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Fitting the empirical distribution of intertrade durations

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  • Politi, Mauro
  • Scalas, Enrico

Abstract

Based on the analysis of a tick-by-tick data set used in the previous work by one of the authors (DJIA stocks traded at NYSE in October 1999), in this paper, we reject the hypothesis that tails of the empirical intertrade distribution are described by a power law. We further argue that the Tsallis q-exponentials are a viable tool for fitting and describing the unconditional distribution of empirical intertrade durations and they compare well to the Weibull distribution.

Suggested Citation

  • Politi, Mauro & Scalas, Enrico, 2008. "Fitting the empirical distribution of intertrade durations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(8), pages 2025-2034.
    • Handle: RePEc:eee:phsmap:v:387:y:2008:i:8:p:2025-2034
    DOI: 10.1016/j.physa.2007.11.018
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    References listed on IDEAS

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    1. Scalas, Enrico, 2006. "The application of continuous-time random walks in finance and economics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 362(2), pages 225-239.
    2. Lisa Borland, 2002. "Option Pricing Formulas based on a non-Gaussian Stock Price Model," Papers cond-mat/0204331, arXiv.org, revised Sep 2002.
    3. Scalas, Enrico, 2007. "Mixtures of compound Poisson processes as models of tick-by-tick financial data," Chaos, Solitons & Fractals, Elsevier, vol. 34(1), pages 33-40.
    4. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
    5. Bertram, William K, 2004. "An empirical investigation of Australian Stock Exchange data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 341(C), pages 533-546.
    6. Plamen Ch. Ivanov & Ainslie Yuen & Boris Podobnik & Youngki Lee, 2004. "Common Scaling Patterns in Intertrade Times of U. S. Stocks," Papers cond-mat/0403662, arXiv.org.
    7. Z. Eisler & J. Kertész, 2006. "Size matters: some stylized facts of the stock market revisited," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 51(1), pages 145-154, May.
    8. Sazuka, Naoya, 2007. "On the gap between an empirical distribution and an exponential distribution of waiting times for price changes in a financial market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 376(C), pages 500-506.
    9. Zoltan Eisler & Janos Kertesz, 2005. "Size matters: some stylized facts of the stock market revisited," Papers physics/0508156, arXiv.org, revised May 2006.
    10. M. Goldstein & S. Morris & G. Yen, 2004. "Problems with fitting to the power-law distribution," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 41(2), pages 255-258, September.
    11. J. de Souza & L. G. Moyano & S. M. Duarte Queirós, 2006. "On statistical properties of traded volume in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 50(1), pages 165-168, March.
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