Fractal Properties of Financial Time Series

Hong Zhang; Ke Qiang Dong

doi:10.4028/www.scientific.net/KEM.439-440.683

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HomeKey Engineering MaterialsKey Engineering Materials Vols. 439-440Fractal Properties of Financial Time Series

Fractal Properties of Financial Time Series

Abstract:

In this paper, we analyze the stock of Nanjing Panda Electronics Co Ltd for the 44-year period, from May 2, 1996, to October 9, 2009, a total of 3200 trading days. Using the Box-counting dimension method, we find that the financial data have different power law exponents in the plot for the number of box and diameter of box, which indicates the multifractality exist in the time series. In order to investigate the latent properties in the data, the width and maximum of the singular spectrum are calculated. The results show the strong degree of multifractality in the time series.

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Key Engineering Materials (Volumes 439-440)

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683-687

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https://doi.org/10.4028/www.scientific.net/KEM.439-440.683

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June 2010

Authors:

Hong Zhang, Ke Qiang Dong

Keywords:

Box-Counting Dimension, Financial Time Serie, Fractal, Singular Spectrum

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References

[1] Mandelbrot B.B. Intermittent turbulence in self-similar cascades: divergence of high moments and dimension of the carrier. J. Fluid Mech. Vol 62(1974), p.331.

DOI: 10.1017/s0022112074000711

Google Scholar

[2] Xin-tian Zhuang, Xiao-yuan Huang, Yan-li Sha, Research on the fractal structure in the Chinese stock market, Physica A: Statistical and Theoretical Physics, Vol. 333(2004), P. 293.

DOI: 10.1016/j.physa.2003.10.061

Google Scholar

[3] Enrico Onali, John Goddard, Unifractality and multifractality in the Italian stock market, International Review of Financial Analysis, Vol. 18(2009), P. 154.

DOI: 10.1016/j.irfa.2009.05.001

Google Scholar

[4] Fraedrich, K. & Larnder, C. Scaling regimes of composite precipitation time series. Tellus Vol. 45A(1993), p.289.

DOI: 10.1034/j.1600-0870.1993.t01-3-00004.x

Google Scholar

[5] Harris, D., Menabde, M., Seed, A. & Austin, G. Multifractal characterization of precipitation fields with a strong orographic influence. J. Geophys. Res. Vol. 101(1996), p.26405.

DOI: 10.1029/96jd01656

Google Scholar

[6] Véronique L. Billat, Laurence Mille-Hamard, Yves Meyer, Eva Wesfreid, Detection of changes in the fractal scaling of heart rate and speed in a marathon race, Physica A: Statistical Mechanics and its Applications, Vol. 388(2009), P. 3798.

DOI: 10.1016/j.physa.2009.05.029

Google Scholar

[7] Hong Z, Keqiang D. Multifractal Analysis of Traffic Flow Time Series. Journal of Hebei University of Engineering Vol. 26(2009), p.109.

Google Scholar

[8] Takashi Nagatani, Vehicular traffic through a self-similar sequence of traffic lights, Physica A: Statistical Mechanics and its Applications, Vol. 386(2007), P. 381.

DOI: 10.1016/j.physa.2007.07.042

Google Scholar

[9] Falconer KJ. Fractal Geometry-Mathematical Foundations and Applications. New York: John Wiley & Sons(1990).

Google Scholar