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Abstract: The world has entered the digital economy era. As a developing country, China's banking industry plays an important role in the financial industry, and its size ranks first in the world. Therefore, it is of great significance to study the systemic risks of China's banks in the digital economy era. We first compare the traditional indicator approach and the market-based approach theoretically, and Conditional Value at Risk (CoVaR) model, a market-based approach, is considered to be an efficient way to discover systemic risk in different perspectives. Based on static and dynamic models, we evaluate the contributions of sixteen China's listed banks to the systemic risk. Furthermore, we model bank exposures, extend the models by considering extreme circumstance, and incorporate the effects of Fintech and non-bank financial institutions. The results show the levels of systemic risks and the corresponding systemic importance rankings vary in different time periods. We find that the contributions of some small banks to systemic risk are even higher than some big banks during the sample period. Moreover, the big banks face less risks than most of the small banks when the banking system is in distress. We make suggestions for improving financial supervision and maintaining financial stability.
- bank
- financial stability
- systemic risk
- CoVaR
- systemically important banks
- Fintech
- digital economy era
Discovering Systemic Risks of China's Listed Banks
by CoVaR Approach in the Digital Economy Era
In this paper, we first note that China is a bank-based country and that it is of great significance to study the systemic risks of Chinese banks in the digital economy era. In this paper, based on the static, dynamic, and modified CoVaR models, we quantitatively measure the systemic risk of 16 China’s listed commercial banks during the period of 2011–2018. Our findings and suggestions are as follows.
First, the market-based CoVaR approach, which is a useful complement for the indicator approach, could provide more information for strengthening financial supervision than the traditional indicator approach. The indicator system as well as the statistical tests clearly shows that the systemically important banks identified by the indicator approach are always big banks. However, we can conclude that for China, the systemic importance of a bank could not be simplified as the bank size rankings. Besides, the bank size rankings are not always positive and sometimes even negatively correlated with the rankings of systemic risk indicators (i.e., rankings of systemic importance) in the digital economy era. The conclusion still holds true when we assume that a bank is in extreme circumstance or considers the effects of Fintech and non-bank financial institutions.
Second, the systemic risk changes over time. Based on the CoVaR models, we measure the systemic risk of 16 listed banks from 2011 to 2018, year by year, and integrate an overall analysis of the sample banks during the entire sample period. It is found that the levels of systemic risk vary across different time periods with the changes in domestic and international economic and financial situations. In the era of the digital economy, information transfers faster than before and customers can enjoy the benefits in the era, while we also need to know that the risk could also transfer faster. The periodical assessment of the systemic risk in the digital economy era could provide on timely early warning for regulators to avoid the accumulation of the risk and the occurrence of a crisis.
Third, based on both the static CoVaR model and the dynamic CoVaR models which introduce the state variables, the systemic importance rankings of banks change over time. Therefore, we suggest that the financial supervision of SIFIs requires dynamic evaluation, and the dynamic model is an enhanced model of the traditional static model, which contains more information and is time-varying, and it should be further developed for financial supervision. Furthermore, for China, the year-by-year analyses show that systemically important banks change over time, especially the bank which contributes the most to systemic risk. Interestingly, some SOE banks (big banks) are systemically important, and these banks are also identified by the indicator approach due to their huge sizes. However, the results based on the market data indicate that some SOE banks are not always systemically important, and some JOI and CCB banks (small banks) are identified as the systemically important banks. The base and extended models support the following view, which coincides with the point that we propose in the introduction section: small banks could also contribute to systemic risk and cause a systemic effect in the digital economy era. We contribute to the existing studies with empirical evidence and theoretical analysis.
Overall, we suggest that in the digital economy era, the measurement of systemic risk and the identification of systemically important banks should be based on more and more market-based approaches, including the CoVaR model. Besides, with the development of Fintech and non-bank financial institutions, we should develop more models that incorporate the effects of these financial activities. In addition, the financial supervision, especially the reform of the macroprudential framework, should deeply consider the systemic risk contributions of not only big banks but also small banks, which may be not as large as the SOE banks in China but remain systemically important to the banking system. Differential supervision should be further developed to maintain financial stability not only in China but also around the world.
