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Detection Of High And Low States In Stock Market Returns With Mcmc Method In A Markov Switching Model

Author

Listed:
  • Clément Rey

    (CERMICS - Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique - ENPC - École des Ponts ParisTech)

  • Serge Rey

    (CATT - Centre d'Analyse Théorique et de Traitement des données économiques - UPPA - Université de Pau et des Pays de l'Adour)

  • Jean-Renaud Viala

    (AMUNDI Asset Management)

Abstract

To detect abnormal states in stock market returns, this study considers seven indices, over a 21-year period, the Dow Jones, S&P500, Nasdaq, Nikkei225, FTSE100, DAX, and CAC40. Three states are possible, namely a state of high rate of return, a state of low rate of return, both with high volatility and an intermediate state with low volatility. To determine the state of the market at each date, we study the returns using Markov chain Monte Carlo method (Metropolis–Hastings algorithm). Then at a second time, using a Cramer's coefficient, we deduce association coefficients or “correlations” among the different states of the major stock exchange markets around the world. First, the associations were globally stronger during the subprime crisis than during the dot-com bubble period. Second, among European markets Cramer's V is higher regardless of the period. Third, the associations between the Nikkei and the other market indices are systematically lower, indicating the relative disconnection of the Japanese market.
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Clément Rey & Serge Rey & Jean-Renaud Viala, 2013. "Detection Of High And Low States In Stock Market Returns With Mcmc Method In A Markov Switching Model," Working Papers hal-02939031, HAL.
    • Handle: RePEc:hal:wpaper:hal-02939031
    Note: View the original document on HAL open archive server: https://univ-pau.hal.science/hal-02939031
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    3. Álvarez de Toledo, Pablo & Núñez, Fernando & Usabiaga, Carlos, 2020. "Matching in segmented labor markets: An analytical proposal based on high-dimensional contingency tables," Economic Modelling, Elsevier, vol. 93(C), pages 175-186.
    4. Hsu, Yuan-Lin & Lin, Shih-Kuei & Hung, Ming-Chin & Huang, Tzu-Hui, 2016. "Empirical analysis of stock indices under a regime-switching model with dependent jump size risks," Economic Modelling, Elsevier, vol. 54(C), pages 260-275.
    5. 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.

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