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)

    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.

    Original languageEnglish
    Pages (from-to)39-61
    Number of pages23
    JournalJournal of International Money and Finance
    Volume64
    DOIs
    Publication statusPublished - 2016 Jun 1

    Keywords

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

    ASJC Scopus subject areas

    • Finance
    • Economics and Econometrics

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