Correlation Structure and Evolution of World Stock Markets: Evidence from Pearson and Partial Correlation-Based Networks
- 1.7k Downloads
We construct a Pearson correlation-based network and a partial correlation-based network, i.e., two minimum spanning trees (MST-Pearson and MST-Partial), to analyze the correlation structure and evolution of world stock markets. We propose a new method for constructing the MST-Partial. We use daily price indices of 57 stock markets from 2005 to 2014 and find (i) that the distributions of the Pearson correlation coefficient and the partial correlation coefficient differ completely, which implies that the correlation between pairs of stock markets is greatly affected by other markets, and (ii) that both MSTs are scale-free networks and that the MST-Pearson network is more compact than the MST-Partial. Depending on the geographical locations of the stock markets, two large clusters (i.e., European and Asia-Pacific) are formed in the MST-Pearson, but in the MST-Partial the European cluster splits into two subgroups bridged by the American cluster with the USA at its center. We also find (iii) that the centrality structure indicates that outcomes obtained from the MST-Partial are more reasonable and useful than those from the MST-Pearson, e.g., in the MST-Partial, markets of the USA, Germany, and Japan clearly serve as hubs or connectors in world stock markets, (iv) that during the 2008 financial crisis the time-varying topological measures of the two MSTs formed a valley, implying that during a crisis stock markets are tightly correlated and information (e.g., about price fluctuations) is transmitted quickly, and (v) that the presence of multi-step survival ratios indicates that network stability decreases as step length increases. From these findings we conclude that the MST-Partial is an effective new tool for use by international investors and hedge-fund operators.
KeywordsStock markets Correlation structure Network Minimum spanning tree Evolution
We are grateful to the Editor (Hans M. Amman) and four anonymous referees for their insightful comments and suggestions. The work was supported by the National Natural Science Foundation of China (Grant Nos. 71501066 and 71373072), the China Scholarship Council (Grant No. 201506135022), the Specialized Research Fund for the Doctoral Program of Higher Education (Grant No. 20130161110031), and the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (Grant No. 71521061).
- Drożdż, S., Grümmer, F., Ruf, F., & Speth, J. (2001). Towards identifying the world stock market cross-correlations: Dax versus Dow Jones. Physica A, 294(1–2), 226–234.Google Scholar
- Kenett, D. Y., Raddant, M., Zatlavi, L., Lux, T., & Ben-Jacob, E. (2012b). Correlations and dependencies in the global financial village. International Journal of Modern Physics: Conference Series, 16(1), 13–28.Google Scholar
- Mantegna, R. N., & Stanley, H. E. (2000). An introduction to econophysics: Correlations and complexity in finance. Cambirdge: Cambirdge University Press.Google Scholar
- Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91.Google Scholar
- Wang, G. J., Xie, C., Zhang, P., Han, F., & Chen, S. (2014). Dynamics of foreign exchange networks: A time-varying copula approach. Discrete Dynamics in Nature and Society, 2014, 170921.Google Scholar