商业银行系统性风险测度与宏观审慎监管
发布时间：2018-08-13 15:41
【摘要】：在金融市场上,商业银行面临的风险不仅受自身因素影响,还受到由经济周期、国家宏观经济政策的变动、外部金融冲击等风险因素的作用,这种出于银行部门间的相关性,造成不同银行间,乃至商业银行同整个银行系统间的风险传导,致使风险从单个银行向整个银行系统的扩散称之为商业银行系统性风险。2008年爆发的金融危机,使得这种具备隐匿性、积累性和传染性,会对银行部门乃至实体经济带来巨大的负外部性效应,并且不能通过一般的风险管理手段相互抵消或者削弱的系统性风险成为国内外学术界和政府部门关注的焦点。为了合理测度中国商业银行系统性风险的大小,为宏观审慎监管构建监管指标,本文首先采用Adian和Brunnermeier 2009年提出的CoVaR方法,利用2007年11月16日至2014年2月7日12家上市的商业银行收盘价的周数据,通过分位数回归法测度各上市银行对于银行系统的风险贡献度,之后选取国有商业银行和股份制银行中具有代表性的建设银行和浦发银行,利用即得的VaR和CCoVaR数据,兼之两家银行的资产报酬率、不良贷款率、资产总额、权益乘数以及GDP增长率等有关数据,构建主成分方程,按照累计贡献度大于85%的原则,选取了第一、第二和第三主成分,进一步构建考虑自回归和滞后的EGARCH模型,实证结果显示：t-i期的CoVaR、GDP增长率、权益乘数和不良贷款率同t期的CoVaR之间存在反比例关系,t-i期的资产总额同t期的CoVaR呈正比例关系,且不良贷款率与资产总额对当期CoVaR的影响最大,在i=1(滞后一季度)的情况下,上述变量对建设银行当前CoVaR的解释力度最大,在i=2(滞后半年)的情况下,上述变量对浦发银行CoVaR的解释力度最大,监管部门可根据上述变量的变动情况,对系统重要性银行银行未来一段时间风险的变动情况进行有的放矢的监控。
[Abstract]:In the financial market, the risks faced by commercial banks are affected not only by their own factors, but also by risk factors such as economic cycles, changes in national macroeconomic policies, external financial shocks, and so on. Causing risk transmission between different banks, and even between commercial banks and the whole banking system, leading to the spread of risk from a single bank to the entire banking system called systemic risk in commercial banks. The financial crisis broke out in 2008, Making this kind of latent, accumulative and contagious, will bring huge negative externalities to the banking sector and even to the real economy. And the systemic risk which can not be offset or weakened by the general risk management means has become the focus of academic and governmental attention at home and abroad. In order to reasonably measure the systemic risk of Chinese commercial banks and construct the supervision index for macro-prudential supervision, this paper firstly adopts the CoVaR method proposed by Adian and Brunnermeier in 2009. Based on the weekly data of closing prices of 12 listed commercial banks from November 16, 2007 to February 7, 2014, the risk contribution of each listed bank to the banking system was measured by quantile regression method. Then select the representative state-owned commercial banks and joint-stock banks of China Construction Bank and Shanghai Development Bank, and use the obtained VaR and CCoVaR data, as well as the return on assets, non-performing loan ratio, total assets of the two banks. According to the principle of cumulative contribution greater than 85%, the first, second and third principal components are selected to further construct the EGARCH model considering autoregressions and hysteresis. The empirical results show that there is a inverse relationship between the growth rate of CoVaRN, the equity multiplier and the non-performing loan ratio and the CoVaR in t period. The total assets in t-I period are positively proportional to the CoVaR in t period. Non-performing loan ratio and total assets have the greatest influence on current CoVaR. In the case of ix1 (lag first quarter), the above variables explain the current CoVaR of CCB most strongly, and in the case of ix2 (lag half a year), The above variables explain the CoVaR of Pudong Development Bank most intensively. According to the change of these variables, regulators can monitor the risk of systemically important banks in a certain period of time.
【学位授予单位】：南京师范大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：F832.33
本文编号：2181429
[Abstract]:In the financial market, the risks faced by commercial banks are affected not only by their own factors, but also by risk factors such as economic cycles, changes in national macroeconomic policies, external financial shocks, and so on. Causing risk transmission between different banks, and even between commercial banks and the whole banking system, leading to the spread of risk from a single bank to the entire banking system called systemic risk in commercial banks. The financial crisis broke out in 2008, Making this kind of latent, accumulative and contagious, will bring huge negative externalities to the banking sector and even to the real economy. And the systemic risk which can not be offset or weakened by the general risk management means has become the focus of academic and governmental attention at home and abroad. In order to reasonably measure the systemic risk of Chinese commercial banks and construct the supervision index for macro-prudential supervision, this paper firstly adopts the CoVaR method proposed by Adian and Brunnermeier in 2009. Based on the weekly data of closing prices of 12 listed commercial banks from November 16, 2007 to February 7, 2014, the risk contribution of each listed bank to the banking system was measured by quantile regression method. Then select the representative state-owned commercial banks and joint-stock banks of China Construction Bank and Shanghai Development Bank, and use the obtained VaR and CCoVaR data, as well as the return on assets, non-performing loan ratio, total assets of the two banks. According to the principle of cumulative contribution greater than 85%, the first, second and third principal components are selected to further construct the EGARCH model considering autoregressions and hysteresis. The empirical results show that there is a inverse relationship between the growth rate of CoVaRN, the equity multiplier and the non-performing loan ratio and the CoVaR in t period. The total assets in t-I period are positively proportional to the CoVaR in t period. Non-performing loan ratio and total assets have the greatest influence on current CoVaR. In the case of ix1 (lag first quarter), the above variables explain the current CoVaR of CCB most strongly, and in the case of ix2 (lag half a year), The above variables explain the CoVaR of Pudong Development Bank most intensively. According to the change of these variables, regulators can monitor the risk of systemically important banks in a certain period of time.
【学位授予单位】：南京师范大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：F832.33
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