Can credit spreads help predict a yield curve?

被引:2
|
作者
Abdymomunov, Azamat [1 ]
Kang, Kyu Ho [2 ]
Kim, Ki Jeong [3 ]
机构
[1] Fed Reserve Bank Richmond, 530 East Trade St, Charlotte, NC 28202 USA
[2] Korea Univ, Dept Econ, Seoul 136701, South Korea
[3] Bank Korea, 39 Namdaemun Ro, Seoul 04531, South Korea
关键词
Density prediction; Dynamic Nelson-Siegel; Predictive likelihood; Bayesian MCMC estimation; GOVERNMENT BOND YIELDS; TERM STRUCTURE MODELS; AFFINE MODELS; DETERMINANTS; FORECASTS; DENSITY; US;
D O I
10.1016/j.jimonfin.2016.02.003
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
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.
引用
收藏
页码:39 / 61
页数:23
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