| China’s macro leverage ratio reached 270.1% in 2020,far exceeding the average leverage ratio of emerging economies.Among economic sectors,The non-financial corporate sector’s leverage ratio was 151.3 percent,leading the economy.For a long period of the past,China’s policy environment was in a relaxed state.In that time blind expansion of business scale and blind expansion of debt scale are common in various industries.When economic development slowed down,these accumulated problems had developed into serious risks and hidden dangers.Recently,China’s real estate industry has encountered large-scale management difficulties,among which the problems of Evergrande Group are particularly eye-catching.Before 2020,Evergrande Group purchased land and assets on a large scale and greatly expanded its own debt scale.When policies were tightened in 2020,evergrande Group’s potential risks were exposed: first,delayed delivery of real estate occurred on a large scale;second,massive credit and debt defaults occurred.Although Evergrande Group has taken active measures to achieve the goal of reducing the net debt ratio to less than 100%,its operating difficulties have not been fundamentally reversed,causing a great impact on related industries.It can be seen that the problem of systemic risk in non-financial sector has been relatively serious.Therefore,it is of great practical significance to research on the specific performance and evolution mechanism of systemic risk in different industries.Existing literature on systemic risk research focuses on the role of financial institutions,especially the impact of systemically important financial institutions.Chen et al.(2014)and Acharya et al.(2017)adopted different methods to study and find that banking institutions have a higher level of systemic risk than insurance institutions.Pais and Stork(2013)studied the samples of banks in the euro-zone and found that large banks had higher systemic risk contributions.Some scholars also began to pay attention to the systemic risk of non-financial industry.For example,Zhu Bo and Ma Yongtan(2018)found that systemic risk showed great differences between financial industry and non-financial industry.At present,there are few studies on the measurement of industry systemic risk in academia,and studies on the mechanism of industry systemic risk’ dynamic evolution are also relatively insufficient.Systemic risk has different contagion directions.Li Zheng et al.(2019)pointed out that systemic risk has both institutional impact on the system and systemic impact on institutions,and according to this phenomenon,studied the systemic risk of different institutions in the financial industry.Apart from this study,few studies have focused on the difference between different contagion directions of systemic risk.The study of systemic risk should not only focus on its performance,but also understand its driving causes.Some scholars both at home and abroad have studied this field from different focuses.For example,Zhu Bo et al.(2016)used CES method to study and found that there is a nonlinear relationship between systemic risk and non-interest income.Xu Chao(2011)believed that macroeconomic fluctuations would catalyze systemic risks.A large number of empirical studies focus on the analysis of driving causes in one aspect,ignoring the unified consideration of internal and external factors,and also ignoring the influence of periodic fluctuations.Chen Yulu et al.(2016)found that the fluctuation of financial cycle would not only affect economic growth,but also directly affect the probability of financial crisis.In order to effectively resolve systemic risks,regulators of all countries have strengthened the application of monetary policies and macro-prudential policies.Some scholars have analyzed their regulatory effects.For example,Agur et al.(2019)focused on the impact of monetary policies and macro-prudential policies on the credit of financial institutions.But most studies focus only on the influence on the financial sector,ignoring the influence on related entities.Understanding the differences in performance and dynamic evolution mechanisms of different industries’ systemic risk will not only help each industry improve its own risk resistance ability,but also facilitate the construction of macro-prudential regulatory framework.When analyzing the driving factors behind it,not only the internal and external factors should be taken into unified consideration,but also the impact of financial cycle fluctuations should be considered,so as to dynamically analyze the changes of the driving factors of industry systemic risks in different cycle stages,which can provide important reference for the construction of risk early warning mechanism.Monetary policy and macro-prudential policy have been widely used in China’s regulatory practice,and the evaluation of their regulatory effects is conducive to the continuous improvement of the macro-prudential regulatory framework.Therefore,the purpose of this study is to conduct the thorough research to systemic risk at the industry level,and discuss the causes and policy drivers,study industry systemic risk’s measurement method according to its transmission characteristics,study the dynamic evolution mechanism of industry systemic risk in different directions,study the driving factors of industry systemic risk based on the financial cycle perspective,further investigate the influence of the "two-pillar" policy mechanism of macro-prudential policy and monetary policy on industry systemic risk.The thesis has seven chapters.The first chapter is presentation,which presents the background,significance,content,purpose,method and innovation of this thesis.The second chapter makes a detailed discussion of relevant domestic and foreign literature,sorting out the relevant contents of systemic risk,measurement methods of systemic risk,the impact of financial cycle on systemic risk and macro-prudential supervision,and completes a brief review.In third chapter,a new RBF-ERNN model is proposed based on the risk characteristics of industry systemic risk.The fourth chapter divides industry systemic risk into industry importance and industry vulnerability according to different connotations,and analyzes the overall characteristics and dynamic evolution mechanism of industry importance and vulnerability based on RBF-ERNN model.The fifth chapter empirically analyzes the impact of internal and external factors on the importance and vulnerability of the industry,and analyzes the dynamic evolution mechanism of this impact based on the different stages of the financial cycle.The sixth chapter examines the impact of the "two-pillar" policy on the importance and vulnerability of the industry from the perspective of its single effect and its combination effect,and examines its dynamic evolution mechanism in different periods.The seventh chapter summarizes the research contents and conclusions of this paper,discusses relevant policy implications,and further forecasts the possible future research directions.The main research conclusions of this paper include:(1)Considering that industry systemic risk has dynamic and complex contagion effect,this paper introduces cutting-edge RBF neural network into expectile regression,establishes RBF-ERNN model which can reflect extreme data and capture dynamic nonlinear effect,and deduces the application principle and properties of the model theoretically.Monte Carlo simulation method was adopted to study the small sample properties of the model.Compared with the traditional expectile regression model,it is found that RBF-ERNN model has smaller measurement error and obvious comparative advantage.(2)Using RBF-ERNN model to measure various industries in China,it is found that since the international financial crisis in 2008,both the importance of the industry and the vulnerability of the industry have shown a rising trend in the non-financial industry as a whole,indicating the necessity of studying the systemic risk of the industry.In terms of industry importance,the systemic risk contribution of some non-financial sectors even exceeds that of the financial sector in some specific periods,among which the fluctuation range of mining,manufacturing,construction and real estate sectors is significantly greater than that of the financial sector.In terms of industry vulnerability,the vulnerability of non-financial industry is larger than that of the financial industry,indicating that non-financial industry is more impacted when the crisis occurs,because the financial industry is subject to strict policy supervision and thus passively improves its risk resistance ability.On the premise of defining the characteristics of systemic risk regulation,the characteristics from the special period and the transitional period are analyzed respectively.The results show that the regulatory characteristics of the financial industry are obviously different in different special periods.For example,the importance and vulnerability of the financial industry in the 2008 financial crisis were strong,but the situation was opposite in the 2015 stock market crash.Such changes are also obvious in the transition period.For some important non-financial sectors,such as the mining industry,it shows a long period of high regulatory importance,indicating that its risk contribution is increasing.However,software-related industries show high regulatory vulnerability in a long period of time,indicating that their ability to resist risks is weak.(3)Industry characteristics and macro economy have a certain explanatory power on industry systemic risk,which is different: first,there are differences between the financial industry and non-financial industry;second,there are differences in different stages of the financial cycle.According to the practice of Braun et al.(2005),this paper divides the financial cycle into three parts according to relevant characteristics: boom stage,recession stage and normal stage.Driver causes from the importance of the industry,the financial industry in the rising phase will be affected by equity trades than,share price earnings ratio and the GDP growth rate,the financial sector in the rising phase are influenced by the size and equity price-to-sales ratio,the normal period are influenced by the scale and total asset turnover,while in the recession will be affected by equity price-to-sales ratio and the influence of price earnings ratio.From the perspective of the driver causes of industry vulnerability,the financial sector in the normal phase will be influenced by the total asset turnover,the financial sector in the cycle of boom will be affected by the cash holdings increment and total asset turnover,in the normal period will be affected by the total asset turnover and the GDP growth rate,the recession will be affected by the incremental cash holdings.(4)The existing "two-pillar" policy mechanism of macro-prudential policy and monetary policy has a relatively obvious regulation effect on the systemic risk of different industries,and this effect is different for different industries and different periods.In general,no matter the importance of the industry or the fragility of the industry,the monetary policy is effective in regulating all industries,and the effect of financial industry is greater than that of non-financial industry.Macroprudential policies are effective in regulating the importance of industries,and the effect of non-financial sector is greater than that of financial sector.When the combination effect of the two is considered,industry vulnerability is significantly more affected than industry importance.In addition,as far as the dynamic evolution mechanism is concerned,the independent effect of "two-pillar" policy on industry importance or industry vulnerability is significantly more effective before the "stock market crash" than after the "stock market crash".It is worth noting that the matching effect of the "two-pillar" policy on industrial vulnerability is significantly effective in the non-financial sample after the "stock market crash",indicating that monetary policy and macro-prudential policy are in a process of gradual integration.The innovation of the thesis is mainly reflected in the following five aspects:(1)The risk characteristics and dynamic evolution mechanism of systemic risk in China are studied from the perspective of cross-industry.For a long time,academic circles have focused on the role of financial institutions,especially systemically important financial institutions,in systemic risk,and analyzed the causes and impacts of systemic risk from different research backgrounds(Kaufman,1996;Kaufman and Scott,2003;Etc.).With the continuous improvement of financial market openness and the increase of potential risks in non-financial industries,more attention has been paid to systematic risk studies in different industries.Some literatures have been concerned that non-financial industries will also bring systemic risk hazards(Guntay et al.,2014;Ma Yongtan,2018),but the research on cross-industry systemic risk is seriously insufficient.Therefore,this paper conducts an in-depth study on industrial systemic risk,not only focusing on its specific performance,but also discusses the driving causes behind it,and further analyzes the regulation effect of the existing macro regulatory framework on industrial systemic risk.(2)The measurement of systemic risk is different from the previous research methods.The RBF neural network is used to innovate the existing measurement methods.Accurate measurement of systemic risk is the premise of relevant theoretical research,so it is necessary to combine the characteristics of China’s industry systemic risk to study the measurement method.Among the mainstream methods,Va R(Vaule-at-risk)index in market data method has gradually become an important part of modern risk management(Philippe,2001),which is favored by regulators and selected by Basel Committee as the basic risk supervision index.Adrian et al.(2011)improved Va R at risk and proposed the condition Co Va R;The new index can better capture the risk contagion relationship and has strong applicability to the study of China’s industry systemic risk.However,the existing measurement methods of this index can not fully reflect the effect of systemic risk across industries.Considering the application advantages of RBF neural network,this paper creatively combines RBF neural network with Expected quantile regression(Expectile)and proposes a new measurement model RBF-ERNN.(3)In the framework of industry systemic risk,the definition of industry importance and industry vulnerability should be clarified,and the specific manifestations of the two should be distinguished so as to accurately understand the theoretical and policy significance behind them.Existing studies have not made a clear conceptual distinction between systemic risk contribution and systemic risk exposure,and most studies only focus on the performance of one of them.For example,Bai Xuemei et al.(2014)and Zhou Tianyun et al.(2014)used Co Va R method to measure China’s systemic risk contribution.Acharya et al.(2012,2017)used the market data method to measure the systemic risk exposure in the market.Some scholars even use the measurement method of systemic risk contribution to study systemic risk exposure,so as to get a wrong conclusion.An accurate measure of industry importance helps us understand the impact on the system as a whole when a particular industry gets into trouble,and industry vulnerability helps us understand how much of a shock it will have on related industries when a crisis occurs.It is of great significance to combine the two to conduct in-depth research on the industry systemic risk for preventing and resolving systemic risk.(4)When discussing the driving factors behind the systemic risks in the industry,not only internal and external factors are taken into account,but also the perspective of financial cycle is taken into account.A large number of studies have focused on the influencing factors of systemic risk.For example,Zhu Bo et al.(2016)found that there is a non-linear relationship between systemic risk and non-interest income.Zhu Bo et al.(2018)pointed out in their research that differences in financing methods and financing structures would affect the spillover effects of systemic risks.However,there are few comprehensive analyses on the causes of systemic risk in the academic circle.Therefore,this paper takes internal and external factors into consideration and further discusses the role of financial cycle fluctuations in this process.Alessi et al.(2009)and Aikman et al.(2015)pointed out successively that the fluctuation of financial cycle is conducive to reflecting the potential risks of macro economy and is a powerful indicator of early warning of financial crisis.Therefore,a comprehensive analysis of the driving causes of systemic risk and the study of its evolution mechanism combined with the financial cycle have important theoretical reference for the construction of risk early warning mechanism.(5)This paper discusses the single effect and matching effect of existing regulatory policies on industry systemic risk,thus providing theoretical support for macro-prudential regulation.At present,China’s macro-prudential framework mainly includes monetary policy and macro-prudential policy,which are called "two-pillar" policies.Their reasonable collocation helps prevent and guard against systemic risks.Existing studies focus on its impact on the financial industry.For example,Angelini(2012)compared the role of monetary policy and macro-prudential policy in the financial system based on DSGE model.Farhi and Werning(2016)found that the effective combination of monetary policy and macro-prudential policy could maintain the stability of the financial system.Few studies focus on its effect on the real industry.This study helps to broaden the theoretical framework of macro-prudential regulation.Due to the limited research level and the complexity of the problem,there are still some deficiencies in this paper:(1)The RBF-ERNN metric model proposed in this paper uses Monte Carlo simulation method rather than complex mathematical derivation to prove its properties.In the future,the properties of the model can be mathematically derived on the basis of existing studies to further prove the applicability of the method in the field of systemic risk research.(2)Considering the feasibility of data collection,this paper only selects the latest CSI 300 stocks as samples.The industry indicators constructed on this basis can effectively reflect the status quo of the industry,but fail to reflect all the industry information.On the basis of the existing research,the data collection ability can be further improved,and the data of all enterprises in various industries can be included as far as possible to construct industry indicators,so as to more accurately study the systemic risk at the industry level.(3)When discussing the regulation effect of the "two-pillar" policy,this paper uses the method of Cerutti et al.(2017)to construct macro-prudential Policy Index(MPI)by adding dummy variables.This method cannot specifically analyze the impact of different policy tools on industry systemic risk before and after implementation.Therefore,these directions can be further studied in the future. |
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