The paper entitled “Measuring Systemic Risk of China’s Banking Based on the Time-Varying Factor Copula Model”, which was written by our school’s professor Wang Hui together with Liang Junhao, a master degree candidate at Guanghua School of Management, Peking University, has been published in the Journal of Financial Research in November 2020.
The 2007 subprime crisis provides ample evidence of the inevitable consequences of systemic risk. The evidence has motivated researchers, academics, and regulators to recognize, measure, and prevent systemic risk. The need to prevent systematic financial risk has been gradually elevated to a level concerning national security. China’s banking system occupies a very important place in its financial system. The banking system has a closer internal relationship and dependence structure than other financial sectors because of inter-bank borrowing, payment, and settlement. Therefore, studies that measure systemic risk in China’s banking system, identify important and vulnerable systemic institutions, and prevent systemic financial risk are of great academic value and practical significance.
An accurate model of institutional dependence structures is required for measuring systemic risk. The model captures the spillover effect between institutions. Classic indicators such as MES and CoVaR for measuring systematic risk focus primarily on the relations between pairs of institutions or an individual firm and the market index. Consequently, they miss the dependency of the whole system and fail to take fat-tail and other related characteristics into full consideration. In view of these shortcomings, the paper applies the time-varying factor copula model, which analyzes the banking system’s idiosyncrasy and interconnectedness to 14 listed Chinese banks’ return data from 2007 to 2019. From the perspective of the dynamic dependence between the individual bank and the system, the paper proposes indicators of systemic risk in China’s banking system. First, the joint probability of distress (JPD) can be used as a measure for the probability that a majority of the financial institutions are in default. In addition, the Systemic Vulnerability Degree (SVD) and Systemic Importance Degree (SID) can identify systemically important institutions and systemically vulnerable institutions. The two categories account for the overall and local dependencies of the banking system. These indicators account for the individual bank’s idiosyncrasy, local and overall dependence, and fat-tailed and asymmetric characteristics of return data, capturing a range of information.
This study results in three findings.
First, the relationship between banks and the banking system increases as risk increases. The joint probability of distress accurately identifies systematic risk events. The JPD shows that macro-prudential assessment lowers systemic risk and the 2018-2019 trade friction between China and US increases the risk.
Second, big-five banks are most systemic stable and city commercial banks are most vulnerable in the sample period. Banks may have different rankings by systematic vulnerability during different periods. The systemic importance indicator (SID) shows that big-five banks are most affected by spillover during the sample period, which implies that big-five banks are not only “too big to fail” but also “too connected to fail.”
Third, compared with the dynamic factor Copula-based overall measurement approach, the DCC-GARCH model portraying the binary structure of market index and single institution will underestimate the systematic risk to some extent. The SID indicators proposed in the paper based on the dynamic factor model outperforms the traditional MES indicators and gives a similar ranking of systemic importance as SRISK. The measurement approach used in the paper saves the cost of data acquisition and is more time effective, which helps to provide reference for differentiated macro-prudential regulation.
