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Can credit spreads help predict a yield curve?
- Authors
- Abdymomunov, Azamat; Kang, Kyu Ho; Kim, Ki Jeong
- Issue Date
- 6월-2016
- Publisher
- ELSEVIER SCI LTD
- Keywords
- Density prediction; Dynamic Nelson-Siegel; Predictive likelihood; Bayesian MCMC estimation
- Citation
- JOURNAL OF INTERNATIONAL MONEY AND FINANCE, v.64, pp.39 - 61
- Indexed
- SSCI
SCOPUS
- Journal Title
- JOURNAL OF INTERNATIONAL MONEY AND FINANCE
- Volume
- 64
- Start Page
- 39
- End Page
- 61
- ISSN
- 0261-5606
- 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. (C) 2016 Elsevier Ltd. All rights reserved.
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