The Relationship Structure of Global Exchange Rate Based on Network Analysis
- 1 Sun Yat-sen Business School, Sun Yat-sen University, Guangzhou, China
- 2 Sun Yat-sen Business School, Sun Yat-sen University, Guangzhou, China
- 3 Sun Yat-sen Business School, Sun Yat-sen University, Guangzhou, China
- 4 Sun Yat-sen Business School, Sun Yat-sen University, Guangzhou, China
Abstract
The optimal threshold strategy is put forward for establishing a suitable network for analyzing the correlation among the different exchange rates. The 33 currencies of the world’s major countries and regions are analyzed by the method of network analysis, and the multilateral exchange rate correlation network is established based on the optimal threshold. Combining with geographical features and the exchange rate regime, it is found that the international currency has obvious community structure, which is composed of three levels: the core currency area, the arbitrage currency area and the shadow currency area. The conclusion reveals the structural characteristics of the Jamaica international monetary system.
- Jackson, M.O. (2008) Social and Economic Networks. Princeton University Press, Princeton, NJ, 44-74.
- Yang, J. (2010) Comparison of Research Paradigms between Complex Network and Social Network. Systems Engineering—Theory & Practice, 30, 2046-2055.
- Mitchell, J.C. (1969) The Concept and Use of Social Networks. Social Networks in Urban Situations.
- Scott, J. (1991) Social Network Analysis: A Handbook. Contemporary Sociology, 22, 128. https://doi.org/10.2307/2075047
- Granovetter, M.S. (1973) The Strength of Weak Ties. American Journal of Sociology, 78, 1360-1380. https://doi.org/10.1086/225469
- Cohen, L., Frazzini, A. and Malloy, C. (2008) The Small World of Investing: Board Connections and Mutual Fund Returns. Journal of Political Economy, 116, 951-979. https://doi.org/10.1086/592415
- Zhang, M., Tong, L. and Xu, H. (2015) Social Networks and Corporate Risk-Taking —Based on the Empirical Evidence of China’s Public Companies. Management World, No. 11, 161-175.
- Watts, D.J. and Strogatz, S.H. (1998) Collective Dynamics of “Small-World” Networks. Nature, 393, 440-442. https://doi.org/10.1038/30918
- Barabasi, A.L. and Albert, R. (1999) Emergence of Scaling in Random Networks. Science, 286, 509-512. https://doi.org/10.1126/science.286.5439.509
- Glasserman, P. and Young, H.P. (2015) How Likely Is Contagion in Financial Networks? Journal of Banking & Finance, 50, 383-399. https://doi.org/10.1016/j.jbankfin.2014.02.006
- Yan, Y., Yin, L., Li, X. and Chen, X. (2015) The U.S. Economy Is Controlled by Wall Street—The Enlightenment to the Development of State-Owned Capital Investment Company in China. Management World, No. 6, 1-7.
- Nier, E., Yang, J., Yorulmazer, T. and Alentorn, A. (2007) Network Models and Financial Stability. Journal of Economic Dynamics and Control, 31, 2033-2060. https://doi.org/10.1016/j.jedc.2007.01.014
- Li, Y., Cao, H. and Xing, H. (2012) Modeling and Simulation of Complex Finance Networks Based on Minority Game. Systems Engineering—Theory & Practice, 32, 1882-1890.
- Li, Z., Liang, Q. and Tu, X. (2016) The Connectedness of Chinese Listed Financial Institutions: A Study Based on Network Analysis. Journal of Financial Research, 434, 95-110.
- Zhang, L., Yang, Z. and Lu, F. (2014) Empirical Analysis of Relevance of Stock Indicators Based on Complex Network Theory. Chinese Journal of Management Science, 22, 85-92.