Forecasting the equity risk premium with frequency-decomposed predictors
Gonçalo Faria () and
Fabio Verona ()
No 1/2017, Research Discussion Papers from Bank of Finland
We show that the out-of-sample forecast of the equity risk premium can be signi ficantly improved by taking into account the frequency-domain relationship between the equity risk premium and several potential predictors. We consider fi fteen predictors from the existing literature, for the out-of-sample forecasting period from January 1990 to December 2014. The best result achieved for individual predictors is a monthly out-of-sample R2 of 2.98 % and utility gains of 549 basis points per year for a mean-variance investor. This performance is improved even further when the individual forecasts from the frequency-decomposed predictors are combined. These results are robust for di fferent subsamples, including the Great Moderation period, the Great Financial Crisis period and, more generically, periods of bad, normal and good economic growth. The strong and robust performance of this method comes from its ability to disentangle the information aggregated in the original time series of each variable, which allows to isolate the frequencies of the predictors with the highest predictive power from the noisy parts.
JEL-codes: C58 G11 G12 G17 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-for and nep-upt
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
Working Paper: Forecasting the equity risk premium with frequency-decomposed predictors (2016)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:bof:bofrdp:2017_001
Access Statistics for this paper
More papers in Research Discussion Papers from Bank of Finland Bank of Finland, P.O. Box 160, FI-00101 Helsinki, Finland. Contact information at EDIRC.
Bibliographic data for series maintained by Minna Nyman ().