Information in the Term Structure: A Forecasting Perspective
研究发现传统期限结构因子遗漏的信息有助于预测超额债券收益，通过调整无套利仿射期限结构模型的样本内外目标函数，新因子能捕捉这些信息并显著提升预测表现。
The existing literature finds that information not captured by traditional term structure factors helps predict excess bond returns. When estimating no-arbitrage affine term structure models, aligning in-sample and out-of-sample objective functions results in term structure factors that capture information that remains hidden from existing approaches. Specifically, the estimates of the third term structure factor radically differ and are related to the fourth principal component, which helps forecast bond returns. The new objective function leads to substantial improvements in forecasting performance. It also results in higher model term premiums and lower expected future short rates. This paper was accepted by David Simchi-Levi, finance.