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The dynamic analysis and prediction of stock markets through the latent Markov model

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  • De Angelis, L

    (Vrije Universiteit Amsterdam, Faculteit der Economische Wetenschappen en Econometrie (Free University Amsterdam, Faculty of Economics Sciences, Business Administration and Economitrics)

  • Paas, L.J.

Abstract

In this paper we show how the latent Markov model can be used to define different conditions in the stock market, called market- regimes. Changes in regimes can be used to detect financial crises, pinpoint the end of a crisis and predict future developments in the stock market, to some degree. The model is applied to changes in monthly price indexes of the Italian and US stock market in the period from January 2000 to July 2009.

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Bibliographic Info

Paper provided by VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics in its series Serie Research Memoranda with number 0053.

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Date of creation: 2009
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Handle: RePEc:dgr:vuarem:2009-53

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Web page: http://www.feweb.vu.nl

Related research

Keywords: Stock market pattern analysis; Regime-switching; Forecasting; Latent Markov model; Financial crises; Market stability periods;

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  1. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
  2. Markus Haas, 2004. "A New Approach to Markov-Switching GARCH Models," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 2(4), pages 493-530.
  3. Leonard J. Paas & Jeroen K. Vermunt & Tammo H. A. Bijmolt, 2007. "Discrete time, discrete state latent Markov modelling for assessing and predicting household acquisitions of financial products," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 955-974.
  4. Turner, Christopher M. & Startz, Richard & Nelson, Charles R., 1989. "A Markov model of heteroskedasticity, risk, and learning in the stock market," Journal of Financial Economics, Elsevier, vol. 25(1), pages 3-22, November.
  5. Christian Francq & Jean-Michel Zakoïan, 2000. "Stationarity of Multivariate Markov-Switching ARMA Models," Working Papers 2000-32, Centre de Recherche en Economie et Statistique.
  6. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer, vol. 52(3), pages 345-370, September.
  7. Goldfeld, Stephen M. & Quandt, Richard E., 1973. "A Markov model for switching regressions," Journal of Econometrics, Elsevier, vol. 1(1), pages 3-15, March.
  8. Demirguc-Kunt, Ash & Levine, Ross, 1996. "Stock Market Development and Financial Intermediaries: Stylized Facts," World Bank Economic Review, World Bank Group, vol. 10(2), pages 291-321, May.
  9. Francesco Bartolucci & Fulvia Pennoni & Brian Francis, 2007. "A latent Markov model for detecting patterns of criminal activity," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(1), pages 115-132.
  10. Lamoureux, Christopher G & Lastrapes, William D, 1993. "Forecasting Stock-Return Variance: Toward an Understanding of Stochastic Implied Volatilities," Review of Financial Studies, Society for Financial Studies, vol. 6(2), pages 293-326.
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