Forecasting the term structures of Treasury and corporate yields using dynamic Nelson-Siegel models

Wei-Choun Yu () and Eric Zivot

International Journal of Forecasting, 2011, vol. 27, issue 2, 579-591

Abstract: We extend Diebold and Li's dynamic Nelson-Siegel three-factor model to a broader empirical prospective by including the evaluation of the state space approach and by using nine different ratings for corporate bonds. We find that the dynamic Nelson-Siegel factor AR(1) model outperforms other competitors on the out-of-sample forecast accuracy, especially on the investment-grade bonds for the short-term forecast horizon and on the high-yield bonds for the long-term forecast horizon. The dynamic Nelson-Siegel factor state space model, however, becomes appealing on the high-yield bonds in the short-term forecast horizon, where the factor dynamics are more likely time-varying and parameter instability is more probable in the model specification.

Keywords: Term; structures; Treasury; yields; Corporate; yields; Nelson-Siegel; model; Factor; model; AR(1); VAR(1); Out-of-sample; forecasting; evaluations (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (21)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0169-2070(10)00095-6
Full text for ScienceDirect subscribers only

Related works:
Journal Article: Forecasting the term structures of Treasury and corporate yields using dynamic Nelson-Siegel models (2011)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:27:y::i:2:p:579-591

Access Statistics for this article

International Journal of Forecasting is currently edited by R. J. Hyndman

More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Catherine Liu ().