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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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Related research

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

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References

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Citations

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Cited by:
  1. Georges Dionne & Olfa Maalaoui Chun, 2013. "Default and liquidity regimes in the bond market during the 2002-2012 period," Canadian Journal of Economics, Canadian Economics Association, vol. 46(4), pages 1160-1195, November.
  2. Andrew Ang & Allan Timmermann, 2011. "Regime Changes and Financial Markets," NBER Working Papers 17182, National Bureau of Economic Research, Inc.
  3. Yong Song, 2012. "Modelling Regime Switching and Structural Breaks with an Infinite Hidden Markov Model," Working Paper Series 28_12, The Rimini Centre for Economic Analysis.
  4. Jochmann, Markus & Koop, Gary & Strachan, Rodney W., 2010. "Bayesian forecasting using stochastic search variable selection in a VAR subject to breaks," International Journal of Forecasting, Elsevier, vol. 26(2), pages 326-347, April.
  5. He, Zhongfang & Maheu, John M., 2010. "Real time detection of structural breaks in GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2628-2640, November.
  6. John M. Maheu & Yong Song, 2012. "A New Structural Break Model with Application to Canadian Inflation Forecasting," Working Paper Series 27_12, The Rimini Centre for Economic Analysis.
  7. Maheu, John M. & Song, Yong, 2014. "A new structural break model, with an application to Canadian inflation forecasting," International Journal of Forecasting, Elsevier, vol. 30(1), pages 144-160.
  8. Fernandez, Pablo & Aguirreamalloa, Javier & Liechtenstein, Heinrich, 2009. "The equity premium puzzle: High required equity premium, undervaluation and self fulfilling prophecy," IESE Research Papers D/821, IESE Business School.
  9. He, Zhongfang, 2009. "Forecasting output growth by the yield curve: the role of structural breaks," MPRA Paper 28208, University Library of Munich, Germany.

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