Decomposing Uncertainty in Macro-Finance Term Structure Models

33 Pages Posted: 12 Apr 2024

See all articles by Joseph Byrne

Joseph Byrne

University of Strathclyde, Glasgow - Strathclyde Business School - Department of Economics

Shuo Cao

Shenzhen Stock Exchange; City University of Hong Kong (CityU)

Date Written: September 26, 2021

Abstract

This paper studies the extent to which macro-finance term structure models are susceptible to predictive uncertainty. We propose a general form of arbitrage-free models and quantify the relative importance of unpredictable priced risk variance, as well as macro-finance model uncertainty and learning uncertainty in predictability. Predictive performance and relative contributions of uncertainty sources are dynamically measured based on Bayesian methods, revealing dominating priced risk variance and other important uncertainty sources at different points in time. Macro-finance model uncertainty is high for near-term forward spread forecasts and contributes up to 87% of predictive uncertainty prior to recessions, implying strong dispersion in the information content of macro variables when forming near-term monetary policy expectations.

Keywords: Macro-Finance Term Structure Models, Unspanned Macro Risks, Model Uncertainty, Parameter Uncertainty, Learning, Bayesian Econometrics

JEL Classification: C1, C3, C5, D8, E4, G1

Suggested Citation

Byrne, Joseph and Cao, Shuo, Decomposing Uncertainty in Macro-Finance Term Structure Models (September 26, 2021). Available at SSRN: https://ssrn.com/abstract=4757887 or http://dx.doi.org/10.2139/ssrn.4757887

Joseph Byrne

University of Strathclyde, Glasgow - Strathclyde Business School - Department of Economics ( email )

100 Cathedral Street
Glasgow G4 0LN
United Kingdom
+44 (0)141 548 3869 (Phone)
+44 (0)141 548 4445 (Fax)

Shuo Cao (Contact Author)

Shenzhen Stock Exchange ( email )

2012 Shennan Blvd., Futian District
Shenzhen
China

City University of Hong Kong (CityU) ( email )

83 Tat Chee Avenue
Kowloon
Hong Kong

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