Can credit spreads help predict a yield curve?

Azamat Abdymomunov, Kyu Ho Kang, Ki Jeong Kim

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)39-61
Number of pages23
JournalJournal of International Money and Finance
Publication statusPublished - 2016 Jun 1


  • Bayesian MCMC estimation
  • Density prediction
  • Dynamic Nelson-Siegel
  • Predictive likelihood

ASJC Scopus subject areas

  • Finance
  • Economics and Econometrics


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