Can credit spreads help predict a yield curve?

Azamat Abdymomunov, Kyu Ho Kang and Ki Jeong Kim

Journal of International Money and Finance, 2016, vol. 64, issue C, 39-61

Abstract: In this paper we investigate whether information in credit spreads helps improve the forecasts of government bond yields. To do this, we propose and estimate a joint dynamic Nelson–Siegel (DNS) model of the U.S. Treasury yield curve and the credit spread curve. The model accounts for the possibility of regime changes in yield curve dynamics and incorporates a zero lower bound constraint on yields. We show that our joint model produces more accurate out-of-sample density forecasts of bond yields than does the yield-only DNS model. In addition, we demonstrate that incorporating regime changes and a zero lower bound constraint is essential for forecast improvements.

Keywords: Density prediction; Dynamic Nelson–Siegel; Predictive likelihood; Bayesian MCMC estimation (search for similar items in EconPapers)
JEL-codes: C11 C53 E43 E47 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jimfin:v:64:y:2016:i:c:p:39-61

DOI: 10.1016/j.jimonfin.2016.02.003

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