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A dynamic analysis of stock markets using a hidden Markov model

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  • Luca De Angelis
  • Leonard J. Paas

Abstract

This paper proposes a framework to detect financial crises, pinpoint the end of a crisis in stock markets and support investment decision-making processes. This proposal is based on a hidden Markov model (HMM) and allows for a specific focus on conditional mean returns. By analysing weekly changes in the US stock market indexes over a period of 20 years, this study obtains an accurate detection of stable and turmoil periods and a probabilistic measure of switching between different stock market conditions. The results contribute to the discussion of the capabilities of Markov-switching models of analysing stock market behaviour. In particular, we find evidence that HMM outperforms threshold GARCH model with Student- t innovations both in-sample and out-of-sample, giving financial operators some appealing investment strategies.

Suggested Citation

  • Luca De Angelis & Leonard J. Paas, 2013. "A dynamic analysis of stock markets using a hidden Markov model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(8), pages 1682-1700, August.
    • Handle: RePEc:taf:japsta:v:40:y:2013:i:8:p:1682-1700
    DOI: 10.1080/02664763.2013.793302
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    Cited by:

    1. Beatrice Foroni & Luca Merlo & Lea Petrella, 2023. "Expectile hidden Markov regression models for analyzing cryptocurrency returns," Papers 2301.09722, arXiv.org, revised Jan 2024.
    2. De Angelis Luca & Viroli Cinzia, 2017. "A Markov-switching regression model with non-Gaussian innovations: estimation and testing," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(2), pages 1-22, April.
    3. Fulvia Pennoni & Francesco Bartolucci & Gianfranco Forte & Ferdinando Ametrano, 2022. "Exploring the dependencies among main cryptocurrency log‐returns: A hidden Markov model," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 51(1), February.
    4. Beatrice Foroni & Luca Merlo & Lea Petrella, 2023. "Quantile and expectile copula-based hidden Markov regression models for the analysis of the cryptocurrency market," Papers 2307.06400, arXiv.org.
    5. Beatrice Foroni & Luca Merlo & Lea Petrella, 2024. "Hidden Markov graphical models with state-dependent generalized hyperbolic distributions," Papers 2412.03668, arXiv.org.
    6. Danisman, Ozgur & Uzunoglu Kocer, Umay, 2021. "Hidden Markov models with binary dependence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
    7. Anton Gerunov, 2023. "Stock Returns Under Different Market Regimes: An Application of Markov Switching Models to 24 European Indices," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 1, pages 18-35.
    8. Leonard Paas, 2014. "Comments on: Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 473-477, September.
    9. Ichkitidze, Yuri, 2018. "Temporary price trends in the stock market with rational agents," The Quarterly Review of Economics and Finance, Elsevier, vol. 68(C), pages 103-117.
    10. Dias, José G. & Vermunt, Jeroen K. & Ramos, Sofia, 2015. "Clustering financial time series: New insights from an extended hidden Markov model," European Journal of Operational Research, Elsevier, vol. 243(3), pages 852-864.
    11. Valeriy Zakamulin, 2023. "Not all bull and bear markets are alike: insights from a five-state hidden semi-Markov model," Risk Management, Palgrave Macmillan, vol. 25(1), pages 1-25, March.
    12. Giner, Javier & Zakamulin, Valeriy, 2023. "A regime-switching model of stock returns with momentum and mean reversion," Economic Modelling, Elsevier, vol. 122(C).
    13. Tsukasa Hokimoto & Kunio Shimizu, 2014. "A non-homogeneous hidden Markov model for predicting the distribution of sea surface elevation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(2), pages 294-319, February.

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