本文基于最大熵与最小交叉熵原理,采用矩阵法模拟出2015年银行同业市场的微观数据。结合2015年银行–实体行业间的实际贷款数据,从经济金融关联网络的视角,用Gephi分别绘制出银行间网络,银行–实体间网络。根据复杂网络的统计理论,以度、加权平均度、介数中心性等为衡量指标,识别并分析系统重要性银行、系统重要性行业及两者间的内在关联。以期为监管部门立足全局视角,重点关注整个经济体系的关键环节,把控系统性风险,降低管理成本,提供一定的指导意义。
Based on the maximum and minimum cross-entropy principle, this paper simulates the micro-loan data of the interbank market in 2015. Combining with the actual loan data of the corresponding bank-entity industry, the paper draws the inter-bank network and the inter-bank-entity industry network respectively with Gephi, from the perspective of economic and financial related network. According to the statistical theory of complex networks, the paper identifies and analyzes the systemic importance banks, the systemic importance industries and the internal relationship between them, taking the degree, weighted average degree and median centrality as the measurement indicators. It is hoped that this paper will provide some guidance for the supervisory authorities to focus on the key links of the whole economic system from the overall perspective, control system risks and reduce management costs.
References
[1]
Allen, F. and Babus, A. (2009) Networks in Finance. In: Kleindorfer, P.R. and Wind, Y., Eds., The Network Challenge: Strategy, Profit, and Risk in an Interlinked World, Prentice Hall Professional, Upper Saddle River, NJ, 367-382.
[2]
Bech, M., Chapman, J. and Garratt, R. (2008) Which Bank Is the “Central”Bank? An Application of Markov Theory to the Canadian Large Value Transfer System. Federal Reserve Bank of New York Staff Report No. 356.
https://doi.org/10.2139/ssrn.1310283
Blien, U. and Grae, F (1997) Entropy Optimizing Methods for the Estimation of Tables. In: Balderjahn, I., Mathar, R. and Schader, M., Eds., Classification, Data Analysis and Data Highways, Springer Verlag, Berlin.