Forecasting the term structure of government bond yields in unstable environments

Abstract

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.

Dynamic Regression Models for Time-Ordered Functional Data

“…There is accumulating evidence to support the inclusion of macroeconomic variables in a yield curve forecasting model (Coroneo et al, 2016;Altavilla et al, 2017). Among the most successful forecasting approaches, a noteworthy feature is the use of time-varying parameters for modeling the associations between macroeconomic variables and the yield curve (Bianchi et al, 2009;Mumtaz and Surico, 2009;Byrne et al, 2017). However, all of the models referenced above-with or without time-varying parameters-rely on the Nelson-Siegel basis expansion (Nelson and Siegel, 1987).…”

An Overview of Dynamic Model Averaging Techniques in Time‐series Econometrics

“…For instance, going back to the generalized Phillips curve mentioned above, there are a myriad of theories that suggest a link between variables, such as the unemployment rate, T-bill rates, level of economic activity, house prices, and the rate of inflation. Therefore, the practitioner is faced with the situation, where he (she) the purpose of our study is not to list every single publication (or working paper) that applies DMA in one way or another, we can list the following interesting applications: Dangl and Halling (2012), Liu et al (2015), and Naser and Alaali (2018) with regard to predicting aggregate equity returns; Koop and Tole (2013) in the context of forecasting the spot price of carbon permits; Buncic and Moretto (2015), Drachal (2016), and Naser (2016) with regard to predicting commodity prices; Bruyn et al (2015), Beckmann and Schüssler (2016), Byrne et al (2018), and Beckmann et al (2020) in the context of forecasting exchange rates; Gupta et al (2014) with regard to forecasting foreign exchange reserves; Bork and Møller (2015), Risse and Kern (2016), and Wei and Cao (2017) in the context of forecasting house price changes; Aye et al (2015) and Baur et al (2016) with regard to predicting the rate of return on the price of gold; Koop and Korobilis (2011) and Filippo (2015) with regard to forecasting non-U.S. rate of inflation; Byrne et al (2017) with respect to forecasting the term structure of government bond yields; and Wang et al (2016), Liu et al (2017), Nonejad (2017b), and Ma et al (2018) with respect to forecasting equity return and commodity price volatility.…”

“…rate of inflation; Byrne et al . (2017) with respect to forecasting the term structure of government bond yields; and Wang et al . (2016), Liu et al .…”

Forecasting the Term Structure of Interest Rates of the BRICS: Evidence from a Nonparametric Functional Data Analysis

“…At the same time, movements in the term-structure of interest rates serve as a valuable input for practioners in finance to carry out bond portfolio management, derivatives pricing, and risk management (Caldeira et al , 2016a). Hence, accurate forecasting of the termstructure of a yield curve is of paramount importance to both policy-makers and financial market agents in general, and have understandably, resulted in a large literature (see, Caldeira et al , 2016b;Byrne et al , 2017;Caldeira et al , 2018, for detailed reviews) .…”