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How Useful are Historical Data for Forecasting the Long-Run Equity Return Distribution?

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  • Maheu, John M.
  • McCurdy, Thomas H.

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 empirical results strongly reject ignoring structural change or using a fixed-length moving window. The shape of the long-run distribution is affected by breaks, which has implications for risk management and long-run investment decisions.

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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 27 (2009)
Issue (Month): ()
Pages: 95-112

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Handle: RePEc:bes:jnlbes:v:27:y:2009:p:95-112

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Citations

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Cited by:
  1. Ang, Andrew & Timmermann, Allan G, 2011. "Regime Changes and Financial Markets," CEPR Discussion Papers 8480, C.E.P.R. Discussion Papers.
  2. 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.
  3. 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.
  4. 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.
  5. John M Maheu & Yong Song, 2012. "A New Structural Break Model with Application to Canadian Inflation Forecasting," Working Papers tecipa-448, University of Toronto, Department of Economics.
  6. 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.
  7. 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.
  8. 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.
  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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