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

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Author Info
Liesenfeld, Roman
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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Publisher 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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  1. Nikolaus Hautsch, 2007. "Capturing Common Components in High-Frequency Financial Time Series: A Multivariate Stochastic Multiplicative Error Model," CFS Working Paper Series 2007/25, Center for Financial Studies. [Downloadable!]
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  2. Thierry Ané & Loredana Ureche-Rangau, 2004. "Does trading volume really explain stock returns volatility?," Working Papers 2004-FIN-02, IESEG School of Management. [Downloadable!]
  3. 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. [Downloadable!]
  4. Ainhoa Zarraga, 2003. "GMM-based testing procedures of the mixture of distributions model," Applied Financial Economics, Taylor and Francis Journals, vol. 13(11), pages 841-848, November. [Downloadable!] (restricted)
  5. 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. [Downloadable!] (restricted)
  6. Torben G. Andersen & Tim Bollerslev & Per Houmann Frederiksen & Morten Ørregaard Nielsen, 2007. "Continuous-Time Models, Realized Volatilities, and Testable Distributional Implications for Daily Stock Returns," CREATES Research Papers 2007-21, School of Economics and Management, University of Aarhus. [Downloadable!]
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  7. Nikolaus Hautsch, 2005. "The latent factor VAR model: Testing for a common component in the intraday trading process," FRU Working Papers 2005/03, University of Copenhagen. Department of Economics. Finance Research Unit. [Downloadable!]
  8. Marwan Izzeldin, 2007. "Trading volume and the number of trades: a comparative study using high frequency data," Working Papers 004798, Lancaster University Management School, Economics Department. [Downloadable!]
  9. 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. [Downloadable!] (restricted)
  10. 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. [Downloadable!]
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