Publication Type
Journal Article
Version
submittedVersion
Publication Date
12-2023
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, Scaled sufficient forecasting, Machine learning
Discipline
Econometrics | Finance and Financial Management | Portfolio and Security Analysis
Research Areas
Finance
Publication
Journal of Econometrics
Volume
237
Issue
2
First Page
1
Last Page
20
ISSN
0304-4076
Identifier
10.1016/j.jeconom.2022.09.008
Publisher
Elsevier: 24 months
Citation
HUANG, Dashan; JIANG, Fuwei; LI, Kunpeng; TONG, Guoshi; and ZHOU, Guofu.
Are bond returns predictable with real-time macro data?. (2023). Journal of Econometrics. 237, (2), 1-20.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/7368
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.jeconom.2022.09.008
Included in
Econometrics Commons, Finance and Financial Management Commons, Portfolio and Security Analysis Commons