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Learning about Term Structure Predictability under Uncertainty

Author

Listed:
  • Shuo Cao

    (Shenzhen Stock Exchange)

Abstract
This paper proposes a no-arbitrage framework of term structure modeling with learning and model uncertainty. The representative agent considers parameter instability, as well as the uncertainty in learning speed and model restrictions. The empirical evidence shows that apart from observational variance, parameter instability is the dominant source of predictive variance when compared with uncertainty in learning speed or model restrictions. When accounting for ambiguity aversion, the out-of-sample predictability of excess returns implied by the learning model can be translated into significant and consistent economic gains over the Expectations Hypothesis benchmark.

Suggested Citation

  • Shuo Cao, 2018. "Learning about Term Structure Predictability under Uncertainty," GRU Working Paper Series GRU_2018_006, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
    • Handle: RePEc:cth:wpaper:gru_2018_006
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    File URL: https://www.cb.cityu.edu.hk/ef/doc/GRU/WPS/GRU%232018-006%20Shuo.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    Affine Term Structure Models; Learning; Parameter Uncertainty; Model Uncertainty; Ambiguity Aversion; Bayesian Methods;
    All these keywords.

    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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