Iterated Combination Forecast and Treasury Bond Predictability

Abstract

Using a large number of predictors and based on an extended iterated combination approach of Lin, Wu, and Zhou (2017), we document both statistical and economic significance of Treasury bond return predictability. Macroeconomic and aggregate liquidity variables contain predictive information for bond returns and combining them with term structure and Ludvigson-Ng macro factors significantly improve out-of-sample forecast gains. We also find that variance forecasts can be substantially improved with our approach, yielding significant gains in asset allocation decision. Our results show that information from a large number of predictors collectively contributes to the time-varying Treasury bond premia, and this is robust to different return measures, horizons and sample periods.