Paper Info
Title
Risk Spillover Effect Of Chinese Commercial Banks: Based On Indicator Method And Covar Approach
Abstract
This paper took the thirteen listed commercial banks in China as the research objects, and used the financial risk measurement method CoVaR to study the risk spillover effect of commercial banks. Firstly, we employed an indicator-method to study the systemic importance changes of the listed banks in the normal and risk periods. It is found that the systemic importance is always closely related to the size of bank's assets. The application of Cluster Analysis on the indicator score showed that they were divided into two part: four state-owned banks and nine joint-stock banks groups. From the perspective of financial market risk spillover effect caused by relevance, the risk contributions of each bank to the bank groups were calculated by using the quantile regression and CoVaR approaches in two time periods. The results showed that not only the systemically important banks with big asset scale had great risk spillover effect, in the risk period, some of the smaller scale banks had a significant increase in the risk contribution, which would also become an important source of risk. From the normal period to risk period, the risk spillover effects of each bank were obviously enhanced, and the risk contagion between banks rose generally. Therefore, regulators should not only focus on large-scale banks, but also pay attention to those banks with high risk contributions. (C) 2017 The Authors. Published by Elsevier B.V.
Year
DOI
Venue
2017
10.1016/j.procs.2017.11.457
5TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND QUANTITATIVE MANAGEMENT, ITQM 2017
Keywords
Field
DocType
commercial banks, risk spillovers, CoVaR, quantile regression
Financial risk,Computer science,China,Artificial intelligence,Monetary economics,Financial market,Spillover effect,Machine learning,Quantile regression
Conference
Volume
ISSN
Citations 
122
1877-0509
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Xiuqi Fang100.34
Meng-Di Du200.34
Long Wen311.71