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The Predictive Power of the Yield Spread Under the Veil of Time

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  • Paolo Zagaglia

Abstract

This paper studies the sources of cyclical information delivered by the term spread for output growth predictability in the U.S. I use a wavelet-based time-frequency decomposition to decompose the predictive power of the yield spread across time scales, both in-sample and out-of-sample, over various forecast horizons. Spreads between interest rates on 10-year and 3-month Treasuries have a predictive ability for output growth that changes largely over different time scales. I find evidence of a negative correlation between the spread and future GDP growth for fluctuations with a frequency of 4 to 8 years per cycle. A linear combination among filtered yield spreads shows a sizable improvement in forecasting out-of-sample. The time-frequency decomposition is also used to propose an interpretation for the breakdown of in-sample predictability documented by Dotsey (1998) that arises after 1985. Â JEL classification numbers: C19, E43, E27.

Suggested Citation

  • Paolo Zagaglia, 2024. "The Predictive Power of the Yield Spread Under the Veil of Time," Journal of Finance and Investment Analysis, SCIENPRESS Ltd, vol. 13(3), pages 1-1.
    • Handle: RePEc:spt:fininv:v:13:y:2024:i:3:f:13_3_1
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    References listed on IDEAS

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    JEL classification:

    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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