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The use of dynamical networks to detect the hierarchical organization of financial market sectors

  • Topical Issue on Interdisciplinary Applications of Physics in Economics and Finance
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Abstract

Two kinds of filtered networks: minimum spanning trees (MSTs) and planar maximally filtered graphs (PMFGs) are constructed from dynamical correlations computed over a moving window. We study the evolution over time of both hierarchical and topological properties of these graphs in relation to market fluctuations. We verify that the dynamical PMFG preserves the same hierarchical structure as the dynamical MST, providing in addition a more significant and richer structure, a stronger robustness and dynamical stability. Central and peripheral stocks are differentiated by using a combination of different topological measures. We find stocks well connected and central; stocks well connected but peripheral; stocks poorly connected but central; stocks poorly connected and peripheral. It results that the Financial sector plays a central role in the entire system. The robustness, stability and persistence of these findings are verified by changing the time window and by performing the computations on different time periods. We discuss these results and the economic meaning of this hierarchical positioning.

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References

  1. R.N. Mantegna, EPJB 11, 193 (1999)

    Article  ADS  Google Scholar 

  2. S.H. Strogatz, Nature 410, 268 (2001)

    Article  ADS  Google Scholar 

  3. J.-P. Onnela, M.Sc. Thesis, Department of Electrical and Communications Engineering, Helsinki University of Technology (2002)

  4. J.-P. Onnela, A. Chakraborti, K. Kaski, J. Kertész, EPJB 30, 285 (2002)

    Article  ADS  Google Scholar 

  5. J.-P. Onnela, A. Chakraborti, K. Kaski, J. Kertész, A. Kanto, Phys. Rev. E 68, 056110 (2003)

    Article  ADS  Google Scholar 

  6. J.-P. Onnela, A. Chakraborti, K. Kaski, J. Kertész, Physica A 324, 247 (2003)

    Article  MATH  MathSciNet  ADS  Google Scholar 

  7. T. Aste, T. Di Matteo, S.T. Hyde, Physica A 346, 20 (2005)

    Article  ADS  Google Scholar 

  8. M. Tumminello, T. Aste, T. Di Matteo, R.N. Mantegna, PNAS 102/30, 10421 (2005)

    Article  ADS  Google Scholar 

  9. M. Tumminello, T. Aste, T. Di Matteo, R.N. Mantegna, EPJB 55, 209 (2007)

    Article  ADS  Google Scholar 

  10. H.M. Ohlenbusch, T. Aste, B. Dubertret, N. Rivier, EPJB 2, 211 (1998)

    Article  ADS  Google Scholar 

  11. T. Aste, D. Sherrington, J. Phys. A 32, 7049 (1999)

    Article  MATH  MathSciNet  ADS  Google Scholar 

  12. T. Aste, T. Di Matteo, M. Tumminello, R.N. Mantegna, Proc. SPIE 5848, 100 (2005)

    Article  ADS  Google Scholar 

  13. T. Aste, T. Di Matteo, Physica 370, 156 (2006)

    Article  Google Scholar 

  14. T. Di Matteo, T. Aste, Proc. SPIE 6039, 60390P–1 (2006)

    Article  Google Scholar 

  15. J.C. Gower, G.J.S. Ross, Applied Statistics 18/1, 54 (1969)

    Article  MathSciNet  Google Scholar 

  16. A planar graph is a graph that can be represented on an Euclidean plane with no intersections between edges

  17. T. Di Matteo, T. Aste, International Journal of Theoretical and Applied Finance 5/1, 107 (2002)

    Google Scholar 

  18. T. Di Matteo, T. Aste, R.N. Mantegna, Physica A 339, 181 (2004)

    Article  ADS  Google Scholar 

  19. T. Di Matteo, T. Aste, S.T. Hyde, S. Ramsden, Physica A 355, 21 (2005)

    Article  MathSciNet  ADS  Google Scholar 

  20. J. Eisner, Manuscript, University of Pennsylvania (1997)

  21. F. Pozzi, T. Aste, G. Rotundo, T. Di Matteo, Proc. SPIE 6802, 68021E (2008)

    Article  Google Scholar 

  22. G. Caldarelli, Scale-Free Networks. Complex Webs in Nature and Technology (Oxford University Press, 2007)

  23. S.N. Dorogovtsev, J.F.F. Mendes, Evolution of Networks. From Biological Nets to the Internet and www (Oxford University Press, 2003)

  24. L. Lebart, A. Morineau, M. Piron, Statistique exploratoire multidimensionnelle (Dunod, Paris, 1995), Chap. 2, pp. 155–175

    MATH  Google Scholar 

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Authors and Affiliations

  1. Applied Mathematics, Research School of Physical Sciences, The Australian National University, 0200, Canberra, Australia

    T. Di Matteo, F. Pozzi & T. Aste

  2. Department of Mathematics, King’s College, The Strand, London, WC2R 2LS, UK

    T. Di Matteo & T. Aste

  3. School of Physical Sciences, University of Kent, Canterbury, Kent, CT2 7NH, UK

    T. Aste

Authors
  1. T. Di Matteo
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  2. F. Pozzi
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  3. T. Aste
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Correspondence to T. Aste.

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Di Matteo, T., Pozzi, F. & Aste, T. The use of dynamical networks to detect the hierarchical organization of financial market sectors. Eur. Phys. J. B 73, 3–11 (2010). https://doi.org/10.1140/epjb/e2009-00286-0

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  • DOI: https://doi.org/10.1140/epjb/e2009-00286-0

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