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Multiplicative Stochastic Model of the Time Interval between Trades in Financial Markets

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  • V. Gontis
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    Abstract

    Stock price change in financial market occurs through transactions in analogy with diffusion in stochastic physical systems. The analysis of price changes in real markets shows that long-range correlations of price fluctuations largely depend on the number of transactions. We introduce the multiplicative stochastic model of time interval between trades and analyze spectral density and correlations of the number of transactions. The model reproduces spectral properties of the real markets and explains the mechanism of power law distribution of trading activity. Our study provides an evidence that statistical properties of financial markets are enclosed in the statistics of the time interval between trades. Multiplicative stochastic diffusion may serve as a consistent model for this statistics.

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    File URL: http://arxiv.org/pdf/cond-mat/0211317
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    Bibliographic Info

    Paper provided by arXiv.org in its series Papers with number cond-mat/0211317.

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    Date of creation: Nov 2002
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    Publication status: Published in Nonlinear Analysis: Modelling and Control, 2002, v. 7, No. 1, p. 43-54
    Handle: RePEc:arx:papers:cond-mat/0211317

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    Web page: http://arxiv.org/

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    1. Gopikrishnan, P & Plerou, V & Liu, Y & Amaral, L.A.N & Gabaix, X & Stanley, H.E, 2000. "Scaling and correlation in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 287(3), pages 362-373.
    2. Pierre Cizeau & Marc Potters & Jean-Philippe Bouchaud, 2000. "Correlation structure of extreme stock returns," Papers cond-mat/0006034, arXiv.org, revised Jan 2001.
    3. P. Cizeau & M. Potters & J-P. Bouchaud, 2001. "Correlation structure of extreme stock returns," Quantitative Finance, Taylor and Francis Journals, vol. 1(2), pages 217-222.
    4. Blank, Aharon & Solomon, Sorin, 2000. "Power laws in cities population, financial markets and internet sites (scaling in systems with a variable number of components)," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 287(1), pages 279-288.
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