Are bond returns predictable with real-time macro data?

• Main Authors: HUANG, Dashan, JIANG, Fuwei, LI, Kunpeng, TONG, Guoshi, ZHOU, Guofu
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Bond return predictability; Real-time macro data; Scaled sufficient forecasting; Machine learning; Econometrics; Finance and Financial Management; Portfolio and Security Analysis
• Online Access: https://ink.library.smu.edu.sg/lkcsb_research/7368; https://ink.library.smu.edu.sg/context/lkcsb_research/article/8367/viewcontent/BondReturns_RT_MacroData_sv.pdf
• Institution: Singapore Management University

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.