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Modelling China's Credit System with Complex Network Theory for Systematic Credit Risk Control (1812.01341v1)

Published 4 Dec 2018 in q-fin.RM

Abstract: The insufficient understanding of the credit network structure was recognized as a key factor for regulators' underestimation of the destructive systematic risk during the financial crisis that started in 2007. The existing credit network research either took a macro perspective to clarify the topological properties of financial systems at a descriptive level or analyzed the risk transmission path and characteristics of individual entities with much pre-assumptions of the network. Here, we used the theory of complex network to model China's credit system from 2000 to 2014 based on actual financial data. A bipartite financial institution-firm network and its projected sub-networks were constructed for an integrated analysis from both macro and micro perspectives, and the relationship between typological properties and systematic credit risk control was also explored. The typological analysis of the networks suggested that the financial institutions and firms were highly but asymmetrically connected, and the credit network structure made local idiosyncratic shocks possible to proliferate through the whole economy. In addition, the Chinese credit market was still dominated by state-owned financial institutions with firms competing fiercely for financial resources in the past fifteen years. Furthermore, the credit risk score (CRS) was introduced by simulation to identify the systematically important vertices in terms of systematic risk control. The results indicated that the vertices with more access to the credit market or less likelihood to be a bridge in the network were the ones with higher systematically importance. The empirical results from this study would provide specific policy suggestions to financial regulators on supervisory approaches and optimizing the allocation of regulatory resources to enhance the robustness of credit systems in China and in other countries.

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