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Frequency-domain information for active portfolio management

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  • Faria, Gonçalo
  • Verona, Fabio

Abstract

We assess the benefits of using frequency-domain information for active portfolio management. To do so, we forecast the bond risk premium and equity risk premium using a methodology that isolates frequencies (of the predictors) with the highest predictive power. The resulting forecasts are more accurate than those of traditional forecasting methods for both asset classes. When used in the context of active portfolio man- agement, the forecasts based on frequency-domain information lead to better portfolio performances than when using the original time series of the predictors. It produces higher information ratio (0.57 vs 0.45), higher CER gains (1.12% vs 0.81%), and lower maximum drawdown (19.1% vs 19.6%).

Suggested Citation

  • Faria, Gonçalo & Verona, Fabio, 2020. "Frequency-domain information for active portfolio management," Bank of Finland Research Discussion Papers 2/2020, Bank of Finland.
    • Handle: RePEc:zbw:bofrdp:rdp2020_002
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    1. Bekiros, Stelios & Marcellino, Massimiliano, 2013. "The multiscale causal dynamics of foreign exchange markets," Journal of International Money and Finance, Elsevier, vol. 33(C), pages 282-305.
    2. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    3. Marianne Baxter & Robert G. King, 1999. "Measuring Business Cycles: Approximate Band-Pass Filters For Economic Time Series," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 575-593, November.
    4. Daniel L. Thornton & Giorgio Valente, 2012. "Out-of-Sample Predictions of Bond Excess Returns and Forward Rates: An Asset Allocation Perspective," The Review of Financial Studies, Society for Financial Studies, vol. 25(10), pages 3141-3168.
    5. Antonio Gargano & Davide Pettenuzzo & Allan Timmermann, 2019. "Bond Return Predictability: Economic Value and Links to the Macroeconomy," Management Science, INFORMS, vol. 65(2), pages 508-540, February.
    6. 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.
    7. Theo Berger, 2016. "Forecasting Based on Decomposed Financial Return Series: A Wavelet Analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(5), pages 419-433, August.
    8. repec:zbw:bofrdp:2017_001 is not listed on IDEAS
    9. Bandi, F.M. & Perron, B. & Tamoni, A. & Tebaldi, C., 2019. "The scale of predictability," Journal of Econometrics, Elsevier, vol. 208(1), pages 120-140.
    10. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    11. Sydney C. Ludvigson & Serena Ng, 2009. "Macro Factors in Bond Risk Premia," The Review of Financial Studies, Society for Financial Studies, vol. 22(12), pages 5027-5067, December.
    12. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    13. Fama, Eugene F. & French, Kenneth R., 1989. "Business conditions and expected returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 25(1), pages 23-49, November.
    14. Sarno, Lucio & Schneider, Paul & Wagner, Christian, 2016. "The economic value of predicting bond risk premia," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 247-267.
    15. Jozef Barunik & Lukas Vacha, 2015. "Realized wavelet-based estimation of integrated variance and jumps in the presence of noise," Quantitative Finance, Taylor & Francis Journals, vol. 15(8), pages 1347-1364, August.
    16. Gallegati, Marco & Ramsey, James B., 2013. "Bond vs stock market's Q: Testing for stability across frequencies and over time," Journal of Empirical Finance, Elsevier, vol. 24(C), pages 138-150.
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    More about this item

    Keywords

    equity risk premium; bond risk premium; predictability; multiresolutionanalysis; active portfolio management;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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