Are Bond Returns Predictable with Real-Time Macro Data?

Abstract

We investigate the predictability of bond returns using real-time macro variables and consider the possibility of a nonlinear predictive relationship and the presence of weak factors. To address these issues, we propose a scaled sufficient forecasting (sSUFF) method and analyze its asymptotic properties. Using both the existing and the new method, we find empirically that real-time macro variables have significant forecasting power both in-sample and out-of-sample. Moreover, they generate sizable economic values, and their predictability is not spanned by the yield curve. We also observe that the forecasted bond returns are countercyclical, and the magnitude of predictability is stronger during economic recessions, which lends empirical support to well-known macro finance theories.

Keywords: Bond Return Predictability, Real Time Macro Data, Vintage, PCA, Big Data, Machine Learning

JEL Classification: C22, C53, G11, G12, G17