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Indispensable Source of Risk Contagion With Big Data Analysis From a More Comprehensive View on Shadow Banking
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Author(s): Peijin Li (Shanghai University of International Business and Economics, China), Xiutong Yi (Tonghui Equity Investment Fund Management Co., Ltd., China), Chonghui Zhang (Zhejiang Gongshang University, China)and Tomas Baležentis (Vilnius University, Lithuania)
Copyright: 2024
Volume: 32
Issue: 1
Pages: 29
Source title:
Journal of Global Information Management (JGIM)
Editor(s)-in-Chief: Zuopeng (Justin) Zhang (University of North Florida, USA)
DOI: 10.4018/JGIM.339190
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Abstract
Although shadow banking widely exists in the financial systems of various countries, their definitions vary significantly due to specific economic and financial characteristics. This paper classifies Chinese shadow banking into six categories: securities, trust, private lending, banking, fund, and insurance. The AR-GARCH-DCC model is used to measure systemic risk spillover through from an industrial and institutional perspective. The network topology index is employed to analyze risk contagion and further explore influencing factors. Firstly, based on the results of the AR-GARCH-DCC, the estimated dynamic volatility (σ) indicates that shadow banking risk spillover is time-varying, especially in trust and securities. Second, according to the static risk spillover analysis, various institutions play different roles and can transform between risk spillovers and overflowers. Thirdly, eigenvector centrality, leverage, assets, CPI, and macroeconomic prosperity significantly impact shadow banking systemic risk spillover.
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