商业银行大型集团客户信用风险压力测试——基于蒙特卡洛模拟方法
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摘要
为描绘商业银行大型集团客户在压力情景下的信用风险情况,本文以某银行过去12年的不良贷款数据为对象,进行了压力测试实证研究。在研究中引入还原不良贷款率作为中间变量,使用Logit回归建立宏观经济数据和还原不良贷款率之间的量化模型,进而通过商业银行内部评级数据调整得到不同情景下每个集团成员的违约概率;在此基础上,通过蒙特卡洛模拟计算损失分布情况,并估算集团关联风险所致额外损失额。研究结果显示,大型集团客户的压力损失分布,呈现明显的"厚尾"特征,进出口、质押式回购利率、工业生产者出厂价格指数、广义货币、工业增加值对其的影响较大。据此,本文建议监管部门应引导商业银行加强宏观经济和政策研究,扶优控劣优化用信结构,控制用信集中度。
In order to study the credit risk of large conglomerate clients under stressed scenarios, this paper conducts an empirical study of stress testing based on the non-performing loan data of a commercial bank within a period of 12 years. The accumulated non-performing loan ratio is first introduced as an intermediate variable, then a logit regression model is constructed to study the impact of macro-economic data on the accumulated non-performing loan ratio. Based on the model and the internal rating data, the probability of default of each conglomerate member under different scenarios is estimated respectively. We compute loss distributions of the whole portfolio under Monte-Carlo simulations and then measure the extra loss caused by the correlation introduced by conglomerates. The result shows a significant fat-tail in the loss distribution curve, which is impacted by foreign trade, pledged repo interest rate, PPI, M2 and industrial added value. Accordingly, several suggestions are proposed, including more researches on macroeconomics and policies, optimizing credit portfolio, and controlling credit concentration ratio.
In order to study the credit risk of large conglomerate clients under stressed scenarios, this paper conducts an empirical study of stress testing based on the non-performing loan data of a commercial bank within a period of 12 years. The accumulated non-performing loan ratio is first introduced as an intermediate variable, then a logit regression model is constructed to study the impact of macro-economic data on the accumulated non-performing loan ratio. Based on the model and the internal rating data, the probability of default of each conglomerate member under different scenarios is estimated respectively. We compute loss distributions of the whole portfolio under Monte-Carlo simulations and then measure the extra loss caused by the correlation introduced by conglomerates. The result shows a significant fat-tail in the loss distribution curve, which is impacted by foreign trade, pledged repo interest rate, PPI, M2 and industrial added value. Accordingly, several suggestions are proposed, including more researches on macroeconomics and policies, optimizing credit portfolio, and controlling credit concentration ratio.
引文
1.卞家涛和余珊萍,经济新常态下商业银行压力测试模型构建与应用研究,华东理工大学学报(社会科学版),2016年第6期,56-69。
2.程功、文晴和李志勇,基于时变参数模型的银行信用风险与宏观经济形势的动态关系研究,金融监管研究,2017年第3期,51-72。
3.董申和王倩,基于机器学习的信用评级展望研究--以发电企业为例,农村金融研究,2017年第3期,41-46。
4.刘瑞霞,美国CCAR与我国商业银行全面风险管理体系构建,金融监管研究,2017年第9期,29-47。
5.邵勉也,银行压力测试的国际实践、挑战和应对策略,武汉金融,2017年第3期,4-11。
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11.谢太峰和孙璐,我国上市商业银行盈利能力影响因素的实证研究,征信,2019年第1期,62-67。
12.张乐柱和黄文苑,农信社信用风险:基于压力测试方法的实证研究--以广东省A市为例,金融理论与实践,2018年第3期,108-114。
13.张岩和段楠,中小银行信用风险压力测试传导模型选择问题研究,金融监管研究,2016年第10期,23-41。
14.中国人民银行济南分行课题组苑治亭,金融稳定压力测试实践与构建要素分析,金融发展研究,2017年第3期,44-50。
15.周梅,金融周期识别及宏观信号效应研究,征信,2019年第1期,68-73。
16.Buncic,D.,and M.Melecky,Macro Prudential Stress Testing of Credit Risk:A Practical Approach for Policy Makers,Journal of Financial Stability,2013,9(3),347-370.
17.Drehmann,M.,S.Sorensen,and M.Stringa,The Integrated Impact of Credit and Interest Rate Risk on Banks:A Dynamic Framework and Stress Testing Application,Journal of Banking&Finance,2010,34(4),713-729.
18.Kucukkocaoglu,G.,and A.Altintas,Using Non-performing Loan Ratios as Default Rates in the Estimation of Credit Losses and Macro Economic Credit Risk Stress Testing:A Case from Turkey,Risk Governance&Control:Financial markets and Institutions,2016,Vol.52.
19.Wilson,T.,Portfolio Credit Risk(Part I),Risk Magazine,1997,10(9),111-117.
2.程功、文晴和李志勇,基于时变参数模型的银行信用风险与宏观经济形势的动态关系研究,金融监管研究,2017年第3期,51-72。
3.董申和王倩,基于机器学习的信用评级展望研究--以发电企业为例,农村金融研究,2017年第3期,41-46。
4.刘瑞霞,美国CCAR与我国商业银行全面风险管理体系构建,金融监管研究,2017年第9期,29-47。
5.邵勉也,银行压力测试的国际实践、挑战和应对策略,武汉金融,2017年第3期,4-11。
6.汪寿阳和杨翠红,2018中国经济预测与展望,科学出版社,2018年。
7.王天宇和杨勇,商业银行信用风险宏观压力测试研究,商业经济与管理,2017年第5期,70-76。
8.王祥云,基于MC方法的商业银行信用风险压力测试--以某银行为例的实证分析,金融发展评论,2016年第10期,61-70。
9.吴建华、张颖和王新军,基于分位数回归模型的债务违约损失预测,证券市场导报,2018年第8期,55-65。
10.习近平,金融活经济活金融稳经济稳做好金融工作维护金融安全,紫光阁,2017年第5期,7。
11.谢太峰和孙璐,我国上市商业银行盈利能力影响因素的实证研究,征信,2019年第1期,62-67。
12.张乐柱和黄文苑,农信社信用风险:基于压力测试方法的实证研究--以广东省A市为例,金融理论与实践,2018年第3期,108-114。
13.张岩和段楠,中小银行信用风险压力测试传导模型选择问题研究,金融监管研究,2016年第10期,23-41。
14.中国人民银行济南分行课题组苑治亭,金融稳定压力测试实践与构建要素分析,金融发展研究,2017年第3期,44-50。
15.周梅,金融周期识别及宏观信号效应研究,征信,2019年第1期,68-73。
16.Buncic,D.,and M.Melecky,Macro Prudential Stress Testing of Credit Risk:A Practical Approach for Policy Makers,Journal of Financial Stability,2013,9(3),347-370.
17.Drehmann,M.,S.Sorensen,and M.Stringa,The Integrated Impact of Credit and Interest Rate Risk on Banks:A Dynamic Framework and Stress Testing Application,Journal of Banking&Finance,2010,34(4),713-729.
18.Kucukkocaoglu,G.,and A.Altintas,Using Non-performing Loan Ratios as Default Rates in the Estimation of Credit Losses and Macro Economic Credit Risk Stress Testing:A Case from Turkey,Risk Governance&Control:Financial markets and Institutions,2016,Vol.52.
19.Wilson,T.,Portfolio Credit Risk(Part I),Risk Magazine,1997,10(9),111-117.