Forecasting bond yields with segmented term structure models

Abstract

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.