17. Christopher M. Turner & Richard Startz & Charles R. Nelson, 1989. "A Markov Model of Heteroskedasticity, Risk, and Learning in the Stock Market," NBER Working Papers 2818, National Bureau of Economic Research, Inc.
  18. Martin Lettau & Stijn Van Nieuwerburgh, 2008. "Reconciling the Return Predictability Evidence," Review of Financial Studies, Society for Financial Studies, Society for Financial Studies, vol. 21(4), pages 1607-1652, July.
  19. K. J. Martijn Cremers, 2002. "Stock Return Predictability: A Bayesian Model Selection Perspective," Review of Financial Studies, Society for Financial Studies, Society for Financial Studies, vol. 15(4), pages 1223-1249.
  20. Pettenuzzo, Davide & Timmermann, Allan, 2011. "Predictability of stock returns and asset allocation under structural breaks," Journal of Econometrics, Elsevier, Elsevier, vol. 164(1), pages 60-78, September.
  21. Martin Lettau & Sydney C. Ludvigson & Jessica A. Wachter, 2008. "The Declining Equity Premium: What Role Does Macroeconomic Risk Play?," Review of Financial Studies, Society for Financial Studies, Society for Financial Studies, vol. 21(4), pages 1653-1687, July.
  22. Chib, Siddhartha, 2001. "Markov chain Monte Carlo methods: computation and inference," Handbook of Econometrics, Elsevier, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 57, pages 3569-3649 Elsevier.
  23. Paye, Bradley S. & Timmermann, Allan, 2006. "Instability of return prediction models," Journal of Empirical Finance, Elsevier, Elsevier, vol. 13(3), pages 274-315, June.
  24. Siegel, Jeremy J., 1992. "The real rate of interest from 1800-1990 : A study of the U.S. and the U.K," Journal of Monetary Economics, Elsevier, Elsevier, vol. 29(2), pages 227-252, April.
  25. Turner, C.M. & Startz, R. & Nelson, C.R., 1989. "The Markov Model Of Heteroskedasticity, Risk And Learning In The Stock Market," Working Papers, University of Washington, Department of Economics 89-01, University of Washington, Department of Economics.
  26. Giordani, Paolo & Kohn, Robert, 2008. "Efficient Bayesian Inference for Multiple Change-Point and Mixture Innovation Models," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 26, pages 66-77, January.
  27. Schwert, G William, 1990. "Indexes of U.S. Stock Prices from 1802 to 1987," The Journal of Business, University of Chicago Press, University of Chicago Press, vol. 63(3), pages 399-426, July.
  28. Eklund, Jana & Karlsson, Sune, 2005. "Forecast Combination and Model Averaging Using Predictive Measures," CEPR Discussion Papers, C.E.P.R. Discussion Papers 5268, C.E.P.R. Discussion Papers.
  29. David E. Rapach & Mark E. Wohar, 2006. "Structural Breaks and Predictive Regression Models of Aggregate U.S. Stock Returns," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 4(2), pages 238-274.
  30. Nicholas Barberis, 2000. "Investing for the Long Run when Returns Are Predictable," Journal of Finance, American Finance Association, American Finance Association, vol. 55(1), pages 225-264, 02.
  31. Eric Jacquier & Alex Kane & Alan J. Marcus, 2005. "Optimal Estimation of the Risk Premium for the Long Run and Asset Allocation: A Case of Compounded Estimation Risk," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 3(1), pages 37-55.
  32. Pesaran, M. Hashem & Timmermann, Allan, 2007. "Selection of estimation window in the presence of breaks," Journal of Econometrics, Elsevier, Elsevier, vol. 137(1), pages 134-161, March.
  33. Kim, Chang-Jin & Morley, James C. & Nelson, Charles R., 2005. "The Structural Break in the Equity Premium," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 23, pages 181-191, April.
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Citations

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Cited by:
  1. Andrew Ang & Allan Timmermann, 2011. "Regime Changes and Financial Markets," NBER Working Papers 17182, National Bureau of Economic Research, Inc.
  2. Gary Koop & Markus Jochmann & Rodney W. Strachan, 2008. "Bayesian Forecasting using Stochastic Search Variable Selection in a VAR Subject to Breaks," Working Paper Series, The Rimini Centre for Economic Analysis 19-08, The Rimini Centre for Economic Analysis, revised Jan 2008.
  3. Georges Dionne & Olfa Maalaoui Chun, 2013. "Default and Liquidity Regimes in the Bond Market during the 2002-2012 Period," Cahiers de recherche, CIRPEE 1322, CIRPEE.
  4. Zhongfang He & John M. Maheu, 2009. "Real Time Detection of Structural Breaks in GARCH Models," Working Paper Series, The Rimini Centre for Economic Analysis 11_09, The Rimini Centre for Economic Analysis, revised Jan 2009.
  5. He, Zhongfang, 2009. "Forecasting output growth by the yield curve: the role of structural breaks," MPRA Paper 28208, University Library of Munich, Germany.
  6. Fernandez, Pablo & Aguirreamalloa, Javier & Liechtenstein, Heinrich, 2009. "The equity premium puzzle: High required equity premium, undervaluation and self fulfilling prophecy," IESE Research Papers, IESE Business School D/821, IESE Business School.
  7. John M Maheu & Yong Song, 2012. "A New Structural Break Model with Application to Canadian Inflation Forecasting," Working Papers, University of Toronto, Department of Economics tecipa-448, University of Toronto, Department of Economics.
  8. Maheu, John M. & Song, Yong, 2014. "A new structural break model, with an application to Canadian inflation forecasting," International Journal of Forecasting, Elsevier, Elsevier, vol. 30(1), pages 144-160.
  9. Yong Song, 2012. "Modelling Regime Switching and Structural Breaks with an Infinite Hidden Markov Model," Working Paper Series, The Rimini Centre for Economic Analysis 28_12, The Rimini Centre for Economic Analysis.

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