Decomposing Uncertainty in Macro-Finance Term Structure Models

B-Tier
Journal: Review of Asset Pricing Studies
Year: 2024
Volume: 14
Issue: 3
Pages: 428-449

Authors (2)

Score contribution per author:

1.005 = (α=2.01 / 2 authors) × 1.0x B-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

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. (JEL C1, C3, C5, D8, E4, G1)

Technical Details

RePEc Handle
repec:oup:rasset:v:14:y:2024:i:3:p:428-449.
Journal Field
Finance
Author Count
2
Added to Database
2026-01-25