Equity premium predictability over the business cycle

, and Tobias Stein
Authors registered in the RePEc Author Service: Emanuel Moench

No 16357, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: The equity premium follows a pronounced v-shape pattern around the beginning of recessions. It sharply drops into negative territory just before business cycle peaks and then strongly recovers as the recession unfolds. Recessions are preceded by an inverted yield curve. Thus probit models using the term spread as predictor time the beginning of recessions well. We show that such model-implied recession probabilities strongly improve equity premium prediction out-of-sample. We document a structural break in the mean of the term spread in 1982. When correcting for this break, the forecast performance further strengthens, outperforming other recently proposed benchmark predictors.

Keywords: Recession predictability; Return predictability; Business cycle; Probit model; Term spread (search for similar items in EconPapers)
JEL-codes: C53 E32 E37 G11 G17 (search for similar items in EconPapers)
Date: 2021-09
References: Add references at CitEc
Citations:

Downloads: (external link)
https://cepr.org/publications/DP16357 (application/pdf)
CEPR Discussion Papers are free to download for our researchers, subscribers and members. If you fall into one of these categories but have trouble downloading our papers, please contact us at subscribers@cepr.org

Related works:
Working Paper: Equity premium predictability over the business cycle (2021)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cpr:ceprdp:16357

Ordering information: This working paper can be ordered from
https://cepr.org/publications/DP16357

Access Statistics for this paper

More papers in CEPR Discussion Papers from C.E.P.R. Discussion Papers Centre for Economic Policy Research, 33 Great Sutton Street, London EC1V 0DX.
Bibliographic data for series maintained by ().