A Research On Measuring The Systemic Risk And Its Contagion In Networks

The devastating global financial crisis in 2008 has proved that it is difficult to get early-warming of financial crisis.However,it is certain that systemic risk,which has a significant feature of risk surge,needs a long accumulative process to become financial risk.Although the process is very subtle,the in–depth study and surveillance can create possibility of recognizing,capturing it.Furthermore,the implementation of effective measure can limit risk accumulation and release excessive accumulated risk.Therefore,how to establish mechanism of capturing the surge of risk and construct an effective systemic risk measurement becomes an important issue for the academic and regulators.Under the accelerated process of economic globalization and financial liberalization,the linkages between the global economic and financial systems are getting closer and closer,causing the global financial system to be a complex network.In this network,the contagion of the crisis will be more rapid,and the systemic risk caused by it will be more destructive.It is necessary to study the systemic risk contagion mechanism of banking system,especially the contagion effect of systemic risk in complex financial network,with the background of the sustained expansion of interbank business and the tightening of institutional contact.What is the influence of mechanism of bank network structure on systemic risk contagion? What types of network structure is more prone to systemic risk? What types of network can slow down systemic risk? with the networked trend of the current financial system,the study of the above problems can provide a useful theoretical basis and empirical support for the prevention and regulation of systemic risk of China's financial system.In order to conduct further study on banking systemic risk and its network contagion mechanism,this paper combines theoretical analysis with empirical analysis by CCA,Co Va R,complex network analysis,numerical simulation analysis,panel regression and other methods.This paper mainly solves three problems: Firstly,how to take into account the nonlinear mechanism of the surge in risk theoretically so that the existing systemic risk measurement method can be closer to the extreme financial events.Secondly,from a theoretical point of view,the article analyses the specific functionary mechanism,which leads bank network to be robust and fragile,and conditions of the network structure in which it will emerge risk contagion effect and risk sharing effect respectively.Thirdly,from the angle of empirical research,this paper explores the impact of the network structure of banking institutions on the systemic risk contagion.With the further combination of the individual risk and network structure characteristics,it also explores the comprehensive effect on systemic risk contagion and the type of network structure in which the regulatory to curb individual risk can play a role in preventing systemic risk.Thirdly,from a theoretical point of view,the article analyses the specific functionary mechanism,which leads bank network to be robust and fragile,and conditions of the network structure in which it will emerge risk contagion effect and risk sharing effect respectively.The whole text is divided into five chapters,including three parts.The first part consists of the chapter one and the chapter two.This part measures systemic risk of China's banking system from time dimension and section dimension.First of all,it introduces the theoretical basis of traditional CCA method,and then loosens the original pure diffusion assumption to jump-diffusion,thus proposing Jump-CCA which incorporates the risk surge mechanism into consideration.This chapter creatively constructs mixed-frequency macro dynamic factor to replace the original forward-looking information and come up with Macro-Jump-CCA by comprehensively using the financial market and macroeconomic information.Finally,it adopts traditional CCA?Jump-CCA and Macro-Jump-CCA to measure the systemic risk of banking sector from two angles of numerical simulation and empirical study,which takes case of banking sector.The third chapter introduces Co Va R based on quantile regression.Then taking the listed banks in our country as the research sample,it measures the systemic risk spillover of each institution by Co Va R from the cross-section dimension.Besides,this chapter builds a systemic risk prediction model based on the influencing factors.The second part is the third chapter,which is theoretical basis of the third part and covers the theoretical mechanism analysis of the impact of banking network structure on systemic risk.In the setting of the theoretical model,this part analyzes risk contagion and risk sharing,produced from integration and diversification in bank network,and explains the formation mechanism of the systemic risk caused by the it.At last,this section studies stochastic network and core—periphery network with the application of numerical simulation,and also focuses on the impact of integration and diversification on systemic risk.The third part includes the fourth and the fifth chapter,which covers empirical study on the impact of banking network structure towards systemic risk.Making the listed bank as the sample,the fourth chapter selects correlation coefficient to not only construct bank network but also access to the network diagram and visual description.This section ends with calculation of individual centricity index to exactly quantify the network structure characteristics of banking institutions.The fifth chapter uses several measurement of network centrality such as degree centrality,betweenness centrality,closeness centricity and eigenvector centrality to representative bank.Then the introduction of higher-degree polynomial of network structure aims to show regime-switching of risk contagion in networks and obtain mechanism of risk contagion under different network structure on basis of panel regression model.Further,Individual bank risk and its Interaction with network structure are included in model to analyze the transmission mechanism of individual risk to systemic risk under different network structure.Through the above research,this paper comes to some meaningful conclusions:The result of systemic risk of banking sector in China,estimated by Macro-Jump-CCA after improvement,reflects three deductions.(1)by using Jump-CCA,systemic risk can be predicted three to six months in advance,and its warning signal strength can be improved effectively.When the systemic risk is stable,the default distance between Jump-CCA and the traditional CCA demonstrates separated.When there is a surge in systemic risk and a precursor of extreme financial events,default distance of Jump-CCA exhibits a jump convergence to default distance of traditional CCA.(2)risk information of Jump-CCA and the traditional CCA are derived from financial market information,but empirical evidence of China's economy illustrates that complete dependence on financial market information can easily result in noise interference to the warming,which causes a total failure of the original policy reaction time——around 3~6 months.(3)from the point of view of improving the quality of risk information,mixed-frequency macro dynamic factor estimated in this paper basically maintain dynamic characteristics of banking index yield and jumping clustering.At the same time,it not only absorbs the macroeconomic information not fully reflected in the financial market,but also amends jump volatility of banking index yield,owning the advantages of real-time capture of systemic risk and its risk surge dynamics.(4)Because of complete dependence on financial market information Jump-CCA will lead to invalid alert.In contrast,Macro-Jump-CCA,using mixed-frequency macro dynamic factor as a source of risk information,can identify the financial market noise signal in advance and reduce the impact of the error signal,so as to provide policy response time of 2~3 months for systemic risk warning under noise conditions.Secondly,Random simulation results of stochastic network and core—periphery network reflects that both of integration and diversification have non-unidirectional mechanism on systemic risk,but they are different.When the initial diversification is small,increasing the effect can enhance the contact of financialing institutions,causing the expansion of contagion range and systemic risk.Along with the enlargement of diversification beyond threshold,it will play a role in risk sharing and reduce systemic risk,because risk contagion in each financialing institution can be fully dispersed.When the integration is small,the contact between financialing institutions are very weak.Increasing the effect can enhance the contact of financialing institutions,accompanied by positive effects on systemic risk.However,financialing institutions risk conditions will depend entirely on other institutions when the effect is beyond threshold.And its original financialruptcy risk also will decline.These responses have a slow-release effect on systemic risk.Thirdly,an empirical study on the direct impact of bank network structure on systemic risk contagion proves:(1)degree centrality of banking institution in network has a significant positive impact on systemic risk.The higher degree centrality of bank is,and the stronger the ability to expand the scope of risk contagion in the network is,the greater the systemic risk will be.Therefore,a higher degree of degree centrality will lead to a more fragile banking network.(2)closeness centrality is not a one-way effect on systemic risk,but a risk contagion effect of two-zone of "robust and fragile".It means that there is a U type relationship between it and systemic risk(3)Compared with the risk effect of two-zone,betweenness centrality shows a more complex risk contagion effect of three-zone,whose risk regime is divided into three parts: low,middle and high.When the bank institution is located between the low regime and the high regime,the augment of betweenness centrality will increase the systemic risk;when the bank institution is located in the middle regime,the augment of betweenness centrality will slow the release of systemic risk.Fourthly,based on an empirical study of incorporating network structure and individual risk into a analytical framework,the paper finds that The individual risk of the banking institution and its closeness centrality have synergistic effects on systemic risk.The concrete representations is that risk mechanism of two regime switching in view of closeness centrality has low closeness centrality and high closeness centrality.In the low closeness centrality,the individual risk of the bank has a positive effect on the systemic risk,while the capital regulation can reduce the systemic risk and the individual risk.In the high closeness centrality,the individual risk of banks has reverse and shifting effect on systemic risk.Besides,raising the capital adequacy ratio will cause transmission of individual risk from banks to the system,induction of the rise of systemic risk,and finally the failure capital regulation.The innovations of this paper are as follows:Firstly,it is difficult to analysis the risk surge in reality with traditional CCA,because it assumes that Risk change is consistent with continuous diffusion process.So the paper loosen the assumption of continuous diffusion on the basis of traditional CCA.The setting of model introduces jump diffusion to reflect risk surge caused by extreme financial events.This innovation achieves the fitting of risk surge mechanism of extreme financial events so as to enhance the theoretical feasibility of systemic risk.Secondly,an important highlight of CCA lies on obtaining forward-looking information from financial markets,but not fully consistent with macro risk information.In order to improve prospective reflection of the source of risk information,This chapter creatively constructs mixed-frequency macro dynamic factor to replace the original asset price from financial market by comprehensively using the financial market and macroeconomic information.It is expected to effectively solve the mismatch of market's risk information and risk surge caused by jump diffusion.Consequently,this paper plans to fill up the missing of the feature of risk surge in traditional CCA,thus open up a new field of application of CCA.Thirdly,Compared with the research on impact of the bank network structure's compactness,node degree and other local characteristics on systemic risk,this paper contributes to use more indicators of network structure such as degree centrality,betweenness centrality,closeness centricity and eigenvector centrality,so that can provide a comprehensive analysis of contagion of systemic risk.What's more,this paper is not limited to only concentrate on directly affects of the bank network structure on systemic risk,and instead integrates the individual bank risk and network structure into the same empirical analysis framework,and make clear the transmission effect from individual bank risk to banking system under different network structure.