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On the analysis of cross-correlations in South African market data

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Author Info

  • Wilcox, Diane
  • Gebbie, Tim
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    Abstract

    We report briefly on an application of random matrix theory to the analysis of SA financial market data (An Analysis of cross-correlations in South African financial market data, e- print cond-mat/0402389). Correlation matrices C are constructed from 10 years of daily data for stocks listed on the Johannesburg Stock Exchange from January 1993 to December 2002. Spectral properties of C are tested against random matrix predictions. We highlight some quantitative differences which arise when treating prices as existing only when measured, as opposed to interpolating missing or illiquid trading days with a zero-order hold.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0378437104009537
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    Bibliographic Info

    Article provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.

    Volume (Year): 344 (2004)
    Issue (Month): 1 ()
    Pages: 294-298

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    Handle: RePEc:eee:phsmap:v:344:y:2004:i:1:p:294-298

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    Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/

    Related research

    Keywords: Random matrices; Cross-correlations; Finance; Emerging markets;

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    Cited by:
    1. D. L. Wilcox & T. J. Gebbie, 2013. "On pricing kernels, information and risk," Papers 1310.4067, arXiv.org, revised Oct 2013.
    2. Thomas Conlon & Heather J. Ruskin & Martin Crane, 2010. "Random Matrix Theory and Fund of Funds Portfolio Optimisation," Papers 1005.5021, arXiv.org.
    3. Eterovic, Nicolas A. & Eterovic, Dalibor S., 2013. "Separating the wheat from the chaff: Understanding portfolio returns in an emerging market," Emerging Markets Review, Elsevier, vol. 16(C), pages 145-169.
    4. Sitabhra Sinha & Raj Kumar Pan, 2007. "Uncovering the Internal Structure of the Indian Financial Market: Cross-correlation behavior in the NSE," Papers 0704.2115, arXiv.org.
    5. Shi, Wenbin & Shang, Pengjian & Wang, Jing & Lin, Aijing, 2014. "Multiscale multifractal detrended cross-correlation analysis of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 35-44.
    6. Thomas Conlon & Heather J. Ruskin & Martin Crane, 2010. "Cross-Correlation Dynamics in Financial Time Series," Papers 1002.0321, arXiv.org.
    7. Conlon, T. & Ruskin, H.J. & Crane, M., 2009. "Cross-correlation dynamics in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(5), pages 705-714.
    8. Cheong, Siew Ann & Fornia, Robert Paulo & Lee, Gladys Hui Ting & Kok, Jun Liang & Yim, Woei Shyr & Xu, Danny Yuan & Zhang, Yiting, 2011. "The Japanese economy in crises: A time series segmentation study," Economics Discussion Papers 2011-24, Kiel Institute for the World Economy.
    9. Conlon, T. & Ruskin, H.J. & Crane, M., 2007. "Random matrix theory and fund of funds portfolio optimisation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(2), pages 565-576.
    10. Yin, Yi & Shang, Pengjian, 2013. "Modified DFA and DCCA approach for quantifying the multiscale correlation structure of financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6442-6457.
    11. Goswami, B. & Ambika, G. & Marwan, N. & Kurths, J., 2012. "On interrelations of recurrences and connectivity trends between stock indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(18), pages 4364-4376.

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