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Evolution of worldwide stock markets, correlation structure and correlation based graphs

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Author Info

  • Dong-Ming Song
  • Michele Tumminello
  • Wei-Xing Zhou
  • Rosario N. Mantegna

Abstract

We investigate the daily correlation present among market indices of stock exchanges located all over the world in the time period Jan 1996 - Jul 2009. We discover that the correlation among market indices presents both a fast and a slow dynamics. The slow dynamics reflects the development and consolidation of globalization. The fast dynamics is associated with critical events that originate in a specific country or region of the world and rapidly affect the global system. We provide evidence that the short term timescale of correlation among market indices is less than 3 trading months (about 60 trading days). The average values of the non diagonal elements of the correlation matrix, correlation based graphs and the spectral properties of the largest eigenvalues and eigenvectors of the correlation matrix are carrying information about the fast and slow dynamics of correlation of market indices. We introduce a measure of mutual information based on link co-occurrence in networks, in order to detect the fast dynamics of successive changes of correlation based graphs in a quantitative way.

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File URL: http://arxiv.org/pdf/1103.5555
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Bibliographic Info

Paper provided by arXiv.org in its series Papers with number 1103.5555.

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Date of creation: Mar 2011
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Publication status: Published in Physical Review E 84 (2), 026108 (2011)
Handle: RePEc:arx:papers:1103.5555

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Web page: http://arxiv.org/

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Cited by:
  1. Chunxia, Yang & Bingying, Xia & Sen, Hu & Rui, Wang, 2012. "A study of the interplay between the structure variation and fluctuations of the Shanghai stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(11), pages 3198-3205.
  2. Tang, Yong & Luo, Yong & Xiong, Jie & Zhao, Fei & Zhang, Yi-Cheng, 2013. "Impact of monetary policy changes on the Chinese monetary and stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4435-4449.
  3. A. Sienkiewicz & T. Gubiec & R. Kutner & Z. R. Struzik, 2013. "Dynamic structural and topological phase transitions on the Warsaw Stock Exchange: A phenomenological approach," Papers 1301.6506, arXiv.org.
  4. Sandoval, Leonidas Junior, 2013. "To lag or not to lag? How to compare indices of stock markets that operate at different times," Insper Working Papers wpe_319, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
  5. Trancoso, Tiago, 2014. "Emerging markets in the global economic network: Real(ly) decoupling?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 499-510.
  6. Lyócsa, Štefan & Výrost, Tomáš & Baumöhl, Eduard, 2012. "Stock market networks: The dynamic conditional correlation approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(16), pages 4147-4158.
  7. Hao Meng & Wen-Jie Xie & Zhi-Qiang Jiang & Boris Podobnik & Wei-Xing Zhou & H. Eugene Stanley, 2013. "Systemic risk and spatiotemporal dynamics of the US housing market," Papers 1306.2831, arXiv.org.
  8. Yue-Hua Dai & Wen-Jie Xie & Zhi-Qiang Jiang & George J. Jiang & Wei-Xing Zhou, 2014. "Correlation structure and principal components in global crude oil market," Papers 1405.5000, arXiv.org.
  9. 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.
  10. M. Wili\'nski & A. Sienkiewicz & T. Gubiec & R. Kutner & Z. R. Struzik, 2013. "Structural and topological phase transitions on the German Stock Exchange," Papers 1301.2530, arXiv.org, revised Jul 2013.
  11. Sandoval, Leonidas, 2014. "To lag or not to lag? How to compare indices of stock markets that operate on different times," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 227-243.
  12. Sandoval, Leonidas, 2012. "Pruning a minimum spanning tree," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(8), pages 2678-2711.
  13. Leonidas Sandoval Junior, 2011. "Pruning a Minimum Spanning Tree," Papers 1109.0642, arXiv.org.
  14. Caraiani, Petre, 2014. "The predictive power of singular value decomposition entropy for stock market dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 571-578.
  15. Dror Y. Kenett & Matthias Raddant & Thomas Lux & Eshel Ben-Jacob, 2011. "Evolvement of uniformity and volatility in the stressed global financial village," Kiel Working Papers 1739, Kiel Institute for the World Economy.
  16. Wiliński, M. & Sienkiewicz, A. & Gubiec, T. & Kutner, R. & Struzik, Z.R., 2013. "Structural and topological phase transitions on the German Stock Exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 5963-5973.
  17. Nobi, Ashadun & Maeng, Seong Eun & Ha, Gyeong Gyun & Lee, Jae Woo, 2014. "Effects of global financial crisis on network structure in a local stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 135-143.
  18. Mai, Yong & Chen, Huan & Meng, Lei, 2014. "An analysis of the sectorial influence of CSI300 stocks within the directed network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 235-241.
  19. Yang, Chunxia & Chen, Yanhua & Niu, Lei & Li, Qian, 2014. "Cointegration analysis and influence rank—A network approach to global stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 400(C), pages 168-185.
  20. Hu, Sen & Yang, Hualei & Cai, Boliang & Yang, Chunxia, 2013. "Research on spatial economic structure for different economic sectors from a perspective of a complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3682-3697.

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