金融机构系统性风险:重要性与脆弱性
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摘要
金融机构的系统性风险包括风险贡献与风险敞口两个方面,前者反映其系统重要性,后者反映其系统脆弱性,两者不可偏废其一。文章基于CoVaR的统一框架,首次采用ΔCoVaR和Exposure-ΔCoVaR方法,对我国金融机构的系统性风险进行全面度量,评估其系统重要性与脆弱性。研究发现,我国银行和保险部门的系统重要性高于证券部门,证券部门的系统脆弱性则高于银行和保险部门,而且这种部门间差异在时间维度上持续存在。四家大型商业银行的系统重要性较高而系统脆弱性较低,少数金融机构则同时具有较高的系统重要性与脆弱性。此外,资产规模和杠杆率分别是机构系统重要性与脆弱性的重要影响因素,证券公司的融资融券规模对其系统脆弱性有显著的正向影响,但对其系统重要性的影响不显著。文章的研究对于我国防范系统性风险、维护金融安全稳定具有重要的指导意义。
Since the outbreak of the global financial crisis,forestalling and defusing systemic financial risks has been a hot topic of social concerns. In China,with constant development and innovation of the financial system,higher level financial deepening and openness,and economic downside pressure under "new normal" economy,risk-prevention becomes much more complicated. In this case,the financial system should better serve the real economy,reduce financial risks and deepen financial reforms—three tasks of China's financial work. The report of the 19th National Congress of the Communist Party of China further emphasized that the government should improve the financial regulatory system to forestall systemic financial risks. Therefore,ensuring China's financial stability and preventing systemic risks have become the priority and major challenges for China's financial regulatory authorities. Accurate measurement of systemic risks is the basis for risk prevention,the improvement of financial regulations,and any effective regulatory actions. However,existing domestic studies measure financial institutions' systemic risks from only one aspect—systemic risk contribution or systemic risk exposure,and lack a clear distinction between the two measures in theoretical and policy implications. Some scholars even use systemic risk exposure metrics to measure the systemic risk contribution of financial institutions and assess its systemic importance. Actually,the aggregate risks of financial institutions include both risk contribution and risk exposure—the former focuses on systemic importance while the latter underlines systemic vulnerability,so we should take both sides into risk measurement. This paper uses ΔCoVaR and Exposure-ΔCoVaR to comprehensively measure the systemic risks of financial institutions from both sides—systemic importance and systemic vulnerability. This paper finds no significant correlation between the systemic importance and vulnerability of financial institutions in the cross-sectional dimension,but significant correlation in the time-series dimension,which means the systemic importance and vulnerability of financial institutions change simultaneously and periodically. The results imply that,in China,the systemic importance of bank and insurance industry exceed that of securities industry,while the latter's systemic vulnerability exceeds that of the former. These differences exist persistently in the time-series dimension. The "big four" banks have high systemic importance but low systemic vulnerability,while a handful of financial institutions have both significantly high systemic importance and vulnerability. Furthermore,the size of financial institutions' asset is an important influencing factor of systemic importance,and the leverage is an important influencing factor of systemic vulnerability,while the margin trading of securities has a significant positive effect on systemic vulnerability but no significant effect on systemic importance. This paper accurately measures the systemic risks of 33 listed financial institutions in China from two aspects—risk contribution and risk exposure,and makes a precise assessment on their systemic importance and vulnerability. We also investigate the influencing factors of financial institutions' systemic importance and vulnerability. These findings help to understand the systemic risks of China's financial institutions in crosssectional and time-series dimensions and correct some wrong perceptions in existing academic studies,and further provide useful empirical references and policy suggestions to China's financial regulatory authorities to forestall systemic risks and improve macro-regulation. The policy implications of the results are mainly reflected in the following three aspects. First,regulators need to select targeted regulatory objectives and policy tools to make differential regulations based on the features of institutions in systemic importance and vulnerability.Second,different institutions are different in systemic importance and vulnerability,so regulatory authorities should pick out key financial institutions through their performance in systemic importance and vulnerability,and enhance the supervision of key institutions. Third,financial regulators are able to choose proper and effective regulatory tools according to the main drivers of systemic importance and vulnerability.
Since the outbreak of the global financial crisis,forestalling and defusing systemic financial risks has been a hot topic of social concerns. In China,with constant development and innovation of the financial system,higher level financial deepening and openness,and economic downside pressure under "new normal" economy,risk-prevention becomes much more complicated. In this case,the financial system should better serve the real economy,reduce financial risks and deepen financial reforms—three tasks of China's financial work. The report of the 19th National Congress of the Communist Party of China further emphasized that the government should improve the financial regulatory system to forestall systemic financial risks. Therefore,ensuring China's financial stability and preventing systemic risks have become the priority and major challenges for China's financial regulatory authorities. Accurate measurement of systemic risks is the basis for risk prevention,the improvement of financial regulations,and any effective regulatory actions. However,existing domestic studies measure financial institutions' systemic risks from only one aspect—systemic risk contribution or systemic risk exposure,and lack a clear distinction between the two measures in theoretical and policy implications. Some scholars even use systemic risk exposure metrics to measure the systemic risk contribution of financial institutions and assess its systemic importance. Actually,the aggregate risks of financial institutions include both risk contribution and risk exposure—the former focuses on systemic importance while the latter underlines systemic vulnerability,so we should take both sides into risk measurement. This paper uses ΔCoVaR and Exposure-ΔCoVaR to comprehensively measure the systemic risks of financial institutions from both sides—systemic importance and systemic vulnerability. This paper finds no significant correlation between the systemic importance and vulnerability of financial institutions in the cross-sectional dimension,but significant correlation in the time-series dimension,which means the systemic importance and vulnerability of financial institutions change simultaneously and periodically. The results imply that,in China,the systemic importance of bank and insurance industry exceed that of securities industry,while the latter's systemic vulnerability exceeds that of the former. These differences exist persistently in the time-series dimension. The "big four" banks have high systemic importance but low systemic vulnerability,while a handful of financial institutions have both significantly high systemic importance and vulnerability. Furthermore,the size of financial institutions' asset is an important influencing factor of systemic importance,and the leverage is an important influencing factor of systemic vulnerability,while the margin trading of securities has a significant positive effect on systemic vulnerability but no significant effect on systemic importance. This paper accurately measures the systemic risks of 33 listed financial institutions in China from two aspects—risk contribution and risk exposure,and makes a precise assessment on their systemic importance and vulnerability. We also investigate the influencing factors of financial institutions' systemic importance and vulnerability. These findings help to understand the systemic risks of China's financial institutions in crosssectional and time-series dimensions and correct some wrong perceptions in existing academic studies,and further provide useful empirical references and policy suggestions to China's financial regulatory authorities to forestall systemic risks and improve macro-regulation. The policy implications of the results are mainly reflected in the following three aspects. First,regulators need to select targeted regulatory objectives and policy tools to make differential regulations based on the features of institutions in systemic importance and vulnerability.Second,different institutions are different in systemic importance and vulnerability,so regulatory authorities should pick out key financial institutions through their performance in systemic importance and vulnerability,and enhance the supervision of key institutions. Third,financial regulators are able to choose proper and effective regulatory tools according to the main drivers of systemic importance and vulnerability.
引文
[1]卜林,李政.我国上市金融机构系统性风险溢出研究--基于CoVaR和MES的比较分析[J].当代财经,2015,(6):55-65.
[2]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.
[3]方意.中国银行业系统性风险研究--宏观审慎视角下的三个压力测试[J].经济理论与经济管理,2017,(2):48-66.
[4]方意,赵胜民,王道平.我国金融机构系统性风险测度--基于DCC-GARCH模型的研究[J].金融监管研究,2012,(11):26-42.
[5]李政,梁琪,涂晓枫.我国上市金融机构关联性研究--基于网络分析法[J].金融研究,2016,(8):95-110.
[6]李志辉,樊莉.中国商业银行系统性风险溢价实证研究[J].当代经济科学,2011,(6):13-20.
[7]李志辉,李源,李政.中国银行业系统性风险监测研究--基于SCCA技术的实现与优化[J].金融研究,2016,(3):92-106.
[8]廉永辉.同业网络中的风险传染--基于中国银行业的实证研究[J].财经研究,2016,(9):63-74.
[9]梁琪,李政.系统重要性、审慎工具与我国银行业监管[J].金融研究,2014,(8):32-46.
[10]梁琪,李政,郝项超.我国系统重要性金融机构的识别与监管--基于系统性风险指数SRISK方法的分析[J].金融研究,2013,(9):56-70.
[11]梁琪,余峰燕.金融危机、国有股权与资本投资[J].经济研究,2014,(4):47-61.
[12]田娇,王擎.银行资本约束、银行风险外溢与宏观金融风险[J].财贸经济,2015,(8):74-90.
[13]许友传.金融体系的结构脆弱性及其系统性风险[J].复旦学报(社会科学版),2018,(4):129-141.
[14]杨子晖,李东承.我国银行系统性金融风险研究--基于“去一法”的应用分析[J].经济研究,2018,(8):36-51.
[15]赵进文,张胜保,韦文彬.系统性金融风险度量方法的比较与应用[J].统计研究,2013,(10):46-53.
[16]周天芸,周开国,黄亮.机构集聚、风险传染与香港银行的系统性风险[J].国际金融研究,2012,(4):77-87.
[17]Acharya V V,Pedersen L H,Philippon T,et al.Measuring systemic risk[J].The Review of Financial Studies,2017,30(1):2-47.
[18]Adrian T,Brunnermeier M K.CoVaR[J].American Economic Review,2016,106(7):1705-1741.
[19]Benoit S,Colliard J E,Hurlin C,et al.Where the risks lie:A survey on systemic risk[J].Review of Finance,2017,21(1):109-152.
[20]Brownlees C,Engle R F.SRISK:A conditional capital shortfall measure of systemic risk[J].The Review of Financial Studies,2017,30(1):48-79.
[21]Ellis L,Haldane A,Moshirian F.Systemic risk,governance and global financial stability[J].Journal of Banking and Finance,2014,45:175-181.
[22]Huang X,Zhou H,Zhu H B.A framework for assessing the systemic risk of major financial institutions[J].Journal of Banking and Finance,2009,33(11):2036-2049.
[23]Huang X,Zhou H,Zhu H B.Systemic risk contributions[J].Journal of Financial Services Research,2012,42(1-2):55-83.
[24]López-Espinosa G,Moreno A,Rubia A,et al.Short-term wholesale funding and systemic risk:A global CoVaRapproach[J].Journal of Banking and Finance,2012,36(12):3150-3162.
[25]Pagano M S,Sedunov J.A comprehensive approach to measuring the relation between systemic risk exposure and sovereign debt[J].Journal of Financial Stability,2016,23:62-78.
(1)限于篇幅,这里仅对尾部依赖方法进行评述,系统未定权益分析SCCA等联合违约方法(李志辉等,2016)、Granger因果网络等关联性度量方法(李政等,2016)、基于金融机构财务数据的网络模型(范小云等,2012;廉永辉,2016)以及“去一法”(杨子晖和李东承,2018)等结构化方法也得到国内外学者的关注。
(1)本文的金融机构系统重要性与脆弱性是基于金融市场数据度量评估的,受到市场噪音等多种因素的干扰。与均值相比,中值受极端值的影响相对较小。因此,本文系统重要性与脆弱性的排名采用指标的中值。
(1)限于篇幅,正文中未列出33家金融机构每一年ΔCoVaR和Exposure-ΔCoVaR的中值及其排名。
(1)本文将ΔCoVaR和Exposure-ΔCoVaR由周频转化为季频,作为被解释变量。在解释变量中,规模以总资产取自然对数度量,杠杆率以负债除以总资产度量,融资融券以证券公司每个季度末融资融券余额除以资产度量。期限错配采用Adrian和Brunnermeier(2016)的度量方法,以资产除以(短期负债-现金)测度,该指标值越小,表明期限错配越严重;由于存款准备金制度,银行的现金以现金及存放于中央银行的款项度量,保险和证券公司的现金以货币资金度量。
[2]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.
[3]方意.中国银行业系统性风险研究--宏观审慎视角下的三个压力测试[J].经济理论与经济管理,2017,(2):48-66.
[4]方意,赵胜民,王道平.我国金融机构系统性风险测度--基于DCC-GARCH模型的研究[J].金融监管研究,2012,(11):26-42.
[5]李政,梁琪,涂晓枫.我国上市金融机构关联性研究--基于网络分析法[J].金融研究,2016,(8):95-110.
[6]李志辉,樊莉.中国商业银行系统性风险溢价实证研究[J].当代经济科学,2011,(6):13-20.
[7]李志辉,李源,李政.中国银行业系统性风险监测研究--基于SCCA技术的实现与优化[J].金融研究,2016,(3):92-106.
[8]廉永辉.同业网络中的风险传染--基于中国银行业的实证研究[J].财经研究,2016,(9):63-74.
[9]梁琪,李政.系统重要性、审慎工具与我国银行业监管[J].金融研究,2014,(8):32-46.
[10]梁琪,李政,郝项超.我国系统重要性金融机构的识别与监管--基于系统性风险指数SRISK方法的分析[J].金融研究,2013,(9):56-70.
[11]梁琪,余峰燕.金融危机、国有股权与资本投资[J].经济研究,2014,(4):47-61.
[12]田娇,王擎.银行资本约束、银行风险外溢与宏观金融风险[J].财贸经济,2015,(8):74-90.
[13]许友传.金融体系的结构脆弱性及其系统性风险[J].复旦学报(社会科学版),2018,(4):129-141.
[14]杨子晖,李东承.我国银行系统性金融风险研究--基于“去一法”的应用分析[J].经济研究,2018,(8):36-51.
[15]赵进文,张胜保,韦文彬.系统性金融风险度量方法的比较与应用[J].统计研究,2013,(10):46-53.
[16]周天芸,周开国,黄亮.机构集聚、风险传染与香港银行的系统性风险[J].国际金融研究,2012,(4):77-87.
[17]Acharya V V,Pedersen L H,Philippon T,et al.Measuring systemic risk[J].The Review of Financial Studies,2017,30(1):2-47.
[18]Adrian T,Brunnermeier M K.CoVaR[J].American Economic Review,2016,106(7):1705-1741.
[19]Benoit S,Colliard J E,Hurlin C,et al.Where the risks lie:A survey on systemic risk[J].Review of Finance,2017,21(1):109-152.
[20]Brownlees C,Engle R F.SRISK:A conditional capital shortfall measure of systemic risk[J].The Review of Financial Studies,2017,30(1):48-79.
[21]Ellis L,Haldane A,Moshirian F.Systemic risk,governance and global financial stability[J].Journal of Banking and Finance,2014,45:175-181.
[22]Huang X,Zhou H,Zhu H B.A framework for assessing the systemic risk of major financial institutions[J].Journal of Banking and Finance,2009,33(11):2036-2049.
[23]Huang X,Zhou H,Zhu H B.Systemic risk contributions[J].Journal of Financial Services Research,2012,42(1-2):55-83.
[24]López-Espinosa G,Moreno A,Rubia A,et al.Short-term wholesale funding and systemic risk:A global CoVaRapproach[J].Journal of Banking and Finance,2012,36(12):3150-3162.
[25]Pagano M S,Sedunov J.A comprehensive approach to measuring the relation between systemic risk exposure and sovereign debt[J].Journal of Financial Stability,2016,23:62-78.
(1)限于篇幅,这里仅对尾部依赖方法进行评述,系统未定权益分析SCCA等联合违约方法(李志辉等,2016)、Granger因果网络等关联性度量方法(李政等,2016)、基于金融机构财务数据的网络模型(范小云等,2012;廉永辉,2016)以及“去一法”(杨子晖和李东承,2018)等结构化方法也得到国内外学者的关注。
(1)本文的金融机构系统重要性与脆弱性是基于金融市场数据度量评估的,受到市场噪音等多种因素的干扰。与均值相比,中值受极端值的影响相对较小。因此,本文系统重要性与脆弱性的排名采用指标的中值。
(1)限于篇幅,正文中未列出33家金融机构每一年ΔCoVaR和Exposure-ΔCoVaR的中值及其排名。
(1)本文将ΔCoVaR和Exposure-ΔCoVaR由周频转化为季频,作为被解释变量。在解释变量中,规模以总资产取自然对数度量,杠杆率以负债除以总资产度量,融资融券以证券公司每个季度末融资融券余额除以资产度量。期限错配采用Adrian和Brunnermeier(2016)的度量方法,以资产除以(短期负债-现金)测度,该指标值越小,表明期限错配越严重;由于存款准备金制度,银行的现金以现金及存放于中央银行的款项度量,保险和证券公司的现金以货币资金度量。