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Identifying Speculative Bubbles Using an Infinite Hidden Markov Model

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  • Shuping Shi
  • Yong Song

Abstract

This article proposes an infinite hidden Markov model (iHMM) to detect, date stamp, and estimate speculative bubbles. Three features make this new approach attractive to practitioners. First, the iHMM is capable of capturing the complex nonlinear dynamics of bubble behaviors because it allows for an infinite number of regimes. Second, implementing this procedure is straightforward because bubbles are detected, dated, and estimated simultaneously in a coherent Bayesian framework. Third, because the iHMM assumes hierarchical structures, it is parsimonious and superior in out-of-sample forecasts. This model and extensions of this model are applied to the NASDAQ stock market. The in-sample posterior analysis and out-of-sample predictions find evidence of explosive dynamics during the dot-com bubble period. A model comparison shows that the iHMM is strongly supported by the data compared with finite hidden Markov models.

Suggested Citation

  • Shuping Shi & Yong Song, 2016. "Identifying Speculative Bubbles Using an Infinite Hidden Markov Model," Journal of Financial Econometrics, Oxford University Press, vol. 14(1), pages 159-184.
    • Handle: RePEc:oup:jfinec:v:14:y:2016:i:1:p:159-184.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbu025
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    Citations

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    Cited by:

    1. Pang, Tianxiao & Du, Lingjie & Chong, Terence Tai-Leung, 2021. "Estimating multiple breaks in nonstationary autoregressive models," Journal of Econometrics, Elsevier, vol. 221(1), pages 277-311.
    2. Laurent, Sébastien & Shi, Shuping, 2020. "Volatility estimation and jump detection for drift–diffusion processes," Journal of Econometrics, Elsevier, vol. 217(2), pages 259-290.
    3. Shi, Shuping, 2017. "Speculative bubbles or market fundamentals? An investigation of US regional housing markets," Economic Modelling, Elsevier, vol. 66(C), pages 101-111.
    4. Yang, Qiao, 2019. "Stock returns and real growth: A Bayesian nonparametric approach," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 53-69.
    5. Yong Song & Tomasz Wo'zniak, 2020. "Markov Switching," Papers 2002.03598, arXiv.org.
    6. Balcombe, Kelvin & Fraser, Iain, 2017. "Do bubbles have an explosive signature in markov switching models?," Economic Modelling, Elsevier, vol. 66(C), pages 81-100.
    7. Janusz Sobieraj & Dominik Metelski, 2021. "Testing Housing Markets for Episodes of Exuberance: Evidence from Different Polish Cities," JRFM, MDPI, vol. 14(9), pages 1-29, September.
    8. 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.
    9. 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.
    10. Qin, Meng & Su, Chi-Wei & Hao, Lin-Na & Tao, Ran, 2020. "The stability of U.S. economic policy: Does it really matter for oil price?," Energy, Elsevier, vol. 198(C).
    11. Moreira, Afonso M. & Martins, Luis F., 2020. "A new mechanism for anticipating price exuberance," International Review of Economics & Finance, Elsevier, vol. 65(C), pages 199-221.
    12. GHERBOVEȚ, Sergiu, 2017. "The Poorest In The World Pays For Crisis," Journal of Financial and Monetary Economics, Centre of Financial and Monetary Research "Victor Slavescu", vol. 4(1), pages 141-148.

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