Arbeitspapier

Forecasting the equity risk premium with frequency-decomposed predictors

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.

ISBN
978-952-323-144-3
Sprache
Englisch

Erschienen in
Series: Bank of Finland Research Discussion Papers ; No. 1/2017

Klassifikation
Wirtschaft
Financial Econometrics
Portfolio Choice; Investment Decisions
Asset Pricing; Trading Volume; Bond Interest Rates
Financial Forecasting and Simulation

Ereignis
Geistige Schöpfung
(wer)
Faria, Gonçalo
Verona, Fabio
Ereignis
Veröffentlichung
(wer)
Bank of Finland
(wo)
Helsinki
(wann)
2017

Handle
Letzte Aktualisierung
10.03.2025, 11:43 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Faria, Gonçalo
  • Verona, Fabio
  • Bank of Finland

Entstanden

  • 2017

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