摘要
2008年的国际金融危机表明,亟需找出从宏观审慎监管角度测度系统性风险的方法,而长期边际期望损失(LRMES)方法能测度单体银行的系统性风险贡献度。本文基于2008年1月2日至2018年3月31日中国14家上市商业银行日收益率数据,运用LRMES方法度量中国上市商业银行对系统性风险贡献度,并用各银行季度LRMES与规模和收入结构等变量建立面板数据模型。实证表明,LRMES比较符合中国银行业的实际情况。扩大银行资产规模可降低系统性风险,而非利息收入业务及其规模增速扩大则增加了系统性风险;规模和收入结构之间相互作用能降低系统性风险。此外,在进一步考察规模和收入结构之间相互作用时发现,资产规模在某个临界值以下的小银行,开展非利息收入业务会提高系统性风险;而在该临界值以上的大银行,开展非利息收入业务则能降低系统性风险。
The 2008 financial crisis showed that it was urgent to find a method to measure the systemic risk among financial institutions from the perspective of macro-prudential regulation, and the method of Long Run Marginal Expected Shortfall(LRMES) can measure the contribution of individual bank to the systemic risk. Based on the daily yield data of 14 listed commercial banks in China from January 2, 2008 to March 31, 2018, this paper uses the LRMES method to measure the contribution of Chinese listed commercial banks to the systemic risk, and establishes a panel data model with the variables of each bank’s quarterly LRMES, scale, income structure, etc. The empirical results show that LRMES is more in line with the actual situation of China’s banking industry. Expanding the scale of banks can reduce the systemic risk of banks, but developing non-interest income business will increase the systemic risk of banks. And interacting between scale and income structure can also reduce the systemic risk of banks. Moreover, we find that small banks with assets below a certain critical value conducting non-interest income business will increase their systemic risk, while large banks business with assets above the critical value conducting non-interest income will reduce their systemic risk.
出处
《金融监管研究》
CSSCI
北大核心
2019年第3期39-53,共15页
Financial Regulation Research
