Improving equity premium forecasts by incorporating structural break uncertainty

Jing Tian, Qing Zhou

Research output: Contribution to journalArticleResearchpeer-review

Abstract

This article compares five alternative methods for directly dealing with structural break uncertainty in forecasting the U.S. equity premium using 30 widely used bivariate and multivariate predictive regressions. We find that two recently developed methods – Robust Optimal Weights on Observations and Forecast Combination across Estimation Windows – outperform the conventional rolling window and postbreak estimation methods. This result indicates that very early historical information is beneficial for U.S. equity premium forecasting but should be discounted to incorporate structural break uncertainty.
LanguageEnglish
Pages619-656
Number of pages38
JournalAccounting and Finance
Volume58
Issue numberS1
Early online date18 Oct 2016
DOIs
Publication statusPublished - Nov 2018
Externally publishedYes

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Structural breaks
Equity premium
Uncertainty
Forecast combination
Predictive regressions

Keywords

  • Structural break uncertainty
  • Out-of-sample forecast
  • Equity premium

Cite this

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Improving equity premium forecasts by incorporating structural break uncertainty. / Tian, Jing; Zhou, Qing.

In: Accounting and Finance, Vol. 58, No. S1, 11.2018, p. 619-656.

Research output: Contribution to journalArticleResearchpeer-review

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