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Learning and Risk Premiums in an Arbitrage-Free Term Structure Model

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Listed:
  • Giacoletti, Marco

    (U of Southern California)

  • Laursen, Kristoffer T.

    (AQR Capital Management, LLC)

  • Singleton, Kenneth J.

    (Stanford U)

Abstract

We study the evolution of risk premiums on US Treasury bonds from the perspective of a real-time Bayesian learner RA who updates her beliefs using a dynamic term structure model. Learning about the historical dynamics of yields led to substantial variation in RA's subjective risk premiums. Moreover, she gained substantial forecasting power by conditioning her learning on measures of disagreement among professional forecasters about future yields. This gain was distinct from the (much weaker) forecasting power of macroeconomic information. RA's views about the pricing distribution of yields remained nearly constant over time. Her learning rule outperformed consensus forecasts of market professionals, particularly following U.S. recessions.

Suggested Citation

  • Giacoletti, Marco & Laursen, Kristoffer T. & Singleton, Kenneth J., 2018. "Learning and Risk Premiums in an Arbitrage-Free Term Structure Model," Research Papers 3670, Stanford University, Graduate School of Business.
    • Handle: RePEc:ecl:stabus:3670
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    File URL: https://www.gsb.stanford.edu/gsb-cmis/gsb-cmis-download-auth/461306
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    Cited by:

    1. Granziera, Eleonora & Sihvonen, Markus, 2024. "Bonds, currencies and expectational errors," Journal of Economic Dynamics and Control, Elsevier, vol. 158(C).
    2. Ifrim, Adrian, 2023. "Sentimental Discount Rate Shocks," EconStor Preprints 268363, ZBW - Leibniz Information Centre for Economics.

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