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Infinite-State Markov-Switching for Dynamic Volatility

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  • Arnaud Dufays

Abstract

Generalized auto-regressive conditional heteroskedastic model with changing parameters is specified using the sticky infinite hidden Markov-chain framework. Estimation by Bayesian inference determines the adequate number of regimes as well as the optimal specification (Markov-switching or change-point). The new provided algorithms are studied in terms of mixing properties and computational time. Applications highlight the flexibility of the model and compare it to existing parametric alternatives.

Suggested Citation

  • Arnaud Dufays, 2016. "Infinite-State Markov-Switching for Dynamic Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 14(2), pages 418-460.
    • Handle: RePEc:oup:jfinec:v:14:y:2016:i:2:p:418-460.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbv017
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    Citations

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    Cited by:

    1. Fisher, Mark & Jensen, Mark J., 2019. "Bayesian inference and prediction of a multiple-change-point panel model with nonparametric priors," Journal of Econometrics, Elsevier, vol. 210(1), pages 187-202.
    2. Maciej Augustyniak & Mathieu Boudreault & Manuel Morales, 2018. "Maximum Likelihood Estimation of the Markov-Switching GARCH Model Based on a General Collapsing Procedure," Methodology and Computing in Applied Probability, Springer, vol. 20(1), pages 165-188, March.
    3. Yang, Qiao, 2019. "Stock returns and real growth: A Bayesian nonparametric approach," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 53-69.
    4. Yong Song & Tomasz Wo'zniak, 2020. "Markov Switching," Papers 2002.03598, arXiv.org.
    5. Li, Chenxing, 2022. "A multivariate GARCH model with an infinite hidden Markov mixture," MPRA Paper 112792, University Library of Munich, Germany.
    6. Li, Chenxing & Maheu, John M & Yang, Qiao, 2022. "An Infinite Hidden Markov Model with Stochastic Volatility," MPRA Paper 115456, University Library of Munich, Germany.
    7. Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2022. "Bayesian Forecasting in Economics and Finance: A Modern Review," Papers 2212.03471, arXiv.org, revised Jul 2023.
    8. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    9. Jin, Xin & Maheu, John M. & Yang, Qiao, 2022. "Infinite Markov pooling of predictive distributions," Journal of Econometrics, Elsevier, vol. 228(2), pages 302-321.

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