Caio Almeida, Kym Ardison, Daniela Kubudi, Axel Simonsen, José Vicente; Forecasting Bond Yields with Segmented Term Structure Models. Journal of Financial Econometrics 2017 nbx002. doi: 10.1093/jjfinec/nbx002
We would like to thank two anonymous referees, the editor (Federico Bandi), Francis Diebold, Raffaella Giacomini, Allan Timmermann, Michel van der Wel (discussant) and participants at the 2010 Workshop on Yield Curve Modeling and Forecasting at Erasmus University, the 2011 Brazilian Econometric Society Meeting, the 2012 SoFiE Annual Conference at Oxford, and the 2012 Brazilian Meeting of Finance for useful comments and suggestions. Caio Almeida and José Vicente gratefully acknowledge financial support from CNPq. Kym Ardison thanks FAPERJ and ANBIMA for financial support. The views expressed in this paper are those of the authors and do not necessarily reflect those of Banco Central do Brasil.
Inspired by the preferred habitat theory, we propose parametric interest rate models that split the term structure into segments. The proposed models are compared with successful term structure benchmarks based on out-of-sample forecasting exercises using U.S. Treasury data. We show that segmentation can improve long-horizon term structure forecasts when compared with nonsegmentation. Additionally, introducing cointegration in latent factor dynamics of segmented models makes them particularly strong to forecast short-maturity yields. Better forecasting is justified by the segmented models’ ability to accommodate idiosyncratic shocks in the cross-section of yields.