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Dynamic Bivariate Mixture Models: Modeling the Behavior of Prices and Trading Volume

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  • Liesenfeld, Roman
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    Abstract

    Bivariate mixture models have been used to explain the stochastic behavior of daily price changes and trading volume on financial markets. In this class of models, price changes and volume follow a mixture of bivariate distributions with the unobservable number of price-relevant information serving as the mixing variable. The time series behavior of this mixture variable determines the dynamics of the price-volume system. In this article, bivariate mixture specifications with a serially correlated mixing variable are estimated by simulated maximum likelihood and analyzed concerning their ability to account for the observed dynamics on financial markets, especially the persistence in the variance of price changes. The results, based on German stock-market data, reveal that the dynamic bivariate mixture models cannot account for the persistence in the price-change variance.

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    Bibliographic Info

    Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

    Volume (Year): 16 (1998)
    Issue (Month): 1 (January)
    Pages: 101-09

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    Handle: RePEc:bes:jnlbes:v:16:y:1998:i:1:p:101-09

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    Cited by:
    1. repec:lan:wpaper:3142 is not listed on IDEAS
    2. Nikolaus Hautsch, 2007. "Capturing Common Components in High-Frequency Financial Time Series: A Multivariate Stochastic Multiplicative Error Model," SFB 649 Discussion Papers SFB649DP2007-052, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    3. Liesenfeld, Roman, 2001. "A generalized bivariate mixture model for stock price volatility and trading volume," Journal of Econometrics, Elsevier, vol. 104(1), pages 141-178, August.
    4. Maciejowska, Katarzyna, 2013. "Assessing the number of components in a normal mixture: an alternative approach," MPRA Paper 50303, University Library of Munich, Germany.
    5. Katarzyna Maciejowska, 2010. "Estimation Methods Comparison of SVAR Models with a Mixture of Two Normal Distributions," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 2(4), pages 279-314, September.
    6. Roman Liesenfeld & Robert C. Jung, 2000. "Stochastic volatility models: conditional normality versus heavy-tailed distributions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(2), pages 137-160.
    7. repec:lan:wpaper:3326 is not listed on IDEAS
    8. Asai, Manabu & Brugal, Ivan, 2013. "Forecasting volatility via stock return, range, trading volume and spillover effects: The case of Brazil," The North American Journal of Economics and Finance, Elsevier, vol. 25(C), pages 202-213.
    9. Pyun, Chong Soo & Lee, Sa Young & Nam, Kiseok, 2000. "Volatility and information flows in emerging equity market: A case of the Korean Stock Exchange," International Review of Financial Analysis, Elsevier, vol. 9(4), pages 405-420.
    10. repec:lan:wpaper:3048 is not listed on IDEAS
    11. Roland Füss & Michael Bechtel, 2008. "Partisan politics and stock market performance: The effect of expected government partisanship on stock returns in the 2002 German federal election," Public Choice, Springer, vol. 135(3), pages 131-150, June.
    12. Liesenfeld, Roman & Richard, Jean-Francois, 2003. "Univariate and multivariate stochastic volatility models: estimation and diagnostics," Journal of Empirical Finance, Elsevier, vol. 10(4), pages 505-531, September.
    13. Junji Shimada & Yoshihiko Tsukuda, 2004. "Estimation of Stochastic Volatility Models : An Approximation to the Nonlinear State Space," Econometric Society 2004 Far Eastern Meetings 611, Econometric Society.
    14. Torben G. Andersen & Tim Bollerslev & Per Frederiksen & Morten Ørregaard Nielsen, 2008. "Continuous-Time Models, Realized Volatilities, and Testable Distributional Implications for Daily Stock Returns," Working Papers 1173, Queen's University, Department of Economics.
    15. repec:lan:wpaper:3050 is not listed on IDEAS
    16. Henryk Gurgul & Paweł Majdosz & Roland Mestel, 2007. "Price–volume relations of DAX companies," Financial Markets and Portfolio Management, Springer, vol. 21(3), pages 353-379, September.
    17. Thierry Ané & Loredana Ureche-Rangau, 2004. "Does trading volume really explain stock returns volatility?," Working Papers 2004-FIN-02, IESEG School of Management.
    18. Marwan Izzeldin, 2007. "Trading volume and the number of trades: a comparative study using high frequency data," Working Papers 584864, Lancaster University Management School, Economics Department.
    19. Ainhoa Zarraga, 2003. "GMM-based testing procedures of the mixture of distributions model," Applied Financial Economics, Taylor & Francis Journals, vol. 13(11), pages 841-848.

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