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

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

Author Info

John M Maheu
Thomas H McCurdy

Additional information is available for the following registered author(s):

John M. Maheu

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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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
Date of revision:
Handle: RePEc:tor:tecipa:tecipa-293

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Paper
- 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, revised Jul 2007. [Downloadable!]

Find related papers by JEL classification:
C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis

This paper has been announced in the following NEP Reports:

NEP-ALL-2007-06-30 (All new papers)
NEP-ECM-2007-06-30 (Econometrics)
NEP-ETS-2007-06-30 (Econometric Time Series)
NEP-FOR-2007-06-30 (Forecasting)
NEP-LAB-2007-06-30 (Labour Economics)
NEP-RMG-2007-06-30 (Risk Management)

References listed on IDEAS
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