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

44 Pages Posted: 27 Jun 2007 Last revised: 2 Mar 2012

See all articles by John M. Maheu

John M. Maheu

McMaster University - Michael G. DeGroote School of Business; RCEA

Thomas H. McCurdy

University of Toronto - Rotman School of Management; Center for Interuniversity Research and Analysis on Organization (CIRANO)

Multiple version iconThere are 2 versions of this paper

Date Written: April 1, 2007

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.

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

JEL Classification: C1, C11, C5, C53, G1, G19

Suggested Citation

Maheu, John M. and McCurdy, Thomas H., How Useful are Historical Data for Forecasting the Long-Run Equity Return Distribution? (April 1, 2007). Available at SSRN: https://ssrn.com/abstract=996696 or http://dx.doi.org/10.2139/ssrn.996696

John M. Maheu

McMaster University - Michael G. DeGroote School of Business ( email )

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Hamilton, Ontario L8S 4M4
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HOME PAGE: http://profs.degroote.mcmaster.ca/ads/maheujm/

RCEA

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Thomas H. McCurdy (Contact Author)

University of Toronto - Rotman School of Management ( email )

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Canada
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416-971-3048 (Fax)

HOME PAGE: http://www-2.rotman.utoronto.ca/~tmccurdy

Center for Interuniversity Research and Analysis on Organization (CIRANO)

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