Abstract
This paper measures systemic risk in the Chinese banking sector by using the CoVaR approach. First, we introduce the student's t distribution into the standard DCC-MIDAS and propose a DCC-MIDAS-t model, particularly suitable for processing fat-tailed financial returns. We then apply the proposed DCC-MIDAS-t model to measure systemic risk following the idea of CoVaR. The empirical studies on the Chinese banking industry show that the DCC-MIDAS-t model is preferred in terms of the accuracy of volatility prediction and CoVaR measure. Macroeconomic information are proved to be able to improve the accuracy of systemic risk measure and M2 is most useful in comparing with IP and PPI. Several banks, such as BOC and BONB, suffered a lot from the stock market crash in June 2015 and some of them still maintain high risk spillovers during the after-crash period. Our findings suggest that these banks should be the focus of supervision even after the crash.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
