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New statistic for financial return distributions: power-law or exponential?

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  • V. F. Pisarenko

    (Russian Acad. Sci.)

  • D. Sornette

    (UCLA and CNRS-Univ. Nice)

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    Abstract

    We introduce a new statistical tool (the TP-statistic and TE-statistic) designed specifically to compare the behavior of the sample tail of distributions with power-law and exponential tails as a function of the lower threshold u. One important property of these statistics is that they converge to zero for power laws or for exponentials correspondingly, regardless of the value of the exponent or of the form parameter. This is particularly useful for testing the structure of a distribution (power law or not, exponential or not) independently of the possibility of quantifying the values of the parameters. We apply these statistics to the distribution of returns of one century of daily data for the Dow Jones Industrial Average and over one year of 5-minutes data of the Nasdaq Composite index. Our analysis confirms previous works showing the tendency for the tails to resemble more and more a power law for the highest quantiles but we can detect clear deviations that suggest that the structure of the tails of the distributions of returns is more complex than usually assumed; it is clearly more complex that just a power law.

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    File URL: http://arxiv.org/pdf/physics/0403075
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    Bibliographic Info

    Paper provided by arXiv.org in its series Papers with number physics/0403075.

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    Date of creation: Mar 2004
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    Publication status: Published in Physica A 366, 387-400 (2006)
    Handle: RePEc:arx:papers:physics/0403075

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    Web page: http://arxiv.org/

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    1. Y. Malevergne & V. F. Pisarenko & D. Sornette, 2003. "Empirical Distributions of Log-Returns: between the Stretched Exponential and the Power Law?," Papers physics/0305089, arXiv.org.
    2. Y. Malevergne & V. Pisarenko & D. Sornette, 2006. "On the power of generalized extreme value (GEV) and generalized Pareto distribution (GPD) estimators for empirical distributions of stock returns," Applied Financial Economics, Taylor & Francis Journals, Taylor & Francis Journals, vol. 16(3), pages 271-289.
    3. P. Gopikrishnan & M. Meyer & L.A.N. Amaral & H.E. Stanley, 1998. "Inverse cubic law for the distribution of stock price variations," The European Physical Journal B - Condensed Matter and Complex Systems, Springer, Springer, vol. 3(2), pages 139-140, July.
    4. Benoit Mandelbrot, 1963. "The Variation of Certain Speculative Prices," The Journal of Business, University of Chicago Press, University of Chicago Press, vol. 36, pages 394.
    5. Quintos, Carmela & Fan, Zhenhong & Phillips, Peter C B, 2001. "Structural Change Tests in Tail Behaviour and the Asian Crisis," Review of Economic Studies, Wiley Blackwell, Wiley Blackwell, vol. 68(3), pages 633-63, July.
    6. Parameswaran Gopikrishnan & Vasiliki Plerou & Luis A. Nunes Amaral & Martin Meyer & H. Eugene Stanley, 1999. "Scaling of the distribution of fluctuations of financial market indices," Papers cond-mat/9905305, arXiv.org.
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