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Decomposing Uncertainty in Macro-Finance Term Structure Models

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  • Joseph P Byrne
  • Shuo Cao

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)

Suggested Citation

  • Joseph P Byrne & Shuo Cao, 2024. "Decomposing Uncertainty in Macro-Finance Term Structure Models," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 14(3), pages 428-449.
  • Handle: RePEc:oup:rasset:v:14:y:2024:i:3:p:428-449.
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    File URL: http://hdl.handle.net/10.1093/rapstu/raae004
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    More about this item

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • E4 - Macroeconomics and Monetary Economics - - Money and Interest Rates
    • G1 - Financial Economics - - General Financial Markets

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