Forecasting the term structure of government bond yields in unstable environments

Byrne, J. P., Cao, S. and Korobilis, D. (2017) Forecasting the term structure of government bond yields in unstable environments. Journal of Empirical Finance, 44, pp. 209-225. (doi: 10.1016/j.jempfin.2017.09.004)

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In this paper we model and predict the term structure of US interest rates in a data-rich and unstable environment. The dynamic Nelson-Siegel factor model is extended to allow the model dimension and the parameters to change over time, in order to account for both model uncertainty and sudden structural changes, in one setting. The proposed specification performs better than several alternatives, since it incorporates additional macrofinance information during hard times, while it allows for more parsimonious models to be relevant during normal periods. A dynamic variance decomposition measure constructed from our model shows that parameter uncertainty and model uncertainty regarding different choices of predictors explain a large proportion of the predictive variance of bond yields.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Korompilis Magkas, Professor Dimitris and Byrne, Dr Joseph
Authors: Byrne, J. P., Cao, S., and Korobilis, D.
Subjects:H Social Sciences > HA Statistics
H Social Sciences > HG Finance
College/School:College of Social Sciences > Adam Smith Business School > Economics
Journal Name:Journal of Empirical Finance
ISSN (Online):1879-1727
Published Online:10 October 2017
Copyright Holders:Copyright © 2017 Elsevier B.V.
First Published:First published in Journal of Empirical Finance 44: 209-225
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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