同业业务、流动性波动与中央银行流动性管理
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摘要
本文将同业业务特征及其对银行资产负债行为的影响纳入到流动性分析框架,探索传统存贷业务监管约束强度、同业规模与流动性水平之间的联动关系,进而构建基于银行间市场的动态系统模型,模拟分析同业业务对流动性波动的影响,同时考察中央银行流动性管理的不同方式对平滑流动性波动的效果。研究结果表明:商业银行扩张传统信贷业务时受到的传统监管约束越大,发展同业的动机相应越强;在同业规模超过一定界限后,随着银行同业比重的提高,银行流动性风险会随之提高;银行同业行为导致资产与负债之间期限错配的扩大,加剧了其顺周期性特征,导致金融市场流动性的波动幅度和频率升高,弱化了央行原有流动性管理工具的执行效果;面对同业引致的货币市场流动性波动,中央银行采取以利率引导为主、非常规政策工具为辅的组合型流动性管理方式行之有效。
Liquidity management is a key component of monetary policy. In recent years,liquidity in China has become a departure point between the macro and micro periodicals. This phenomenon makes it difficult for the central bank to carry out monetary policy and to manage liquidity effectively. To determine the reason for this liquidity problem,the literature focuses on the micro factors of shadow banking and the effects of traditional monetary policy instruments without considering the role of interbank business. Interbank business is a special scale expansion channel of China's banking institution and impacts the effectiveness of unconventional liquidity management systematically. This framework is not sufficient for exploring the factors behind the liquidity problem in China.To improve our understanding of liquidity fluctuation,this paper theoretically analyzes the expansion mechanism of interbank business and its influence channel on liquidity fluctuation. By incorporating the characteristics of interbank business and its influences into a new liquidity analysis framework,this paper analyzes the transmission mechanism 's dependence on financial regulation,the scale of interbank business and the liquidity level. We then empirically examine the correlation between the intensity of financial regulation,the development of interbank businessand the risk of liquidity by using a panel regression analysis model. We develop a dynamic system model,which includes the "herd behavior"of the money market and liquidity management by the central bank. We use this model to simulate the influence of interbank business on liquidity fluctuation and to analyze the effect of different liquidity management methods the central bank uses.Based on the empirical analysis of 40 Chinese banks from 2010 to 2016( including state-owned,joint-stock commercial and local corporate commercial banks) and the dynamic system simulation of the central bank's liquidity management,we come to several conclusions. Tighter financial regulation gives commercial banks a strong desire to develop interbank business. Banks' liquidity risks increase with the expansion of interbank business. Interbank business exacerbates the maturity mismatch between assets and liabilities,which intensifies banks' pro-cyclical characteristics and weakens the implementation effect of the central bank's original instruments. Finally,we find that a combined liquidity management method that is based on interest rate and supplemented by unconventional policy instruments is effective.This paper makes several contributions. From a micro perspective,this paper theoretically analyzes the expansion mechanism of interbank business and the mechanism behind its influence on banks' liquidity fluctuations. We empirically test these hypotheses using data from China's bank industry. At the macro level,by developing a dynamic system model of liquidity management,this paper focuses on the influence of the money market and the whole financial system surrounding interbank business behavior on liquidity and analyzes the effects of different liquidity management methods such as pricebased policy instruments and unconventional policy instruments on smoothing the fluctuation of liquidity.We suggest that the central bank should adjust its traditional management policies of liquidity by including the size and risk of interbank behavior in the variables used in its liquidity management policy. The central bank should also create new instruments for liquidity management and strengthen the coordination between new and traditional instruments. The financial supervision authorities should update regulations that have lagged behind developments in the financial business and the evolution of financial risk and improve the coordination of monetary policy,macro-prudential supervision and microprudential supervision to prevent unnecessary volatility in the financial sector caused by overregulation.
Liquidity management is a key component of monetary policy. In recent years,liquidity in China has become a departure point between the macro and micro periodicals. This phenomenon makes it difficult for the central bank to carry out monetary policy and to manage liquidity effectively. To determine the reason for this liquidity problem,the literature focuses on the micro factors of shadow banking and the effects of traditional monetary policy instruments without considering the role of interbank business. Interbank business is a special scale expansion channel of China's banking institution and impacts the effectiveness of unconventional liquidity management systematically. This framework is not sufficient for exploring the factors behind the liquidity problem in China.To improve our understanding of liquidity fluctuation,this paper theoretically analyzes the expansion mechanism of interbank business and its influence channel on liquidity fluctuation. By incorporating the characteristics of interbank business and its influences into a new liquidity analysis framework,this paper analyzes the transmission mechanism 's dependence on financial regulation,the scale of interbank business and the liquidity level. We then empirically examine the correlation between the intensity of financial regulation,the development of interbank businessand the risk of liquidity by using a panel regression analysis model. We develop a dynamic system model,which includes the "herd behavior"of the money market and liquidity management by the central bank. We use this model to simulate the influence of interbank business on liquidity fluctuation and to analyze the effect of different liquidity management methods the central bank uses.Based on the empirical analysis of 40 Chinese banks from 2010 to 2016( including state-owned,joint-stock commercial and local corporate commercial banks) and the dynamic system simulation of the central bank's liquidity management,we come to several conclusions. Tighter financial regulation gives commercial banks a strong desire to develop interbank business. Banks' liquidity risks increase with the expansion of interbank business. Interbank business exacerbates the maturity mismatch between assets and liabilities,which intensifies banks' pro-cyclical characteristics and weakens the implementation effect of the central bank's original instruments. Finally,we find that a combined liquidity management method that is based on interest rate and supplemented by unconventional policy instruments is effective.This paper makes several contributions. From a micro perspective,this paper theoretically analyzes the expansion mechanism of interbank business and the mechanism behind its influence on banks' liquidity fluctuations. We empirically test these hypotheses using data from China's bank industry. At the macro level,by developing a dynamic system model of liquidity management,this paper focuses on the influence of the money market and the whole financial system surrounding interbank business behavior on liquidity and analyzes the effects of different liquidity management methods such as pricebased policy instruments and unconventional policy instruments on smoothing the fluctuation of liquidity.We suggest that the central bank should adjust its traditional management policies of liquidity by including the size and risk of interbank behavior in the variables used in its liquidity management policy. The central bank should also create new instruments for liquidity management and strengthen the coordination between new and traditional instruments. The financial supervision authorities should update regulations that have lagged behind developments in the financial business and the evolution of financial risk and improve the coordination of monetary policy,macro-prudential supervision and microprudential supervision to prevent unnecessary volatility in the financial sector caused by overregulation.
引文
林琳、曹勇、肖寒,2016:《中国式影子银行下的金融系统脆弱性》,《经济学(季刊)》第15卷第3期。
彭兴韵,2007:《流动性、流动性过剩与货币政策》,《经济研究》第11期。
孙国峰、蔡春春,2014:《货币市场利率、流动性供求与中央银行流动性管理:对货币市场利率波动的新分析框架》,《经济研究》第12期。
王晓枫、廖凯亮、徐金池,2015:《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》第3期。
肖崎、阮健浓,2014:《我国银行同业业务发展对货币政策和金融稳定的影响》,《国际金融研究》第3期。
曾刚、李广子,2013:《商业银行流动性影响因素研究》,《金融监管研究》第10期。
张靖佳、刘澜飚、王博,2016:《金融危机、非传统货币政策与中央银行资产负债表:探究美欧两大中央银行非传统货币政策之谜》,《经济学(季刊)》第15卷第2期。
周再清、甘易、胡月,2017:《商业银行同业资产特性与风险承担行为——基于中国银行业动态面板系统GMM的实证分析》,《国际金融研究》第7期。
朱孟楠、侯哲,2014:《中国商业银行资金错配问题研究》,《国际金融研究》第4期。
祝继高、胡诗阳、陆正飞,2016:《商业银行从事影子银行业务的影响因素与经济后果:基于影子银行体系资金融出方的实证研究》,《金融研究》第1期。
Akerlof,G.A.,and R.J.Shiller,2009,Animal Spirits:How Human Psychology Drives the Economy and Why It Matters for Global Capitalism,Princeton University Press.
Allen,F.,E.Carletti,and D.Gale,2009,“Interbank Market Liquidity and Central Bank Intervention”,Journal of Monetary Economics,Vol.56(5):639-652.
Chiarella,C.,C.D.Guilmi,and T.Zhi,2014,“Modelling the‘Animal Spirits’of Bank’s Lending Behaviour”,EDG Working Paper Series,No.1306.
Diamond,Douglas W.,and Philip H.Dybvig.,1983,“Bank Runs,Deposit Insurance,and Liquidity”,Journal of Political Economy,91,401-419.
Gale,D.,and T.Yorulmazer,2011,“Liquidity Hoarding”,Federal Reserve Bank of New York Staff Reports,No.488.
Gennaioli,N.,A.Shleifer,and R.W.Vishny,2013,“A Model of Shadow Banking”,Journal of Finance,68(4),1331-1363.
Hachem,K.,and Z.M.Song,2015,“The Rise of China’s Shadow Banking System”,Meeting Papers from Society for Economic Dynamics,No.931.
Herider,F.,H.Marie,and C.Holthausen,2009,“Liquidity Hoarding and Interbank Market Spreads:The Role of Counterparty Risk”,European Central Bank Working Paper,No.1126.
Luck,S.,and P.Schempp,2014,“Banks,Shadow Banking,and Fragility”,ECB Working Paper,No.1726.
Martin,A.,D.Skeie,and E.von Thadden,2014,“Repo Runs”,Review of Financial Studies,27(4),957-989.
Plantin,G.,2015,“Shadow Banking and Bank Capital Regulation”,Review of Financial Studies,Vol.28(1),146-175.
Poole,W.,1970,“Optimal Choice of Monetary Policy Instruments in a Simple Stochastic Macro Model”,Quarterly Journal of Economics,84(2),197-216.
Qi,J.,1994,“Bank Liquidity and Stability in an Overlapping Generations Model”,Review of Financial Studies,7(2),389-417.
(1)其中I~C为商业银行受到信贷规模约束后的最大信贷投放数量,I~σ为商业银行受到资本约束后的最大信贷投放数量,I~α为商业银行受到存贷比约束后的最大信贷投放数量。
(1)同业业务运作复杂,交易对手多,存在一定过桥费用和利润分成,我们将其产生的费用统称为同业业务成本C(AS)。
(2)BR~d为银行流动性缺口,满足(I_0+I_S)/(S+BR~d)=I_0/S=λ_0,可推得BR~d=[(I_0+I_S)S]/I_0-S=I_SS/I_0=G(s~*)S/(1-G(s~*)。
(1)具体参数设置由于篇幅原因在此略去,有需要者可与作者联系。
彭兴韵,2007:《流动性、流动性过剩与货币政策》,《经济研究》第11期。
孙国峰、蔡春春,2014:《货币市场利率、流动性供求与中央银行流动性管理:对货币市场利率波动的新分析框架》,《经济研究》第12期。
王晓枫、廖凯亮、徐金池,2015:《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》第3期。
肖崎、阮健浓,2014:《我国银行同业业务发展对货币政策和金融稳定的影响》,《国际金融研究》第3期。
曾刚、李广子,2013:《商业银行流动性影响因素研究》,《金融监管研究》第10期。
张靖佳、刘澜飚、王博,2016:《金融危机、非传统货币政策与中央银行资产负债表:探究美欧两大中央银行非传统货币政策之谜》,《经济学(季刊)》第15卷第2期。
周再清、甘易、胡月,2017:《商业银行同业资产特性与风险承担行为——基于中国银行业动态面板系统GMM的实证分析》,《国际金融研究》第7期。
朱孟楠、侯哲,2014:《中国商业银行资金错配问题研究》,《国际金融研究》第4期。
祝继高、胡诗阳、陆正飞,2016:《商业银行从事影子银行业务的影响因素与经济后果:基于影子银行体系资金融出方的实证研究》,《金融研究》第1期。
Akerlof,G.A.,and R.J.Shiller,2009,Animal Spirits:How Human Psychology Drives the Economy and Why It Matters for Global Capitalism,Princeton University Press.
Allen,F.,E.Carletti,and D.Gale,2009,“Interbank Market Liquidity and Central Bank Intervention”,Journal of Monetary Economics,Vol.56(5):639-652.
Chiarella,C.,C.D.Guilmi,and T.Zhi,2014,“Modelling the‘Animal Spirits’of Bank’s Lending Behaviour”,EDG Working Paper Series,No.1306.
Diamond,Douglas W.,and Philip H.Dybvig.,1983,“Bank Runs,Deposit Insurance,and Liquidity”,Journal of Political Economy,91,401-419.
Gale,D.,and T.Yorulmazer,2011,“Liquidity Hoarding”,Federal Reserve Bank of New York Staff Reports,No.488.
Gennaioli,N.,A.Shleifer,and R.W.Vishny,2013,“A Model of Shadow Banking”,Journal of Finance,68(4),1331-1363.
Hachem,K.,and Z.M.Song,2015,“The Rise of China’s Shadow Banking System”,Meeting Papers from Society for Economic Dynamics,No.931.
Herider,F.,H.Marie,and C.Holthausen,2009,“Liquidity Hoarding and Interbank Market Spreads:The Role of Counterparty Risk”,European Central Bank Working Paper,No.1126.
Luck,S.,and P.Schempp,2014,“Banks,Shadow Banking,and Fragility”,ECB Working Paper,No.1726.
Martin,A.,D.Skeie,and E.von Thadden,2014,“Repo Runs”,Review of Financial Studies,27(4),957-989.
Plantin,G.,2015,“Shadow Banking and Bank Capital Regulation”,Review of Financial Studies,Vol.28(1),146-175.
Poole,W.,1970,“Optimal Choice of Monetary Policy Instruments in a Simple Stochastic Macro Model”,Quarterly Journal of Economics,84(2),197-216.
Qi,J.,1994,“Bank Liquidity and Stability in an Overlapping Generations Model”,Review of Financial Studies,7(2),389-417.
(1)其中I~C为商业银行受到信贷规模约束后的最大信贷投放数量,I~σ为商业银行受到资本约束后的最大信贷投放数量,I~α为商业银行受到存贷比约束后的最大信贷投放数量。
(1)同业业务运作复杂,交易对手多,存在一定过桥费用和利润分成,我们将其产生的费用统称为同业业务成本C(AS)。
(2)BR~d为银行流动性缺口,满足(I_0+I_S)/(S+BR~d)=I_0/S=λ_0,可推得BR~d=[(I_0+I_S)S]/I_0-S=I_SS/I_0=G(s~*)S/(1-G(s~*)。
(1)具体参数设置由于篇幅原因在此略去,有需要者可与作者联系。