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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 an 21-year period, the Dow Jones, S&P500, Nasdaq, Nikkei225, the 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. Third, the associations between the Nikkei and the other markets indices are systematically lower, indicating the relative disconnection of the Japanese market.

Suggested Citation

  • Clément Rey & Serge Rey & Jean-Renaud Viala, 2014. "Detection of High and Low States in Stock Market Returns with MCMC Method in a Markov Switching Model," Post-Print hal-01880340, HAL.
  • Handle: RePEc:hal:journl:hal-01880340
    DOI: 10.1016/j.econmod.2014.05.003
    Note: View the original document on HAL open archive server: https://univ-pau.hal.science/hal-01880340
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    1. Fumio Hayashi & Edward C. Prescott, 2004. "The 1990s in Japan: a lost decade," Chapters, in: Paolo Onofri (ed.), The Economics of an Ageing Population, chapter 2, Edward Elgar Publishing.
    2. Dueker, Michael J, 1997. "Markov Switching in GARCH Processes and Mean-Reverting Stock-Market Volatility," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 26-34, January.
    3. Simon van Norden & Huntley Schaller & ), 1995. "Regime Switching in Stock Market Returns," Econometrics 9502002, University Library of Munich, Germany.
    4. Hunt, J. & Hahn, M., 2010. "Estimation and calibration of a continuous-time semi-Markov switching model," LIDAM Discussion Papers ISBA 2010048, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    6. Cumperayot, Phornchanok & Keijzer, Tjeert & Kouwenberg, Roy, 2006. "Linkages between extreme stock market and currency returns," Journal of International Money and Finance, Elsevier, vol. 25(3), pages 528-550, April.
    7. King, Mervyn & Sentana, Enrique & Wadhwani, Sushil, 1994. "Volatility and Links between National Stock Markets," Econometrica, Econometric Society, vol. 62(4), pages 901-933, July.
    8. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    9. King, Mervyn A & Wadhwani, Sushil, 1990. "Transmission of Volatility between Stock Markets," The Review of Financial Studies, Society for Financial Studies, vol. 3(1), pages 5-33.
    10. Markus Hahn & Sylvia Frühwirth-Schnatter & Jörn Sass, 2010. "Markov Chain Monte Carlo Methods for Parameter Estimation in Multidimensional Continuous Time Markov Switching Models," Journal of Financial Econometrics, Oxford University Press, vol. 8(1), pages 88-121, Winter.
    11. Chib, Siddhartha & Ramamurthy, Srikanth, 2010. "Tailored randomized block MCMC methods with application to DSGE models," Journal of Econometrics, Elsevier, vol. 155(1), pages 19-38, March.
    12. Ziobrowski, Alan J. & Cheng, Ping & Boyd, James W. & Ziobrowski, Brigitte J., 2004. "Abnormal Returns from the Common Stock Investments of the U.S. Senate," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 39(4), pages 661-676, December.
    13. Brown, Stephen J. & Warner, Jerold B., 1985. "Using daily stock returns : The case of event studies," Journal of Financial Economics, Elsevier, vol. 14(1), pages 3-31, March.
    14. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    15. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    16. Brown, Stephen J. & Warner, Jerold B., 1980. "Measuring security price performance," Journal of Financial Economics, Elsevier, vol. 8(3), pages 205-258, September.
    17. Cai, Jun, 1994. "A Markov Model of Switching-Regime ARCH," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 309-316, July.
    18. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
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    2. 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.
    3. Jacques Jaussaud & Sophie Nivoix & Serge Rey, 2015. "The Great East Japan Earthquake and Stock Prices," Economics Bulletin, AccessEcon, vol. 35(2), pages 1237-1261.
    4. Usabiaga, Carlos & Núñez, Fernando & Arendt, Lukasz & Gałecka-Burdziak, Ewa & Pater, Robert, 2022. "Skill requirements and labour polarisation: An association analysis based on Polish online job offers," Economic Modelling, Elsevier, vol. 115(C).
    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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