摘要
本文基于贝叶斯图模型测度的银行网络,第一次将网络结构中的传染风险和银行的个体风险相结合,设计了考虑网络结构因素的系统重要性指数,并对中国上市的14家银行进行了系统重要性评估。该指数可以用来衡量银行破产或崩溃时对系统造成的外溢效应大小,不仅有助于克服综合指数法无法避免的大型银行由于政府救助的必然性产生的道德风险,还保持了综合指数法的易操作性,为我国银行系统重要性评估和分类监管提供了新的思路。实证研究结果表明:我国银行的个体风险排名与资产规模排名具有极高的一致性,但个体风险排名与传染风险排名差异较大。2016年我国银行网络中传染风险最高的银行并非个体风险最高或规模最大的银行,而是国有银行中个体风险最低且规模最小的银行。本文同时计算出了2007-2016年各年度我国各家上市银行的系统重要性指数,发现我国上市银行中历年都存在规模不大但系统重要性较高的银行,本文对此提出了有针对性的对策建议。
Since the subprime crisis spread the world widely in 2008,most of the govenments have recognized the power of systematic risk and the significance of evaluation and supervision on systematically important institutions. The idea of macro management was adopted by the most influencial international financial institutions. Financial stability board（ FSB）,basel committee on banking supervision（ BCBS） and international monetary fund（ IMF） constructed a framework named＂Systemically Important Financial Institutions＂（ SIFI） in 2011. However,the lists of SIFI published by now only include the top biggest 30 banks around the world,and we will get little help for supervision on banks in China. Further more,the rank of banks＇ systematic importance were highly correlated with asset size,and it will cause the＂Too Big to Fail＂problem during supervision and government assistance. Finanlly,the trigger of subprime crisis is not the biggest bank but the most connected one and the ＂Too Interconnected to Fail＂problem has catched more and more attentions.In order to deal with the before-methioned problems,this paper takes size,substitutability and interconnectedness of banks into account according to the assessment criteria proposed by FSB. Unlike the existing researches,this paper use a graph model to quantify interconnectedness of banks and combines result with individual indexes related with size and substitutability ingeniously. This process makes the systematic importance index taking advantages of comprehensive index as well as analytic network process（ ANP）,which provides a new idea for systematic importance evaluation and classified supervision of China＇s banking system. Further more,this paper firstly use the Bayesian graphical model to estimate the banking network in China and use both financial market data and credit ranking data in the estimation process. It will not noly enlarge the risk coverage which makes the result more accurate,but also helps researchers understand a real banking n
出处
《经济管理》
CSSCI
北大核心
2018年第3期40-56,共17页
Business and Management Journal ( BMJ )
基金
国家社会科学基金重点项目"我国创新驱动转型发展评价指数的构建与应用研究"(16ATJ004)
关键词
系统重要性指数
银行网络
贝叶斯图模型
systematic importance index
banking networls
bayesian graphical model
