金融监管与金融创新的共同演化分析——一个基于非线性动力学的金融监管分析框架
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摘要
本文利用演化经济学的思想和动力学方法研究金融创新的扩散及其与金融监管共同演化的路径和结果,论证了在三种不同监管策略下金融创新与监管共同演化的路径,指出随金融创新扩散程度而渐次展开的一阶监管策略更有助于实现监管目标和市场稳定。本文还考虑了存在时滞的监管策略,进一步论证了监管对金融创新扩散的影响以及监管策略的有效性。最后,总结了理论论证的政策启示,即监管者对市场动态的切实把握和平稳可预期的监管政策是实现市场稳定的关键。
China's financial market has experienced drastic volatility since 2015. Turbulence has affected not only the stock market, which has been depressed for nearly four years, and the bond market, which witnessed a 337% increase in default events in 2017, but also the emerging online-based financial innovation market. For instance, 1,279 peer-to-peer(P2 P) lending platforms involving RMB143.4 billion ceased functioning in 2018 alone. Other innovative tools and institutions such as third-party payment, Internet banking, crowdfunding, and cryptocurrencies have also experienced continuous market shocks. To combat the turbulence, China's policy-makers have prioritized risk control. In 2017, President XI Jinping declared that China would "firmly defend the bottom line of systemic financial risks", naming this the first of "three tough battles" to be fought in the following three years. For implementation, financial regulators, led by the People's Bank of China, have over the past two years strengthened related efforts, including emphasizing regulations on financial innovations. Therefore, how to balance financial innovations and regulations—allowing innovations to continue to develop while mitigating their negative impacts—has become a mutual concern of regulators and general market participants. Financial innovations and regulations are generally considered opposite sides of the same coin. Financial innovations are developed in an effort to sidestep regulations, while regulators seek to bring these innovative products or institutions back in line through corresponding rule-making. This represents a short-run trade-off, where stricter regulations lead to slower innovation development. In the long run, however, it could serve as a mechanism of mutual promotion if regulators implement proper strategies. Based on the perspective of evolutionary economics and using nonlinear dynamics methodology, we try to identify these proper strategies by separately considering the dynamics of financial innovation diffusion and the dynamics of regulations and then discussing their coevolution. We choose the Mansfield model to depict the financial innovation diffusion process and assume that regulators set the innovation participation rate as their target and that regulations have a negative impact on innovation diffusion(not development). We then derive the dynamic function of the innovation market participation rate, which is actually a function in the two-dimensional Lotka-Volterra system, with some adjustments to fit our analysis. Then we compare three different regulatory strategies.(1) The independent strategy, whereby regulatory intensity reaches a constant level regardless of the final market participation rate, will not be effective unless a series of restrictive conditions are satisfied.(2) The zero-order regulatory strategy, whereby regulatory intensity adjusts according to the market participation rate, best describes how China's "paternal" regulators are prone to behave. We show, however, that it is generally ineffective, as the market turbulence in recent years corroborates.(3) Finally, in the first-order regulatory strategy, a participation rate target range is preset, and regulatory intensity is changed only when the target is exceeded or not reached. We prove that when such a strategy is used, the coevolution between innovations and regulations leads to an asymptotically stationary point, implying regulators can meet their target. To extend the discussion, we consider the issue of time lags and show that, while the first-order strategy is still the best, a long enough time lag could nullify the strategy's effectiveness. As such, regulations should be carried out promptly. To more closely approximate real-word conditions in China, we examine the coevolution of innovations and regulations under the economic cycle and find that the stationary point now is turned into a Lyapunov stable one; this means the first-order strategy is still effective, although the impact of the economic cycle cannot be avoided.The conclusions above could be extended and generalized to the relationship between any market participants and policy-makers. An important implication for policy-makers is that the only way to keep a market stable is to closely follow the market and implement a stable, predictable regulatory policy framework.
China's financial market has experienced drastic volatility since 2015. Turbulence has affected not only the stock market, which has been depressed for nearly four years, and the bond market, which witnessed a 337% increase in default events in 2017, but also the emerging online-based financial innovation market. For instance, 1,279 peer-to-peer(P2 P) lending platforms involving RMB143.4 billion ceased functioning in 2018 alone. Other innovative tools and institutions such as third-party payment, Internet banking, crowdfunding, and cryptocurrencies have also experienced continuous market shocks. To combat the turbulence, China's policy-makers have prioritized risk control. In 2017, President XI Jinping declared that China would "firmly defend the bottom line of systemic financial risks", naming this the first of "three tough battles" to be fought in the following three years. For implementation, financial regulators, led by the People's Bank of China, have over the past two years strengthened related efforts, including emphasizing regulations on financial innovations. Therefore, how to balance financial innovations and regulations—allowing innovations to continue to develop while mitigating their negative impacts—has become a mutual concern of regulators and general market participants. Financial innovations and regulations are generally considered opposite sides of the same coin. Financial innovations are developed in an effort to sidestep regulations, while regulators seek to bring these innovative products or institutions back in line through corresponding rule-making. This represents a short-run trade-off, where stricter regulations lead to slower innovation development. In the long run, however, it could serve as a mechanism of mutual promotion if regulators implement proper strategies. Based on the perspective of evolutionary economics and using nonlinear dynamics methodology, we try to identify these proper strategies by separately considering the dynamics of financial innovation diffusion and the dynamics of regulations and then discussing their coevolution. We choose the Mansfield model to depict the financial innovation diffusion process and assume that regulators set the innovation participation rate as their target and that regulations have a negative impact on innovation diffusion(not development). We then derive the dynamic function of the innovation market participation rate, which is actually a function in the two-dimensional Lotka-Volterra system, with some adjustments to fit our analysis. Then we compare three different regulatory strategies.(1) The independent strategy, whereby regulatory intensity reaches a constant level regardless of the final market participation rate, will not be effective unless a series of restrictive conditions are satisfied.(2) The zero-order regulatory strategy, whereby regulatory intensity adjusts according to the market participation rate, best describes how China's "paternal" regulators are prone to behave. We show, however, that it is generally ineffective, as the market turbulence in recent years corroborates.(3) Finally, in the first-order regulatory strategy, a participation rate target range is preset, and regulatory intensity is changed only when the target is exceeded or not reached. We prove that when such a strategy is used, the coevolution between innovations and regulations leads to an asymptotically stationary point, implying regulators can meet their target. To extend the discussion, we consider the issue of time lags and show that, while the first-order strategy is still the best, a long enough time lag could nullify the strategy's effectiveness. As such, regulations should be carried out promptly. To more closely approximate real-word conditions in China, we examine the coevolution of innovations and regulations under the economic cycle and find that the stationary point now is turned into a Lyapunov stable one; this means the first-order strategy is still effective, although the impact of the economic cycle cannot be avoided.The conclusions above could be extended and generalized to the relationship between any market participants and policy-makers. An important implication for policy-makers is that the only way to keep a market stable is to closely follow the market and implement a stable, predictable regulatory policy framework.
引文
孔东民,2005:《Lotka-Volterra系统下市场结构的演进》,《管理工程学报》第3期。
李文泓,2009:《关于宏观审慎监管框架下逆周期政策的探讨》,《金融研究》第7期。
李妍,2010:《金融监管制度、金融机构行为与金融稳定》,《金融研究》第9期。
陆磊、杨骏,2016:《流动性、一般均衡与金融稳定的“不可能三角”》,《金融研究》第1期。
汪天都、孙谦,2018:《传统监管措施能够限制金融市场的波动吗?》,《金融研究》第9期。
谢平、邹传伟,2010:《金融危机后有关金融监管改革的理论综述》,《金融研究》第2期。
尹龙,2005,《金融创新理论的发展与金融监管体制演进》,《金融研究》第3期。
曾志耕,2012:《加强金融监管规范金融创新——“金融创新与风险管理” 研讨会综述》,《经济研究》第2期。
张萍、张相文,2010:《金融创新与金融监管:基于社会福利性的博弈分析》,《管理世界》第8期。
Bettzüge,M.O.,and T.Hens,2001,“An Evolutionary Approach to Financial Innovation”,Review of Economic Studies,68(3),493—522.
Chava,S.,A.Oettl,A.Subvamanian,and K.Subramanian,2013,“Banking Deregulation and Innovation”,Journal of Financial Economics,109(3),759—774.
Grossman,S.J.,and J.E.Stiglitz,1980,“On the Impossibility of Informationally Efficient Markets”,American Economic Review,70(3),393—408.
Hanson,S.G.,A.K.Kashyap,and J.C.Stein,2011,“A Macroprudential Approach to Financial Regulation”,Journal of Economic Perspectives,25(1),3—28.
Kane,E.J.,1981,“Accelerating Inflation,Technological Innovation,and the Decreasing Effectiveness of Banking Regulation”,Journal of Finance,36(2),355—367.
Lee,S.J.,D.J.Lee,and H.S.Oh,2005,“Technological Forecasting at the Korean Stock Market:A Dynamic Competition Analysis Using Lotka-Volterra Model”,Technological Forecasting and Social Change,72(8),1044—1057.
Mahajan,V.,E.Muller,and F.M.Bass,1990,“New Product Diffusion Models in Marketing:A Review and Directions for Research”,Journal of Marketing,1,1—26.
Malerba,F.,2006,“Innovation and the Evolution of Industries”,Journal of Evolutionary Economics,16(1—2),3—23.
Meade,N.,and T.Islam,2006,“Modelling and Forecasting the Diffusion of Innovation-A 25-year Review”,International Journal of Forecasting,22(3),519—545.
Merton,R.C.,1995,“Financial Innovation and the Management and Regulation of Financial Institutions”,Journal of Banking and Finance,19(3—4),461—481.
Miller,M.H.,1986,“Financial Innovation:The Last Twenty Years and the Next”,Journal of Financial and Quantitative Analysis,21(4),459—471.
(1)Lotka-Volterra模型是美国生态学家Lotka和意大利数学家Volterra在20世纪初提出的用于描述种群数量变化的模型,在只考虑两个种群的情形下,其一般形式为:,其中,N1和N2表示两个种群的数量,r1和r2表示两个种群的增长率,K1和K2是两个种群的环境承载量,α21和α12表示两个种群间相互的影响,在物种1被物种2捕食或物种1和物种2存在竞争关系的情形下,α21取正值。
(2)当金融创新的扩散方程为Bass扩散模型时,可表示为,此时特征方程为:与采用Mansfield的模型相比,系数矩阵的行列式和迹的正负都不变,因此结论也相同。
(3)Lyapunov稳定性在数学上指这样一种状态:设t=t0时动力学方程的解为,另一受扰动偏离它的解为。若?∈>0,?η>0,使得当时,对任意t>t0都成立,则称解是Lyapunov稳定的。对比渐近稳定:设t=t0时动力学方程的解为,另一受扰动偏离它的解为。若?∈>0,?η>0,使得当时,若t→∞则,则称解是渐近稳定的。
李文泓,2009:《关于宏观审慎监管框架下逆周期政策的探讨》,《金融研究》第7期。
李妍,2010:《金融监管制度、金融机构行为与金融稳定》,《金融研究》第9期。
陆磊、杨骏,2016:《流动性、一般均衡与金融稳定的“不可能三角”》,《金融研究》第1期。
汪天都、孙谦,2018:《传统监管措施能够限制金融市场的波动吗?》,《金融研究》第9期。
谢平、邹传伟,2010:《金融危机后有关金融监管改革的理论综述》,《金融研究》第2期。
尹龙,2005,《金融创新理论的发展与金融监管体制演进》,《金融研究》第3期。
曾志耕,2012:《加强金融监管规范金融创新——“金融创新与风险管理” 研讨会综述》,《经济研究》第2期。
张萍、张相文,2010:《金融创新与金融监管:基于社会福利性的博弈分析》,《管理世界》第8期。
Bettzüge,M.O.,and T.Hens,2001,“An Evolutionary Approach to Financial Innovation”,Review of Economic Studies,68(3),493—522.
Chava,S.,A.Oettl,A.Subvamanian,and K.Subramanian,2013,“Banking Deregulation and Innovation”,Journal of Financial Economics,109(3),759—774.
Grossman,S.J.,and J.E.Stiglitz,1980,“On the Impossibility of Informationally Efficient Markets”,American Economic Review,70(3),393—408.
Hanson,S.G.,A.K.Kashyap,and J.C.Stein,2011,“A Macroprudential Approach to Financial Regulation”,Journal of Economic Perspectives,25(1),3—28.
Kane,E.J.,1981,“Accelerating Inflation,Technological Innovation,and the Decreasing Effectiveness of Banking Regulation”,Journal of Finance,36(2),355—367.
Lee,S.J.,D.J.Lee,and H.S.Oh,2005,“Technological Forecasting at the Korean Stock Market:A Dynamic Competition Analysis Using Lotka-Volterra Model”,Technological Forecasting and Social Change,72(8),1044—1057.
Mahajan,V.,E.Muller,and F.M.Bass,1990,“New Product Diffusion Models in Marketing:A Review and Directions for Research”,Journal of Marketing,1,1—26.
Malerba,F.,2006,“Innovation and the Evolution of Industries”,Journal of Evolutionary Economics,16(1—2),3—23.
Meade,N.,and T.Islam,2006,“Modelling and Forecasting the Diffusion of Innovation-A 25-year Review”,International Journal of Forecasting,22(3),519—545.
Merton,R.C.,1995,“Financial Innovation and the Management and Regulation of Financial Institutions”,Journal of Banking and Finance,19(3—4),461—481.
Miller,M.H.,1986,“Financial Innovation:The Last Twenty Years and the Next”,Journal of Financial and Quantitative Analysis,21(4),459—471.
(1)Lotka-Volterra模型是美国生态学家Lotka和意大利数学家Volterra在20世纪初提出的用于描述种群数量变化的模型,在只考虑两个种群的情形下,其一般形式为:,其中,N1和N2表示两个种群的数量,r1和r2表示两个种群的增长率,K1和K2是两个种群的环境承载量,α21和α12表示两个种群间相互的影响,在物种1被物种2捕食或物种1和物种2存在竞争关系的情形下,α21取正值。
(2)当金融创新的扩散方程为Bass扩散模型时,可表示为,此时特征方程为:与采用Mansfield的模型相比,系数矩阵的行列式和迹的正负都不变,因此结论也相同。
(3)Lyapunov稳定性在数学上指这样一种状态:设t=t0时动力学方程的解为,另一受扰动偏离它的解为。若?∈>0,?η>0,使得当时,对任意t>t0都成立,则称解是Lyapunov稳定的。对比渐近稳定:设t=t0时动力学方程的解为,另一受扰动偏离它的解为。若?∈>0,?η>0,使得当时,若t→∞则,则称解是渐近稳定的。