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State and Network Structures of Stock Markets Around the Global Financial Crisis

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Abstract

We consider the effects of the 2008 global financial crisis on the global stock market before, during, and after the crisis. We generate complex networks from a cross-correlation matrix such as the threshold network (TN) and the minimal spanning tree (MST). In the threshold network, we assign a threshold value by using the mean and standard deviation of cross-correlation coefficients. When the threshold is equal to the mean of these coefficients, we observe a giant cluster composed of three economic zones in all three periods. We find that during the crisis, the countries in the Asian zone were weakly connected and those in the American zone were tightly linked to the countries in the European zone. At a large threshold, the three economic zones were fragmented. The European countries connected tightly, but the Asian countries bound weakly. The MST constructed from the distance matrix. In the MST, France remained a hub node in all three periods. The size of the MST shrank slightly during the crisis. We observe a scaling relation between the network distance of nodes from the central hub (France) and the geometrical distance. We observe the topological change of the financial network structure during the global financial crisis. The TN and MST are complementary roles to understand the connecting structure of financial complex networks. The TN reveals to observe the clustering effects and robustness of the cluster during the financial crisis. The MST shows the central hub and connecting node among the economic zones.

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Notes

  1. http://www.bloomberg.com/.

  2. http://www.doingbusiness.org/.

  3. Financial Deregulation and Integration in East Asia, NBER-EASE, NgiamKeeJin, Volume 5.

  4. Stephane Dees and Aurther Saint-Guilhem, European Central Bank, Working Paper Series 1034 (1994).

  5. http://www.worldbank.org/.

References

  • Baba, N., & Packer, F. (2009). From turmoil to crisis: Dislocations in the FX swap marketbefore and after the failure of Lehman Brothers. Journal of International Money and Finance, 28, 1350–1374.

    Article  Google Scholar 

  • Brida, J. G., Matesanz, D., & Seijas, M. N. (2016). Network analysis of returns and volume trading in stock market: The Euro Stoxx case. Physica A, 444, 751–764.

    Article  Google Scholar 

  • Clauset, A., Shalizi, C. R., & Newman, M. E. J. (2009). Power-law distributions in empirical data. SIAM Review, 51, 661–703.

    Article  Google Scholar 

  • Cleveland, W. S. (1981). LOWESS: A program for smoothing scatterplots by robust locally weighted regression. The American Statistician, 35(1), 54.

    Article  Google Scholar 

  • Ebel, H., Mielsch, L.-I., & Bornholdt, S. (2002). Scale-free topology of e-mail networks. Physical Review E, 66, 035103.

    Article  Google Scholar 

  • Eryigit, M., & Eryigit, R. (2009). Network structure of cross-correlations among the world market. Physica A, 388, 3551–3562.

    Article  Google Scholar 

  • Fortunate, S. (2010). Community detection in graphs. Physical Report, 486, 75–174.

    Article  Google Scholar 

  • Gleiser, P., & Danon, L. (2003). Jazz musicians network: List of edges of the network of Jazz musicians. Advance in Complex System, 6, 565.

    Article  Google Scholar 

  • He, J., & Deem, M. W. (2010). Structure and response in the world trade network. Physical Review Letters, 105, 198701.

    Article  Google Scholar 

  • Huang, W. Q., Zhuang, X. T., & Yao, S. (2009). A network analysis of the Chinese stock market. Physica A, 388, 2956.

    Article  Google Scholar 

  • Hui, E. C. M., & Chan, K. K. K. (2015). Testing calendar effects on global securitized real estate markets by Shirayaev–Zhou index. Habitat International, 48, 38–45.

    Article  Google Scholar 

  • Kantar, E., Keskin, M., & Deviren, B. (2012). Analysis of the effects of the global financial crisis on the Turkish economy, using hierarchical methods. Physica A, 391, 2342.

    Article  Google Scholar 

  • Kumar, S., & Deo, N. (2012). Correlation and network analysis of global financial indices. Physical Review E, 86, 026101.

    Article  Google Scholar 

  • Latora, V., & Marchiori, M. (2001). Efficient behavior of small-world networks. Physical Review Letters, 87, 198701.

    Article  Google Scholar 

  • Lin, W.-L., Engle, R. F., & Ito, T. (1994). Do bulls and bears move across borders? International transmission of stock returns and volatility. Review of Finance Studies, 7, 507–538.

    Article  Google Scholar 

  • Mantegna, R. N. (1999). Hierarchical structure in financial markets. European Physical Journal B, 11, 193.

    Article  Google Scholar 

  • Namaki, A., Shirazi, A. H., Raei, R., & Jafari, G. R. (2012). Network analysis of a financial market based on genuine correlation and threshold method. Physica A, 390, 3835–3841.

    Article  Google Scholar 

  • Newman, M. E. J. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences USA, 103, 8577–8582.

    Article  Google Scholar 

  • Newman, M. E. J. (2012). Communities, modules and large-scale structure in networks. Nature Physics, 8, 25–32.

    Article  Google Scholar 

  • Nobi, A., & Lee, J. W. (2016). State and group dynamics of world stock market by principal component analysis. Physica A, 450, 85–94.

    Article  Google Scholar 

  • Nobi, A., Maeng, S. E., Ha, G. H., & Lee, J. W. (2013). Random matrix theory and cross-correlations in global financial indices and local stock market indices. Journal of the Korean Physical Society, 62, 569–574.

    Article  Google Scholar 

  • Nobi, A., Maeng, S. E., Ha, G. H., & Lee, J. W. (2014). Effects of global financial crisis on network structur in a local stock market. Physica A, 407, 135–143.

    Article  Google Scholar 

  • Nobi, A., Maeng, S. E., Ha, G. H., & Lee, J. W. (2015). Structural changes in the minimal spanning tree and the hierarchical network in the Korean stock market around the global financial crisis. Journal of the Korean Physical Society, 66, L1153–L1159.

    Article  Google Scholar 

  • Onnella, J.-P., Chakraborti, A., Kaski, K., Kertesz, J., & Kanto, A. (2003). Dynamics of market correlations: Taxonomy and portfolio analysis. Physical Review E, 68, 056110.

    Article  Google Scholar 

  • Onnela, J.-P., Saramaki, J., Kertesz, J., & Kaski, K. (2005). Intensity and coherence of motifs in weighted complex networks. Physical Review E, 71, 065103.

    Article  Google Scholar 

  • Qiu, T., Zheng, B., & Chen, G. (2010). Financial networks with static and dynamic thresholds. New Journal of Physics, 12, 043047.

    Article  Google Scholar 

  • Sienkiewicz, A., Gubiec, T., Kutner, R., & Struzik, Z. R. (2013). Dynamic structural and topological phase transitions on the Warsaw Stock Exchange: A phenomenological approach. Acta Physica Polonica A, 123, 615–620.

    Article  Google Scholar 

  • Song, D.-M., Tumminello, M., Zhou, W.-X., & Mantegna, R. N. (2011). Evolution of worldwide stock markets, correlation structure, and correlation-based graphs. Physical Review E, 84, 026108.

    Article  Google Scholar 

  • Vandewalle, N., Brisbois, F., & Tordoir, X. (2001). Non-random topology of stock markets. Quantitavie Finance, 1, 261–374.

    Google Scholar 

  • Wang, G. J., & Xie, C. (2015). Correlation structure and dynamics of international real estate securities markets: A network perspective. Physica A, 424, 176–193.

    Article  Google Scholar 

  • Wang, G. J., & Xie, C. (2016). Tail dependence structure of the foreign exchange market: A network view. Expert Systems with Applications, 46, 164–179.

    Article  Google Scholar 

  • Wilinski, M., Sienkiewicz, A., Gubiec, T., Kutner, R., & Struzik, Z. R. (2013). Structural and topological phase transitions on the German Stock Exchange. Physica A, 392, 5963.

    Article  Google Scholar 

  • Yan, X.-G., Xie, C., & Wang, G.-J. (2014). The stability of financial market networks. Europhysics Letters, 107, 48002.

    Article  Google Scholar 

  • Zachary, W. W. (1977). An information flow model for conflict and fission in small groups. Journal of Anthropological Research, 33, 452–473.

    Article  Google Scholar 

  • Zhao, L., Li, W., & Cai, X. (2016). Structure and dynamics of stock market in times of crisis. Physics Letters A, 380, 654–666.

    Article  Google Scholar 

  • Zheng, Z., Yamasaki, K., Tenenbaum, J. N., & Stanley, H. E. (2013). Carbon-dioxide emissions trading and hierarchical structure in worldwide finance and commodities markets. Physical Review E, 87, 012814.

    Article  Google Scholar 

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Acknowledgements

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2014R1A2A1A11051982).

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Authors and Affiliations

  1. Inha University, Incheon, Republic of Korea

    Jae Woo Lee

  2. Noakhali Science and Technology University, Sonapur, Bangladesh

    Ashadun Nobi

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  1. Jae Woo Lee
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Correspondence to Jae Woo Lee.

Appendix

Appendix

Global Stock Indices are used in the work.

We consider 35 world stock indices. The European economic zone includes 17 countries: France (FRA), Germany (GER), Italy (ITA), the United Kingdom (UK), Espana (ESP), Switzerland (SWIZ), Netherland (NETH), Belgium (BEL), Norway (NOR), Ireland (IRL), Greece (GRC), Finland (FIN), Denmark (DEN), Austria (AUT), Turkey (TUR), Sweden (SWE), and Russia (RUS). In the Asian and Australian economic zone we include 13 countries: Japan (JPN), South Korea (KOR), Singapore (SING), Hong Kong (HONG), Indonesia (INDO), Taiwan (TWN), Malaysia (MAL), China (CHA), Thailand (THAI), India (IND), the Philippines (PHL), Israel (ISR), and Australia (AUS). The number of countries in American economic zone is five, including the United States (US), Canada (CAN), Mexico (MEX), Argentina (ARG), and Brazil (BRA).

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Lee, J.W., Nobi, A. State and Network Structures of Stock Markets Around the Global Financial Crisis. Comput Econ 51, 195–210 (2018). https://doi.org/10.1007/s10614-017-9672-x

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