Forecasting Bond Yields with Segmented Term Structure Models*
受偏好栖息理论启发，提出将期限结构分段建模的利率模型，用美国国债数据验证发现分段能提升长期预测，且引入协整可增强短期收益率预测。
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.