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

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  • John M. Maheu

    () (University of Toronto, Canada and The Rimini Centre for Economics Analysis, Rimini, Italy)

  • Thomas H. McCurdy

    () (University of Toronto, Canada)

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.

Suggested Citation

  • John M. Maheu & Thomas H. McCurdy, 2007. "How useful are historical data for forecasting the long-run equity return distribution?," Working Paper series 19_07, Rimini Centre for Economic Analysis.
  • Handle: RePEc:rim:rimwps:19_07
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. 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.
    2. Andrew Ang & Allan Timmermann, 2012. "Regime Changes and Financial Markets," Annual Review of Financial Economics, Annual Reviews, vol. 4(1), pages 313-337, October.
    3. 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.
    4. 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.
    5. John M. Maheu & Yong Song, 2012. "A New Structural Break Model with Application to Canadian Inflation Forecasting," Working Paper series 27_12, Rimini Centre for Economic Analysis.
    6. 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.
    7. He, Zhongfang, 2009. "Forecasting output growth by the yield curve: the role of structural breaks," MPRA Paper 28208, University Library of Munich, Germany.
    8. Jiawen Xu & Pierre Perron, 2015. "Forecasting in the presence of in and out of sample breaks," Boston University - Department of Economics - Working Papers Series wp2015-012, Boston University - Department of Economics.
    9. repec:gam:jecnmx:v:5:y:2017:i:4:p:54-:d:122391 is not listed on IDEAS
    10. 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.
    11. Daniele Bianchi & Massimo Guidolin & Francesco Ravazzolo, 2017. "Macroeconomic Factors Strike Back: A Bayesian Change-Point Model of Time-Varying Risk Exposures and Premia in the U.S. Cross-Section," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 110-129, January.
    12. Yong Song, 2014. "Modelling Regime Switching And Structural Breaks With An Infinite Hidden Markov Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 825-842, August.
    13. Huang, Yan & Kou, Gang & Peng, Yi, 2017. "Nonlinear manifold learning for early warnings in financial markets," European Journal of Operational Research, Elsevier, vol. 258(2), pages 692-702.
    14. Ida Wolden Bache & James Mitchell & Francesco Ravazzolo & Shaun P. Vahey, 2009. "Macro modelling with many models," Working Paper 2009/15, Norges Bank.
    15. repec:eee:ecosta:v:4:y:2017:i:c:p:70-90 is not listed on IDEAS

    More about this item

    Keywords

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

    JEL classification:

    • F22 - International Economics - - International Factor Movements and International Business - - - International Migration
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J61 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Geographic Labor Mobility; Immigrant Workers

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