Decomposing uncertainty in macro-finance term structure models
Byrne, Joseph P. and Cao, Shuo (2024) Decomposing uncertainty in macro-finance term structure models. The Review of Asset Pricing Studies, 14 (3). pp. 428-449. raae004. ISSN 2045-9939 (https://doi.org/10.1093/rapstu/raae004)
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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.
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Item type: Article ID code: 87991 Dates: DateEvent1 September 2024Published4 February 2024Published Online25 January 2024AcceptedNotes: Copyright © 2024 Oxford University Press. This is a pre-copy-editing, author-produced version of an article accepted for publication in The Review of Asset Pricing Studies following peer review. The definitive publisher-authenticated version (Joseph P Byrne, Shuo Cao, Decomposing Uncertainty in Macro-Finance Term Structure Models, The Review of Asset Pricing Studies, 2024, raae004) is available online at: https://doi.org/10.1093/rapstu/raae004 Subjects: Social Sciences > Finance
Science > Mathematics > Probabilities. Mathematical statisticsDepartment: Strathclyde Business School > Economics Depositing user: Pure Administrator Date deposited: 30 Jan 2024 14:00 Last modified: 11 Nov 2024 14:12 URI: https://strathprints.strath.ac.uk/id/eprint/87991