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The Journal of Fixed Income

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Predicting Future Yields and Risk Premia: The Blue-Dot Affine Model

Riccardo Rebonato and Riccardo Ronzani
The Journal of Fixed Income Fall 2020, jfi.2020.1.099; DOI: https://doi.org/10.3905/jfi.2020.1.099
Riccardo Rebonato
is a finance professor at EDHEC Business School in London, UK
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Riccardo Ronzani
is a student at Universita’ Bocconi in Milan, Italy.
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Abstract

The authors present a new affine model that can predict future yields and risk premia in the monetary conditions of the past decade more convincingly than current state-of-the-art statistical models. Despite making use of very different sources of information, it produces remarkably similar changes in risk premia as the most popular statistical return-predicting factors. However, it predicts very different—and, they argue, more believable—levels for risk premia and expectations. The model is extremely parsimonious, is financially motivated, fits market yields accurately with very few interpretable parameters, and naturally recovers important qualitative features of the joint ℙ and ℚ dynamics of yields.

TOPICS: Analysis of individual factors/risk premia, factor-based models, statistical methods

Key Findings

  • • A new affine model of the term structure is shown to give more plausible estimates of risk premia and expectations than the current state-of-the-art yield curve statistical models.

  • • The model uses information from the Fed expectations of future Fed Funds rates.

  • • The model is financially justifiable, very parsimonious, and fits observed market yields very well.

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The Journal of Fixed Income: 30 (3)
The Journal of Fixed Income
Vol. 30, Issue 3
Winter 2021
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Predicting Future Yields and Risk Premia: The Blue-Dot Affine Model
Riccardo Rebonato, Riccardo Ronzani
The Journal of Fixed Income Jun 2020, jfi.2020.1.099; DOI: 10.3905/jfi.2020.1.099

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Predicting Future Yields and Risk Premia: The Blue-Dot Affine Model
Riccardo Rebonato, Riccardo Ronzani
The Journal of Fixed Income Jun 2020, jfi.2020.1.099; DOI: 10.3905/jfi.2020.1.099
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  • Article
    • Abstract
    • WHY THE “BEST” RETURN-PREDICTING FACTORS CANNOT BE TRUSTED
    • THE DATA
    • THE MODEL
    • CALIBRATION OF THE MODEL
    • RESULTS AND DISCUSSION
    • CONCLUSIONS
    • ADDITIONAL READING
    • APPENDIX A
    • ENDNOTE
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