Improving equity premium forecasts by incorporating structural break uncertainty

Jing Tian; Qing Zhou

doi:10.1111/acfi.12240

Improving equity premium forecasts by incorporating structural break uncertainty

Research output: Contribution to journal › Article › Research › peer-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.

Language	English
Pages	619-656
Number of pages	38
Journal	Accounting and Finance
Volume	58
Issue number	S1
Early online date	18 Oct 2016
DOIs	10.1111/acfi.12240
Publication status	Published - Nov 2018
Externally published	Yes

Fingerprint

Structural breaks

Equity premium

Uncertainty

Forecast combination

Predictive regressions

Keywords

Structural break uncertainty
Out-of-sample forecast
Equity premium

Cite this

Tian, J., & Zhou, Q. (2018). Improving equity premium forecasts by incorporating structural break uncertainty. Accounting and Finance, 58(S1), 619-656. https://doi.org/10.1111/acfi.12240

Tian, Jing ; Zhou, Qing. / Improving equity premium forecasts by incorporating structural break uncertainty. In: Accounting and Finance. 2018 ; Vol. 58, No. S1. pp. 619-656.

@article{bd625f5e6d994d1695fbd7355785bee2,

title = "Improving equity premium forecasts by incorporating structural break uncertainty",

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.",

keywords = "Structural break uncertainty, Out-of-sample forecast, Equity premium",

author = "Jing Tian and Qing Zhou",

year = "2018",

month = "11",

doi = "10.1111/acfi.12240",

language = "English",

volume = "58",

pages = "619--656",

journal = "Accounting and Finance",

issn = "0810-5391",

publisher = "John Wiley & Sons",

number = "S1",

}

Tian, J & Zhou, Q 2018, 'Improving equity premium forecasts by incorporating structural break uncertainty' Accounting and Finance, vol. 58, no. S1, pp. 619-656. https://doi.org/10.1111/acfi.12240

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 journal › Article › Research › peer-review

TY - JOUR

T1 - Improving equity premium forecasts by incorporating structural break uncertainty

AU - Tian,Jing

AU - Zhou,Qing

PY - 2018/11

Y1 - 2018/11

N2 - 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.

AB - 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.

KW - Structural break uncertainty

KW - Out-of-sample forecast

KW - Equity premium

UR - http://www.scopus.com/inward/record.url?scp=84991585450&partnerID=8YFLogxK

U2 - 10.1111/acfi.12240

DO - 10.1111/acfi.12240

M3 - Article

VL - 58

SP - 619

EP - 656

JO - Accounting and Finance

T2 - Accounting and Finance