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Nonparametric Dynamic Conditional Beta

Author

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  • John M Maheu
  • Azam Shamsi Zamenjani

Abstract

This article derives a dynamic beta representation using a Bayesian semiparametric multivariate generalized autoregressive conditional heteroskedasticity (GARCH) model. The conditional joint distribution of excess stock returns and market excess returns is modeled as a countably infinite mixture of normals. This allows for deviations from the elliptic family of distributions. Empirically, we find the time-varying beta of a stock nonlinearly depends on the expected value of excess market returns. The nonlinear dependence is robust to different GARCH specifications as well as more factors in the model. In highly volatile markets, beta is almost constant, while in stable markets, the beta coefficient can depend asymmetrically on the expected market excess return. We extend the model to several factors and find empirical support for a three-factor model with nonlinear factor sensitives.

Suggested Citation

  • John M Maheu & Azam Shamsi Zamenjani, 2021. "Nonparametric Dynamic Conditional Beta," Journal of Financial Econometrics, Oxford University Press, vol. 19(4), pages 583-613.
    • Handle: RePEc:oup:jfinec:v:19:y:2021:i:4:p:583-613.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbz024
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    Cited by:

    1. Li, Chenxing & Yang, Qiao, 2025. "An infinite hidden Markov model with GARCH for short-term interest rates," Finance Research Letters, Elsevier, vol. 80(C).
    2. Darolles, Serge & Francq, Christian & Laurent, Sébastien, 2018. "Asymptotics of Cholesky GARCH models and time-varying conditional betas," Journal of Econometrics, Elsevier, vol. 204(2), pages 223-247.
    3. Li, Chenxing, 2022. "A multivariate GARCH model with an infinite hidden Markov mixture," MPRA Paper 112792, University Library of Munich, Germany.

    More about this item

    Keywords

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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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