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Network analysis of correlation strength between the most developed countries

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  • Janusz Mi'skiewicz

Abstract

A new algorithm of the analysis of correlation among economy time series is proposed. The algorithm is based on the power law classification scheme (PLCS) followed by the analysis of the network on the percolation threshold (NPT). The algorithm was applied to the analysis of correlations among GDP per capita time series of 19 most developed countries in the periods (1982, 2011), (1992, 2011) and (2002, 2011). The representative countries with respect to strength of correlation, convergence of time series and stability of correlation are distinguished. The results are compared with ultrametric distance matrix analysed by NPT.

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  • Janusz Mi'skiewicz, 2012. "Network analysis of correlation strength between the most developed countries," Papers 1211.3599, arXiv.org.
    • Handle: RePEc:arx:papers:1211.3599
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    1. Todorova, Lora & Vogt, Bodo, 2011. "Power law distribution in high frequency financial data? An econometric analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4433-4444.
    2. Mantegna,Rosario N. & Stanley,H. Eugene, 2007. "Introduction to Econophysics," Cambridge Books, Cambridge University Press, number 9780521039871, October.
    3. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    4. Giovanni Bonanno & Nicolas Vandewalle & Rosario N. Mantegna, 2000. "Taxonomy of Stock Market Indices," Papers cond-mat/0001268, arXiv.org, revised Aug 2000.
    5. Di Matteo, T. & Aste, T. & Mantegna, R.N., 2004. "An interest rates cluster analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 339(1), pages 181-188.
    6. Naylor, Michael J. & Rose, Lawrence C. & Moyle, Brendan J., 2007. "Topology of foreign exchange markets using hierarchical structure methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(1), pages 199-208.
    7. Miśkiewicz, Janusz & Ausloos, Marcel, 2010. "Has the world economy reached its globalization limit?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(4), pages 797-806.
    8. Redelico, Francisco O. & Proto, Araceli N. & Ausloos, Marcel, 2009. "Hierarchical structures in the Gross Domestic Product per capita fluctuation in Latin American countries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(17), pages 3527-3535.
    9. Juan Brida & Wiston Risso, 2010. "Dynamics and Structure of the 30 Largest North American Companies," Computational Economics, Springer;Society for Computational Economics, vol. 35(1), pages 85-99, January.
    10. Miśkiewicz, Janusz & Ausloos, Marcel, 2008. "Correlation measure to detect time series distances, whence economy globalization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(26), pages 6584-6594.
    11. Mizuno, Takayuki & Takayasu, Hideki & Takayasu, Misako, 2006. "Correlation networks among currencies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 364(C), pages 336-342.
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