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Real-time Bayesian learning and bond return predictability

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  • Wan, Runqing
  • Fulop, Andras
  • Li, Junye

Abstract

The paper examines statistical and economic evidence of out-of-sample bond return predictability for a real-time Bayesian investor who learns about parameters, hidden states, and predictive models over time. We find some statistical evidence using information contained in forward rates. However, such statistical predictability can hardly generate any economic value for investors. Furthermore, we find that strong statistical and economic evidence of bond return predictability from fully-revised macroeconomic data vanishes when real-time macroeconomic information is used. We also show that highly levered investments in bonds can improve short-run bond return predictability.

Suggested Citation

  • Wan, Runqing & Fulop, Andras & Li, Junye, 2022. "Real-time Bayesian learning and bond return predictability," Journal of Econometrics, Elsevier, vol. 230(1), pages 114-130.
    • Handle: RePEc:eee:econom:v:230:y:2022:i:1:p:114-130
    DOI: 10.1016/j.jeconom.2020.04.052
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    More about this item

    Keywords

    Bayesian learning; Bond return predictability; Non-overlapping bond returns; Parameter uncertainty; Model combinations; Real-time macroeconomic information;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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