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Forecasting the equity risk premium with frequency-decomposed predictors

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  • Gonçalo Faria

    (Católica Porto Business School and CEGE, Universidade Católica Portuguesa)

  • Fabio Verona

    (Bank of Finland and CEF.UP)

Abstract

We show that the out-of-sample forecast of the equity risk premium can be significantly improved by taking into account the frequency-domain relationship between the equity risk premium and several potential predictors. We consider fifteen 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 different 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.

Suggested Citation

  • Gonçalo Faria & Fabio Verona, 2016. "Forecasting the equity risk premium with frequency-decomposed predictors," Working Papers de Economia (Economics Working Papers) 06, Católica Porto Business School, Universidade Católica Portuguesa.
  • Handle: RePEc:cap:wpaper:062016
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    2. Voutilainen, Ville, 2017. "Wavelet decomposition of the financial cycle : An early warning system for financial tsunamis," Research Discussion Papers 11/2017, Bank of Finland.
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    5. Silvo, Aino, 2017. "House prices, lending standards, and the macroeconomy," Research Discussion Papers 4/2017, Bank of Finland.
    6. Risse, Marian, 2019. "Combining wavelet decomposition with machine learning to forecast gold returns," International Journal of Forecasting, Elsevier, vol. 35(2), pages 601-615.

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    Keywords

    predictability; equity risk premium; frequency domain; discrete wavelets;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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