商业银行中间业务的系统性风险溢出效应
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摘要
本研究使用△CoVaR~≤方法对2007年1季度至2018年3季度我国20家上市银行的系统性风险溢出效应进行了测度,应用面板数据模型对商业银行中间业务发展的系统性风险溢出效应问题进行了考察。研究表明,我国金融体系系统性风险具有显著的时变性特征,我国商业银行系统性风险溢出效应也具有显著的时变性特征。商业银行系统性风险溢出效应与规模并无显著的直接联系,而中间业务的发展具有显著为正的系统性风险溢出效应,这在股份制银行和城市商业银行中尤为明显。对新时期我国系统性风险防范而言,不仅要高度重视商业银行不同中间业务发展所导致差异化网络结构效应,而且要警惕房地产市场调整对股份制银行的潜在冲击,还应密切关注网络关联结构演变对系统重要性银行识别和宏观审慎评估政策实施的重要影响。
In this study, we measure the spillover effect of systemic risk of 20 listed banks in China from the first quarter of 2007 to the third quarter of 2018 by CoVaR≤method, andinvestigate the systemic risk spillovers of the development of intermediate business of commercial banks by using the panel data model. The research shows that the systemic risk of China's financial system has significant time-varying characteristics, so does the systemic risk spillovers of China's commercial banks. There is no significant direct relationship between the systemic risk spillovers and the scale of commercial banks, while the development of intermediate business has a significant positive systemic risk spillover,especially in joint-stock banks and urban commercial banks. For the prevention of systemic risk in China in the new era, we should not only attach great importance to the differentiated network structure effect caused by the development of different intermediate business of commercial banks, but also be alert to the potential impact of real estate market adjustment on joint-stock banks. We should also pay close attention to the important impact of the evolution of network connection structure on the identification of systemically important banks and the implementation of macro-prudential assessment policies.
In this study, we measure the spillover effect of systemic risk of 20 listed banks in China from the first quarter of 2007 to the third quarter of 2018 by CoVaR≤method, andinvestigate the systemic risk spillovers of the development of intermediate business of commercial banks by using the panel data model. The research shows that the systemic risk of China's financial system has significant time-varying characteristics, so does the systemic risk spillovers of China's commercial banks. There is no significant direct relationship between the systemic risk spillovers and the scale of commercial banks, while the development of intermediate business has a significant positive systemic risk spillover,especially in joint-stock banks and urban commercial banks. For the prevention of systemic risk in China in the new era, we should not only attach great importance to the differentiated network structure effect caused by the development of different intermediate business of commercial banks, but also be alert to the potential impact of real estate market adjustment on joint-stock banks. We should also pay close attention to the important impact of the evolution of network connection structure on the identification of systemically important banks and the implementation of macro-prudential assessment policies.
引文
[1]朱波,杨文华,邓叶峰.非利息收入降低了银行的系统性风险吗?——基于规模异质的视角[J].国际金融研究,2016(4):62-73.
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[4]朱波,马永谈.行业特征、货币政策与系统性风险——基于“经济金融”关联网络的分析[J].国际金融研究,2018(4):22-32.
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[8]Adrian T. and Brunnermeier M. K. CoVaR. American Economic Review, 2016, 106:1705-1741.
[9]朱波,马永谈,陈德然.债务融资方式对行业金融风险溢出效应的作用机制[J].财经科学,2018(1):15-27.
[10]范小云,王道平,刘澜飚.规模、关联性与中国系统重要性银行的衡量[J].金融研究,2012(11):16-30.
[11]欧阳红兵,刘晓东.中国金融机构的系统重要性及系统性风险传染机制分析——基于复杂网络的视角[J].中国管理科学,2015, 23(10):30-37.
[12]邓向荣,曹红.系统性风险、网络传染与金融机构系统重要性评估[J].中央财经大学学报,2016(3):52-60.
[13]程凤朝,崔宏.理性看待商业银行中间业务收入占比[J].中国金融,2011(17):51-52.
[14]张国海,高怿.商业银行中间业务的国际比较与发展战略[J].金融研究,2003(8):129-134.
[15]陈胡青.我国商业银行中间业务发展研究——基于2010-2011年我国14家商业银行中间业务收入的分析[J].金融与经济,2012(6):74-77.
[16]MainikG and SchaanningE. On Dependence Consistency of CoVaR and Some Other Systemic Risk Measures.Statistics&Risk Modeling, 2014(31):49-77.
[17]朱波,卢露.中国逆周期缓冲资本调整指标研究——基于金融体系脆弱时期的实证分析[J].国际金融研究,2013(10):86-96.
[2]胡东婉,朱安琪.商业银行非利息收入结构化差异与经营绩效关系研究——基于35家上市银行实证数据[J].经济学家,2018(6):82-87.
[3]李菲雅.国内外上市商业银行中间业务对比研究——兼议我国上市商业银行拓展中间业务的有效对策[J].四川师范大学学报(社会科学版),2016, 43(5):76-82.
[4]朱波,马永谈.行业特征、货币政策与系统性风险——基于“经济金融”关联网络的分析[J].国际金融研究,2018(4):22-32.
[5]黄苑,卢露,陈源.股市冲击压力情景下银行系统性风险演化机制分析[J].财经科学,2017(4):14-27.
[6]Christiansen C., and Ranaldo A. Extreme Coexceedances in New EU Member States'Stock Markets[J]. Journal of Banking and Finance, 2009, 33(6):1048-1057.
[7]Jiang C. Does Tail Dependence Make A Difference in the Estimation of Systemic Risk?CoVaR and MES[R].Working Paper, 2012.
[8]Adrian T. and Brunnermeier M. K. CoVaR. American Economic Review, 2016, 106:1705-1741.
[9]朱波,马永谈,陈德然.债务融资方式对行业金融风险溢出效应的作用机制[J].财经科学,2018(1):15-27.
[10]范小云,王道平,刘澜飚.规模、关联性与中国系统重要性银行的衡量[J].金融研究,2012(11):16-30.
[11]欧阳红兵,刘晓东.中国金融机构的系统重要性及系统性风险传染机制分析——基于复杂网络的视角[J].中国管理科学,2015, 23(10):30-37.
[12]邓向荣,曹红.系统性风险、网络传染与金融机构系统重要性评估[J].中央财经大学学报,2016(3):52-60.
[13]程凤朝,崔宏.理性看待商业银行中间业务收入占比[J].中国金融,2011(17):51-52.
[14]张国海,高怿.商业银行中间业务的国际比较与发展战略[J].金融研究,2003(8):129-134.
[15]陈胡青.我国商业银行中间业务发展研究——基于2010-2011年我国14家商业银行中间业务收入的分析[J].金融与经济,2012(6):74-77.
[16]MainikG and SchaanningE. On Dependence Consistency of CoVaR and Some Other Systemic Risk Measures.Statistics&Risk Modeling, 2014(31):49-77.
[17]朱波,卢露.中国逆周期缓冲资本调整指标研究——基于金融体系脆弱时期的实证分析[J].国际金融研究,2013(10):86-96.