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Issue title: Special Section: Intelligent Data Aggregation Inspired Paradigm and Approaches in IoT Applications
Guest editors: Xiaohui Yuan and Mohamed Elhoseny
Article type: Research Article
Authors: Gu, Yanhuia; b | Zhu, Shuzhena; * | Yang, Zhipinga; b | Zhao, Yuanjuna
Affiliations: [a] Glorious Sun School of Business and Management, Donghua University, Shanghai, China | [b] School of Economic and Management, Jiujiang University, Jiujiang, China
Correspondence: [*] Corresponding author. Shuzhen Zhu, Glorious Sun School of Business and Management, Donghua University, Shanghai 200051,China. E-mail: [email protected].
Abstract: In this paper we suggest a new approach to assessment of banking systemic risk contagion. Rather than the static analysis method, we introduce a dynamic analysis method to simulate the contagion of banking systemic risk. Banking systemic risk contagion is similar to the spread of infectious disease. In this paper, analysis is made on the process of banking systemic risk contagion by means of Matlab simulation based on network dynamic time-variant contagion kinetics model. The conclusion shows that high banking risk contagion rate, low risk immunization rate or low risk isolation protection rate all are the basic reasons for that the “risk contagion reproductive rate” reaches the threshold value to make banking systemic risk contagion uncontrollable, and it is suggested to ensure banking system safety by taking measures from the three aspects pointed out above and combining with prudential supervision policies.
Keywords: Network dynamic time-varying, contagion kinetics model, banking systemic risk contagion, risk contagion rate, risk immunization rate, risk isolation protection rate
DOI: 10.3233/JIFS-179094
Journal: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 381-395, 2019
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