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

Paolo Zagaglia

Journal of Finance and Investment Analysis, 2024, vol. 13, issue 3, 1

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.

Keywords: Multiresolution analysis; Term structure; Predictability. (search for similar items in EconPapers)
Date: 2024
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