商业银行尾部风险网络关联性与系统性风险——基于中国上市银行的实证检验
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摘要
中国经济进入新常态之后,经济下行压力加大,有效防范系统性金融风险已成为当前经济发展中亟须面对的重大问题。基于此,本文利用中国上市银行2010年8月19日至2017年3月31日的股票交易数据,采用分位数回归和LASSO算法,构建了上市银行尾部风险网络,同时使用滚动时间窗口法,分析了网络的动态关联性和拓扑结构。在此基础上,实证检验了上市银行尾部风险网络的关联性对系统性风险的影响。结果表明,银行尾部风险网络关联性与系统性风险显著正相关。虽然个体银行的尾部风险溢出会降低自身的风险承担水平,但也显著增强了银行网络的关联性,从而提高了系统性风险的整体水平。同时我国上市银行尾部风险网络存在较明显的时变特征,在风险积聚过程和经济下行期间,其关联性显著增强。另外,大型国有银行在整个银行网络中居中心地位,具有较强的尾部风险溢出效应。
After China's economy entered a new normal,concerns about the systemic risk of Chinese financial system became increasingly prominent as the downward pressure on the economy intensified. Therefore,how to effectively guard against systemic financial risk has become a meaningful and urgent research topic. In this paper,we investigate the interconnectedness of Chinese banks' tail risk network and its effect on systematic risk under the new normal of China's economy. We employ quantile regression and LASSO algorithm on a real-time data of 16 listed Banks and macro state variables from August 19,2010 to March 31,2017. Our empirical study results suggest that the tail risk network of Chinese banks has obvious time-varying characteristics in the post-crisis era. The interconnectedness will significantly increase during the process of risk accumulation and crisis. Large state-owned banks have a central position in the banking network and have a strong tail risk spillover effect. Additionally,our panel regression analysis results reveal that the high level of tail risk spillover among all banks will lead to an increase in the systemic risk. The banks with larger out-degree centrality tend to reduce the systemic risk contribution but increase other banks' systemic risk.
After China's economy entered a new normal,concerns about the systemic risk of Chinese financial system became increasingly prominent as the downward pressure on the economy intensified. Therefore,how to effectively guard against systemic financial risk has become a meaningful and urgent research topic. In this paper,we investigate the interconnectedness of Chinese banks' tail risk network and its effect on systematic risk under the new normal of China's economy. We employ quantile regression and LASSO algorithm on a real-time data of 16 listed Banks and macro state variables from August 19,2010 to March 31,2017. Our empirical study results suggest that the tail risk network of Chinese banks has obvious time-varying characteristics in the post-crisis era. The interconnectedness will significantly increase during the process of risk accumulation and crisis. Large state-owned banks have a central position in the banking network and have a strong tail risk spillover effect. Additionally,our panel regression analysis results reveal that the high level of tail risk spillover among all banks will lead to an increase in the systemic risk. The banks with larger out-degree centrality tend to reduce the systemic risk contribution but increase other banks' systemic risk.
引文
1.黄聪、贾彦东:《金融网络视角下的宏观审慎管理——基于银行间支付结算数据的实证分析》,《金融研究》2010年第4期。
2 .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》,《金融研究》2016年第8期。
3 .梁琪、李政、郝项超:《中国股票市场国际化研究:基于信息溢出的视角》,《经济研究》2015年第4期。
4 .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》,《经济学动态》2015年第3期。
5 .陈守东、王妍:《我国金融机构的系统性金融风险评估——基于极端分位数回归技术的风险度量》,《中国管理科学》2014年第7期。
6 .严伟祥、张维、牛华伟:《金融风险动态相关与风险溢出异质性研究》,《财贸经济》2017年第10期。
7 .Abdymomunov,A.,Regime-Switching Measure of Systemic Financial Stress.Annals of Finance,Vol.9,No.3,2013,pp.455-470.
8 .Acharya,V.V.,Pedersen,L.H.,Philippon,T.,&Richardson,M.,Measuring Systemic Risk.The Review of Financial Studies,Vol.30,No.1,2017,pp.2-47.
9 .Adrian,T.,&Brunnermeier,M.K.,Co Va R.The American Economic Review,Vol.106,No.7,2016,pp.1705-1741.
10 .Allen,F.,&Gale,D.,Financial Contagion.Journal of Political Economy,Vol.108,No.1,2000,pp.1-33.
11 .Belloni,A.,&Chernozhukov,V.1-penalized Quantile Regression in High-dimensional Sparse Models.The Annals of Statistics,Vol.39,No.1,2011,pp.82-130.
12 .Billio,M.,Getmansky,M.,Lo,A.W.,&Pelizzon,L.,Econometric Measures of Connectedness and Systemic Risk in the Finance and Insurance Sectors.Journal of Financial Economics,Vol.104,No.3,2012,pp.535-559.
13 .Boss,M.,Elsinger,H.,Summer,M.,&Thurner,S.,Network Topology of the Interbank Market.Quantitative Finance,Vol.4,No.6,2004,pp.677-684.
14 .Brownlees,C.,&Engle,R.,SRISK:A Conditional Capital Shortfall Measure of Systemic Risk.Esrb Working Paper,Vol.30,No.1,2016,pp.48-79.
15 .Diebold,F.,&Yilmaz,K.,On the Network Topology of Variance Decompositions:Measuring the Connectedness of Financial Firms.Journal of Econometrics,Vol.182,No.1,2014,pp.119-134.
16 .Engle,R.,Dynamic Conditional Correlation:A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models.Journal of Business&Economic Statistics,Vol.20,No.3,2002,pp.339-350.
17 .Fagiolo,G.,Clustering in Complex Directed Networks.Physical Review E,Vol.76,No.2,2007,pp.1-8.
18 .Freixas,X.,Parigi,B.M.,&Rochet,J.C.,Systemic Risk,Interbank Relations,and Liquidity Provision by the Central Bank.Journal of Money,Credit and Banking,Vol.32,No.3,2000,pp.611-638.
19 .Fricke,D.,&Lux,T.,Core-Periphery Structure in the Overnight Money Market:Evidence from the E-mid Trading Platform.Computational Economics,Vol.45,No.3,2012,pp.359-395.
20 .Glosten,L.R.,Jagannathan,R.,&Runkle,D.E.,On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks.The Fournal of Finance,Vol.48,No.5,1993,pp.1779-1801.
21 .Hrdle,W.K.,Wang,W.,&Yu,L.,Tenet:Tail-Event Driven Network Risk.Journal of Econometrics,Vol.192,No.2,2016,pp.499-513.
22 .Hasman,A.,A Critical Review of Contagion Risk in Banking.Journal of Economic Surveys,Vol.27,No.5,2013,pp.978-995.
23 .Hautsch,N.,Schaumburg,J.,&Schienle,M.,Financial Network Systemic Risk Contributions.Review of Finance,Vol.19,No.2,2015,pp.685-738.
24 .Huang,W.Q.,Zhuang,X.T.,Yao,S.,&Uryasev,S.,A Financial Network Perspective of Financial Institutions'Systemic Risk Contributions.Physica a:Statistical Mechanics&Its Applications,Vol.456,2016,pp.183-196.
25 .Koenker,R.,&Bassett,G.J.,Regression Quantiles.Econometrica,Vol.46,No.1,1978,pp.33-50.
26 .Laeven,L.,Ratnovski,L.,&Tong,H.,Bank Size,Capital,and Systemic Risk:Some International Evidence.Journal of Banking&Finance,Vol.69,2016,S25-S34.
27 .Li,Y.,&Zhu J.L1-norm Quantile Regression.Journal of Computational and Graphical Statistics,Vol.17,No.1,2008,pp.163-185.
28 .Nier,E.,Yang,J.,Yorulmazer,T.,&Alentorn,A.,Network Models and Financial Stability.Journal of Economic Dynamics and Control,Vol.31,No.6,2007,pp.2033-2060.
29 .Papana,A.,Kyrtsou,C.,Kugiumtzis,D.,&Diks,C.,Financial Networks Based on Granger Causality:A Case Study.Physica A:Statistical Mechanics and its Applications,Vol.482,No.15,2017,pp.65-73.
30 .Scaillet,O.,Nonparametric Estimation and Sensitivity Analysis of Expected Shortfall.Mathematical Finance,Vol.14,No.1,2004,pp.115-129.
31 .Tibshirani,R.,Regression Shrinkage and Selection Via the Lasso.Journal of the Royal Statistical Society.Vol.58,No.1,1996,pp.267-288.
(1)所谓幂律分布是指少数节点拥有大量的连接,而大多数节点只拥有少数连接。
(2)作者在2008年美联储工作论文中首次提出这一模型,并得到大量引用,但直到2016年才正式发表。
(1)分位数回归方法最早由Koenker和Bassett(1978)提出,可以对不同分为点的因变量进行估计,从而可以更好地识别因变量在极值状态下的影响,并且契合金融时间序列尖峰、厚尾的特征。
(1)农业银行和光大银行分别于2010年7月和2010年8月18日在A股上市。
(2)由篇幅所限,没有列示16家上市银行对数收益率与宏观状态变量的描述性统计,单位根检验结果表明所有变量均是平稳的时间序列,有兴趣的读者可向作者索要结果。
(3)滚动样本的选择同时考虑了极端分位数估计样本的有效性和后续实证分析的需要。
(1)受篇幅所限,这里仅给出4个年份的关联网络图,对其他年份网络关联图感兴趣的读者可向作者索取。
(1)具体为,E(ψ∑T-εt=∑Tt=11Kmt(ψ-εK mth)vmtt-1(εmt|εmt<ψ)=h)εmtt,∑T(|εψ-εmt<ψ)=t=1K mt∑Th),Et-1(vmt(ψ-εmtt=1Kh),其中ψ=Va Rτmt/σm K(x)=∫x/hkk-!(μ)dμ,(μ)为核函数,h=T-1/5。
2 .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》,《金融研究》2016年第8期。
3 .梁琪、李政、郝项超:《中国股票市场国际化研究:基于信息溢出的视角》,《经济研究》2015年第4期。
4 .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》,《经济学动态》2015年第3期。
5 .陈守东、王妍:《我国金融机构的系统性金融风险评估——基于极端分位数回归技术的风险度量》,《中国管理科学》2014年第7期。
6 .严伟祥、张维、牛华伟:《金融风险动态相关与风险溢出异质性研究》,《财贸经济》2017年第10期。
7 .Abdymomunov,A.,Regime-Switching Measure of Systemic Financial Stress.Annals of Finance,Vol.9,No.3,2013,pp.455-470.
8 .Acharya,V.V.,Pedersen,L.H.,Philippon,T.,&Richardson,M.,Measuring Systemic Risk.The Review of Financial Studies,Vol.30,No.1,2017,pp.2-47.
9 .Adrian,T.,&Brunnermeier,M.K.,Co Va R.The American Economic Review,Vol.106,No.7,2016,pp.1705-1741.
10 .Allen,F.,&Gale,D.,Financial Contagion.Journal of Political Economy,Vol.108,No.1,2000,pp.1-33.
11 .Belloni,A.,&Chernozhukov,V.1-penalized Quantile Regression in High-dimensional Sparse Models.The Annals of Statistics,Vol.39,No.1,2011,pp.82-130.
12 .Billio,M.,Getmansky,M.,Lo,A.W.,&Pelizzon,L.,Econometric Measures of Connectedness and Systemic Risk in the Finance and Insurance Sectors.Journal of Financial Economics,Vol.104,No.3,2012,pp.535-559.
13 .Boss,M.,Elsinger,H.,Summer,M.,&Thurner,S.,Network Topology of the Interbank Market.Quantitative Finance,Vol.4,No.6,2004,pp.677-684.
14 .Brownlees,C.,&Engle,R.,SRISK:A Conditional Capital Shortfall Measure of Systemic Risk.Esrb Working Paper,Vol.30,No.1,2016,pp.48-79.
15 .Diebold,F.,&Yilmaz,K.,On the Network Topology of Variance Decompositions:Measuring the Connectedness of Financial Firms.Journal of Econometrics,Vol.182,No.1,2014,pp.119-134.
16 .Engle,R.,Dynamic Conditional Correlation:A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models.Journal of Business&Economic Statistics,Vol.20,No.3,2002,pp.339-350.
17 .Fagiolo,G.,Clustering in Complex Directed Networks.Physical Review E,Vol.76,No.2,2007,pp.1-8.
18 .Freixas,X.,Parigi,B.M.,&Rochet,J.C.,Systemic Risk,Interbank Relations,and Liquidity Provision by the Central Bank.Journal of Money,Credit and Banking,Vol.32,No.3,2000,pp.611-638.
19 .Fricke,D.,&Lux,T.,Core-Periphery Structure in the Overnight Money Market:Evidence from the E-mid Trading Platform.Computational Economics,Vol.45,No.3,2012,pp.359-395.
20 .Glosten,L.R.,Jagannathan,R.,&Runkle,D.E.,On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks.The Fournal of Finance,Vol.48,No.5,1993,pp.1779-1801.
21 .Hrdle,W.K.,Wang,W.,&Yu,L.,Tenet:Tail-Event Driven Network Risk.Journal of Econometrics,Vol.192,No.2,2016,pp.499-513.
22 .Hasman,A.,A Critical Review of Contagion Risk in Banking.Journal of Economic Surveys,Vol.27,No.5,2013,pp.978-995.
23 .Hautsch,N.,Schaumburg,J.,&Schienle,M.,Financial Network Systemic Risk Contributions.Review of Finance,Vol.19,No.2,2015,pp.685-738.
24 .Huang,W.Q.,Zhuang,X.T.,Yao,S.,&Uryasev,S.,A Financial Network Perspective of Financial Institutions'Systemic Risk Contributions.Physica a:Statistical Mechanics&Its Applications,Vol.456,2016,pp.183-196.
25 .Koenker,R.,&Bassett,G.J.,Regression Quantiles.Econometrica,Vol.46,No.1,1978,pp.33-50.
26 .Laeven,L.,Ratnovski,L.,&Tong,H.,Bank Size,Capital,and Systemic Risk:Some International Evidence.Journal of Banking&Finance,Vol.69,2016,S25-S34.
27 .Li,Y.,&Zhu J.L1-norm Quantile Regression.Journal of Computational and Graphical Statistics,Vol.17,No.1,2008,pp.163-185.
28 .Nier,E.,Yang,J.,Yorulmazer,T.,&Alentorn,A.,Network Models and Financial Stability.Journal of Economic Dynamics and Control,Vol.31,No.6,2007,pp.2033-2060.
29 .Papana,A.,Kyrtsou,C.,Kugiumtzis,D.,&Diks,C.,Financial Networks Based on Granger Causality:A Case Study.Physica A:Statistical Mechanics and its Applications,Vol.482,No.15,2017,pp.65-73.
30 .Scaillet,O.,Nonparametric Estimation and Sensitivity Analysis of Expected Shortfall.Mathematical Finance,Vol.14,No.1,2004,pp.115-129.
31 .Tibshirani,R.,Regression Shrinkage and Selection Via the Lasso.Journal of the Royal Statistical Society.Vol.58,No.1,1996,pp.267-288.
(1)所谓幂律分布是指少数节点拥有大量的连接,而大多数节点只拥有少数连接。
(2)作者在2008年美联储工作论文中首次提出这一模型,并得到大量引用,但直到2016年才正式发表。
(1)分位数回归方法最早由Koenker和Bassett(1978)提出,可以对不同分为点的因变量进行估计,从而可以更好地识别因变量在极值状态下的影响,并且契合金融时间序列尖峰、厚尾的特征。
(1)农业银行和光大银行分别于2010年7月和2010年8月18日在A股上市。
(2)由篇幅所限,没有列示16家上市银行对数收益率与宏观状态变量的描述性统计,单位根检验结果表明所有变量均是平稳的时间序列,有兴趣的读者可向作者索要结果。
(3)滚动样本的选择同时考虑了极端分位数估计样本的有效性和后续实证分析的需要。
(1)受篇幅所限,这里仅给出4个年份的关联网络图,对其他年份网络关联图感兴趣的读者可向作者索取。
(1)具体为,E(ψ∑T-εt=∑Tt=11Kmt(ψ-εK mth)vmtt-1(εmt|εmt<ψ)=h)εmtt,∑T(|εψ-εmt<ψ)=t=1K mt∑Th),Et-1(vmt(ψ-εmtt=1Kh),其中ψ=Va Rτmt/σm K(x)=∫x/hkk-!(μ)dμ,(μ)为核函数,h=T-1/5。