商业银行流动性风险的溢出效应——基于动态CoVaR的方法
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摘要
本文尝试突破传统的研究思路,提出了"商业银行流动性风险的根源在于资产负债的结构性错配,而不仅限于期限错配",将流动性细分为资产的市场流动性和负债的融资流动性,以此去度量商业银行在时间维度下的流动性风险。接着,本文将构建的流动性指标嵌入分位数回归法中,优化了对CoVaR的估计方法,度量了商业银行流动性风险在空间维度的溢出效应,动态地刻画了银行间流动性风险的联动关系。经过测算,本文得到三个结论:第一,股份制商业银行对银行系统的流动性风险的溢出效应最强,体现为冲击力度大和波及范围广两个方面,同时,股份制商业银行自身也承受了最强的风险溢出。第二,股份制商业银行内部之间的风险溢出效应最强,远甚于其他不同类银行间的风险溢出效应。第三,资产规模对商业银行流动性风险的影响具有不确定性,同时,流动性风险的溢出效应还与资产负债的结构组成有着潜在的关联。
This paper breaks through the traditional research ideas and puts forward that "the root of liquidity risk of commercial banks lies in the structural mismatch of assets and liabilities, rather than the mismatch of maturity", which is to measure the liquidity risk of commercial banks under the time dimension by subdividing liquidity into the market liquidity of assets and the financing liquidity of liabilities. Then, this paper embeds the liquidity index embedded in CoVaR, optimizes the estimation method based on the decimal number regression, measures the spillover effect of the liquidity risk of commercial banks in the spatial dimension, and dynamically depicts the linkage relationship between the liquidity risk between banks. In this paper, three conclusions are obtained: first, the spillover effect of joint-stock commercial banks on the liquidity risk of the banking system is the strongest, which is embodied in two aspects: large impact intensity and wide spread range, at the same time, joint-stock commercial banks themselves have suffered the strongest risk overflow. Second, the risk spillover effect between joint-stock commercial banks is the strongest, far more than the spillover effect between other different banks. Third, the impact of asset size on the liquidity risk of commercial banks is uncertain, and the spillover effect of liquidity risk is also potentially related to the structural composition of assets and liabilities.
This paper breaks through the traditional research ideas and puts forward that "the root of liquidity risk of commercial banks lies in the structural mismatch of assets and liabilities, rather than the mismatch of maturity", which is to measure the liquidity risk of commercial banks under the time dimension by subdividing liquidity into the market liquidity of assets and the financing liquidity of liabilities. Then, this paper embeds the liquidity index embedded in CoVaR, optimizes the estimation method based on the decimal number regression, measures the spillover effect of the liquidity risk of commercial banks in the spatial dimension, and dynamically depicts the linkage relationship between the liquidity risk between banks. In this paper, three conclusions are obtained: first, the spillover effect of joint-stock commercial banks on the liquidity risk of the banking system is the strongest, which is embodied in two aspects: large impact intensity and wide spread range, at the same time, joint-stock commercial banks themselves have suffered the strongest risk overflow. Second, the risk spillover effect between joint-stock commercial banks is the strongest, far more than the spillover effect between other different banks. Third, the impact of asset size on the liquidity risk of commercial banks is uncertain, and the spillover effect of liquidity risk is also potentially related to the structural composition of assets and liabilities.
引文
[1]BCBS,“Sound Practices for Managing Liquidity in Banking Organizations”,Rules Text,2008a.
[2]巴曙松,尚航飞,朱元倩.巴塞尔协议Ⅲ流动性监管新规及其影响[J].南方金融,2013(5):35-39.
[3]中国银监会.商业银行流动性风险管理办法(试行),2015年第9号,2015.
[4]中国银监会.商业银行流动性风险管理指引,银监发[2009]87号,2009.
[5]Driga,L.and Socol,A.“Liquidity Risk Management in Banking”,Revista Tinerilor Economisti,2009,Iss.1:46-55.
[6]王晓婷.中国银行业系统流动性风险研究--形成机制、度量与管理[D].太原:山西财经大学,2017.
[7]彭建刚,谭亚平.净稳定资金比例在我国银行业应用的思考[J].武汉金融,2016(5):12-15.
[8]Banerjee,R.,N.“Banking Sector Liquidity Mismatch and the Financial Crisis”,Bank of England Working Papers,2012.
[9]Nikolaidi,M.“Bank Liquidity and Macroeconomic Fragility:Empirical Evidence for the EMU”,Paper prepared for the 18th Conference of the Research Network Macroeconomics and Macroeconomic Policies(FMM),2014.
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[11]Angora and Roulet,C.“Transformation Risk and Its Determinants:A New Approach Based on Basel IIILiquidity Management Framework”,SSRN Working Papers,2011.
[12]高国华,潘英丽.银行系统性风险度量--基于动态CoVaR方法的分析[J].上海交通大学学报,2011(12):1753-1759.
[13]Fur fine C.Interbank exposures:Quantifying the riskof contagion[J].Journal of Money Credit and Banking,2003(35):111-128.
[14]Wells S.Financial interlinkage s in the united kindom's inte rbank market and the risk of contagion[EB/OL][2011-01-05].
[15]王晓枫,廖凯亮,徐金池.复杂网络视角下银行同业间市场风险传染效应研究[J].经济学动态,2015(3):71-81.
[16]Adrian T and Shin H.S.,“Liquidity and Leverage”[J].Journal of Financial Intermediation,2010,19,Iss.3:418-437.
[17]郭卫东,中国上市银行的系统性风险价值及溢出--基于CoVaR方法的实证分析[J].北京工商大学学报,2013(4):32-38.
[18]吴卫星,邵旭方,吴锟.中国商业银行流动性风险传染特征分析--基于商业银行同业负债的时间序列数据[J].国际商务,2016(4):81-92.
[19]王薇,张勇,王运玺.基于动态CoVaR方法的银行系统性风险测度与金融监管问题研究[J].金融理论与实践,2018(12):47-54.
[20]李丛文.中国影子银行与货币政策调控--基于时变Copula动态相关性分析[J].南开经济研究,2015(5):40-58.
[21]肖璞.后危机时代中国有效金融监管问题研究[D].长沙:湖南大学,2013.
[22]范小云,方意,王道平.我国银行系统性风险的动态特征及系统重要性银行甄别--基于CCA与DAG相结合的分析[J].金融研究,2013(11):82-95.
[23]许争.宏观审慎框架下中国商业银行流动性风险研究[D].北京:对外经贸大学大学,2017.
[24]彭建刚,董景文.推动银行业宏观审慎管理与微观审慎管理的协调创新[J].银行家,2014(6):39-41.
[2]巴曙松,尚航飞,朱元倩.巴塞尔协议Ⅲ流动性监管新规及其影响[J].南方金融,2013(5):35-39.
[3]中国银监会.商业银行流动性风险管理办法(试行),2015年第9号,2015.
[4]中国银监会.商业银行流动性风险管理指引,银监发[2009]87号,2009.
[5]Driga,L.and Socol,A.“Liquidity Risk Management in Banking”,Revista Tinerilor Economisti,2009,Iss.1:46-55.
[6]王晓婷.中国银行业系统流动性风险研究--形成机制、度量与管理[D].太原:山西财经大学,2017.
[7]彭建刚,谭亚平.净稳定资金比例在我国银行业应用的思考[J].武汉金融,2016(5):12-15.
[8]Banerjee,R.,N.“Banking Sector Liquidity Mismatch and the Financial Crisis”,Bank of England Working Papers,2012.
[9]Nikolaidi,M.“Bank Liquidity and Macroeconomic Fragility:Empirical Evidence for the EMU”,Paper prepared for the 18th Conference of the Research Network Macroeconomics and Macroeconomic Policies(FMM),2014.
[10]Bai J.,Krishnamurthy,A.and Weymuller C.,H.“Measuring Liquidity Mismatch in the Banking Sector”[J].The Journal of Finance,,2018,73,Iss1:51-93.
[11]Angora and Roulet,C.“Transformation Risk and Its Determinants:A New Approach Based on Basel IIILiquidity Management Framework”,SSRN Working Papers,2011.
[12]高国华,潘英丽.银行系统性风险度量--基于动态CoVaR方法的分析[J].上海交通大学学报,2011(12):1753-1759.
[13]Fur fine C.Interbank exposures:Quantifying the riskof contagion[J].Journal of Money Credit and Banking,2003(35):111-128.
[14]Wells S.Financial interlinkage s in the united kindom's inte rbank market and the risk of contagion[EB/OL][2011-01-05].
[15]王晓枫,廖凯亮,徐金池.复杂网络视角下银行同业间市场风险传染效应研究[J].经济学动态,2015(3):71-81.
[16]Adrian T and Shin H.S.,“Liquidity and Leverage”[J].Journal of Financial Intermediation,2010,19,Iss.3:418-437.
[17]郭卫东,中国上市银行的系统性风险价值及溢出--基于CoVaR方法的实证分析[J].北京工商大学学报,2013(4):32-38.
[18]吴卫星,邵旭方,吴锟.中国商业银行流动性风险传染特征分析--基于商业银行同业负债的时间序列数据[J].国际商务,2016(4):81-92.
[19]王薇,张勇,王运玺.基于动态CoVaR方法的银行系统性风险测度与金融监管问题研究[J].金融理论与实践,2018(12):47-54.
[20]李丛文.中国影子银行与货币政策调控--基于时变Copula动态相关性分析[J].南开经济研究,2015(5):40-58.
[21]肖璞.后危机时代中国有效金融监管问题研究[D].长沙:湖南大学,2013.
[22]范小云,方意,王道平.我国银行系统性风险的动态特征及系统重要性银行甄别--基于CCA与DAG相结合的分析[J].金融研究,2013(11):82-95.
[23]许争.宏观审慎框架下中国商业银行流动性风险研究[D].北京:对外经贸大学大学,2017.
[24]彭建刚,董景文.推动银行业宏观审慎管理与微观审慎管理的协调创新[J].银行家,2014(6):39-41.