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Spectral Analysis And Networks In Financial Correlation Matrices, Analisis Espectral Y Redes En Matrices De Correlacion Financiera

Author

Listed:
  • Linda Margarita Medina Herrera
  • Ernesto Armando Pacheco Velazquez
Abstract
This paper uses the theory of random matrices and minimal spanning trees to analyze the correlation matrix C of returns of the top 35 stocks traded on the Mexican Stock Market. The results show the spectrum of the matrix C has a random band structure. The eigenvector components outside this band show significant similarities to the topological measures of the minimum spanning tree. The telecommunications company AMX is the central vertex of the tree and the main component of the largest eigenvector. The tree measures support the conclusion that only two clusters are formed by economic sectors: construction & telecommunications, which have a great influence on other stocks.

Suggested Citation

  • Linda Margarita Medina Herrera & Ernesto Armando Pacheco Velazquez, 2013. "Spectral Analysis And Networks In Financial Correlation Matrices, Analisis Espectral Y Redes En Matrices De Correlacion Financiera," Revista Internacional Administracion & Finanzas, The Institute for Business and Finance Research, vol. 6(6), pages 15-28.
    • Handle: RePEc:ibf:riafin:v:6:y:2013:i:6:p:15-28
    as

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    References listed on IDEAS

    as
    1. Miccichè, Salvatore & Bonanno, Giovanni & Lillo, Fabrizio & N. Mantegna, Rosario, 2003. "Degree stability of a minimum spanning tree of price return and volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 66-73.
    2. Plerou, V & Gopikrishnan, P & Rosenow, B & Amaral, L.A.N & Stanley, H.E, 2000. "A random matrix theory approach to financial cross-correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 287(3), pages 374-382.
    3. Giovanni Bonanno & Nicolas Vandewalle & Rosario N. Mantegna, 2000. "Taxonomy of Stock Market Indices," Papers cond-mat/0001268, arXiv.org, revised Aug 2000.
    4. Varsha Kulkarni & Nivedita Deo, 2007. "Correlation and volatility in an Indian stock market: A random matrix approach," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 60(1), pages 101-109, November.
    5. Sergei Maslov, 2001. "Measures of globalization based on cross-correlations of world financial indices," Papers cond-mat/0103397, arXiv.org, revised Apr 2001.
    6. Laurent Laloux & Pierre Cizeau & Jean-Philippe Bouchaud & Marc Potters, 1999. "Random matrix theory," Science & Finance (CFM) working paper archive 500052, Science & Finance, Capital Fund Management.
    7. Barthélemy, Marc & Barrat, Alain & Pastor-Satorras, Romualdo & Vespignani, Alessandro, 2005. "Characterization and modeling of weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 346(1), pages 34-43.
    8. J.-P. Bouchaud & L. Laloux & M. A. Miceli & M. Potters, 2007. "Large dimension forecasting models and random singular value spectra," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 55(2), pages 201-207, January.
    9. Laurent Laloux & Pierre Cizeau & Jean-Philippe Bouchaud & Marc Potters, 1999. "Random matrix theory and financial correlations," Science & Finance (CFM) working paper archive 500053, Science & Finance, Capital Fund Management.
    10. Maslov, Sergei, 2001. "Measures of globalization based on cross-correlations of world financial indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 301(1), pages 397-406.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Random Matrix Theory; Stock Market Network; Econophysics; Minimum Spanning Tree;
    All these keywords.

    JEL classification:

    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

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