Summary:
This paper uses the QFVAR model proposed by Ando et al. (2022), which is different from the traditional GFEVD method-based VAR model (Dieblod and Yilmaz, 2014), to measure the tail risk spillover effect among listed financial institutions under extreme positive and negative shocks. The left-tail risk induced by a negative shock and the right-tail risk induced by a positive shock represent risk accumulation and risk outbreak, respectively. This paper analyzes the risk spillover effect of financial systems and financial institutions, and the network structure of the risk spillover. On this basis, this paper empirically tests the risk spillover channels among financial institutions and the effectiveness of macroprudential policy tools in restraining the risk spillover effect. Our sample consists of 31 Chinese listed financial institutions, comprising 16 banks, 12 securities companies, and 3 insurance companies. The sample period runs from January 2011 to December 2020. The main findings are as follows. First, during the sample period, there are two different time trends in the total spillover index of the financial system. Induced by negative shocks, the left-tail spillover index rises sharply when there is drastic volatility in the financial markets, while the right-tail spillover index, induced by positive shocks, is consistent with the trend of the financial sector's leverage. Second, the risk spillover level of financial institutions induced by different directional shocks depends on specific financial characteristics. When the risk spillover is induced by a negative shock, large financial institutions have a higher spillover-to index and financial institutions with a large amount of interbank business have a higher spillover-in index. When the risk spillover is induced by a positive shock, highly leveraged financial institutions have a higher spillover-to index and financial institutions with a serious maturity mismatch problem have a higher spillover-in index. Third, a financial institution's risk induced by a negative shock will influence other institutions along the interbank business channel and the common asset holding channel. Moreover, a financial institution's risk induced by a positive shock will influence other institutions along the information channel. Fourth, the requirements for the reserve ratio and the liquidity coverage ratio can significantly mitigate the risk spillover effect along the interbank business channel, while the requirements for the capital adequacy ratio, the leverage ratio, and the loan-loss provision ratio can significantly mitigate the risk spillover effect along the common asset holding channel and the information channel. Our study has three policy implications. First, the regulator should take advantage of counter-cyclical policy timeously according to the operating conditions of the financial market and the financial industry. During periods of market turbulence, market liquidity should be maintained at an adequate level. During periods of financial boom, policy tools such as counter-cyclical capital buffers and dynamic loan-loss provisions can be used to reduce financial institutions' incentive for risk-taking. Second, the regulator can find out the emphases and put forward heterogeneous requirements appropriately according to the regulatory indicators of financial institutions. For financial institutions with a large amount of interbank business, a higher reserve ratio and liquidity coverage ratio should be required; for financial institutions with high leverage, the regulator should monitor their high-risk business activities; and large financial institutions should be required to maintain a higher leverage or capital adequacy ratio. Third, the regulator should further enrich the toolbox of macroprudential policies and optimize the macroprudential policy management system. Compared with existing studies, this paper makes following contributions. First, this paper divides exogenous shocks into positive and negative shocks, and combines the quantile regression model with the VAR model to measure the tail risk spillover effect. Second, by constructing connectedness indices and a regression analysis, this paper empirically tests the determinants of tail risk spillover among financial institutions to investigate the channels of risk spillover. Third, this paper empirically tests the effectiveness of macroprudential policy tools in mitigating tail risk spillover among institutions.
李志辉, 朱明皓, 李源, 李政. 我国金融机构的系统性风险溢出研究:测度、渠道与防范对策[J]. 金融研究, 2023, 514(4): 55-73.
LI Zhihui, ZHU Minghao, LI Yuan, LI Zheng. The Systemic Risk Spillover of Chinese Financial Institutions: Measurement, Channels,and Prevention. Journal of Financial Research, 2023, 514(4): 55-73.
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