巴塞尔新资本协议框架下商业银行内部评级法研究
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摘要
《巴塞尔新资本协议》是商业银行进行风险管理的指引性文件。信用风险管理则是《巴塞尔新资本协议》的重要内容。本文根据《巴塞尔新资本协议》的要求,在对现有信用管理计量方法进行全面总结的基础上,进一步发展了王恒和沈利生2006年的工作,利用新的计量技术对商业银行现有信用评级系统进行了科学检验。同时,对信用风险管理的核心参数——违约概率提出了新的计量预测方法。
本文研究的主要内容和重要结论是:
1、运用排序响应面板数据模型对商业银行现有信用评级系统进行科学检验。在分离了同一时期对样本个体发生共同作用的因素后,文章发现含有随机横截面和随机系数的排序响应面板数据模型是最合理的检验模型。经过模型检验,我们得到结论:
第一,现有系统确实存在冗余指标。冗余指标的存在,很大一部分原因是主观因素的作用。它们的存在将对银行信用评级工作的结果产生一定的影响。剔除这些冗余指标,可以更好地提升信用评级系统的客观性,使得信用评级结果更加科学、合理。
其次,检验模型对各个指标权重进行了检验。模型的检验结果表明,各指标对评级结果的影响程度,与原来的指标权重设置并不一致。一方面可以重新设定财务比率的重要程度,使得真正重要的财务比率得以发挥重要作用,一方面能够使不重要的财务比率在评级系统中的权重得以调整,使其符合其对评级结果真实的影响。
最后,在加入对不同时点对各个企业共同作用的宏观经济因素时间效应后,模型能够从横截面和时间序列两个方面,更完整地描述银行信用评级系统的统计计量特征,从而更合理地对现有评级系统进行合理性检验。
2、运用二元响应面板数据模型对商业银行贷款客户违约概率进行估计和预测。我们建立三个不同的模型,利用真实数据对商业银行贷款客户违约概率进行预测研究,我们得到结论:
第一,根据模型的估计结果,我们筛选出对企业违约有实质影响的财务比率,并对这些财务比率的重要性进行了排序归类。这一结论可以为银行进行商业贷款提供更全面、更灵活的判断依据。
第二,在经过“0.5”标准静态检验法和ROC曲线动态检验法分别对各个模型进行检验后,证明含有随机截距项和随机系数的二元响应面板数据模型具备更理想的预测能力。无论样本内预测、样本外预测、还是样本总体预测,这一模型都表现出了更加理想的预测效果。因此,这一模型可以更好地完成银行对贷款客户的违约概率预测工作,更加有效地实现商业银行的风险控制。
本文从《巴塞尔新资本协议》要求出发,将对现有信用评级系统的检验和贷款客户违约概率预测融为一体,运用了新的计量技术,具有非常强的理论意义和现实意义。和相关文献相比,本文具有如下创新和独到之处:
1、文章成功地将面板数据模型和多元排序响应模型结合起来。在此基础上,进一步发展了王恒和沈利生2006年的工作,将新模型运用在对现有银行评级系统的检验工作上。新模型充分考虑了样本个体的时间效应,放松了原有方法的独立性假设,更加符合现实情况。因此,新模型能够更好地进行现有信用评级系统的检验工作。
2、文章将在同一时点影响样本个体的共同因素从随机误差项分离出来,建立了相应的二元响应面板数据模型。因为放松了对独立性的假设前提,使得模型能够更好地拟合现实情况,得到更加科学、更加客观估计结果和预测结论。文章将这一方法运用在商业银行贷款客户的违约概率预测工作上,能够更好地满足商业银行的风险管理需要。
3、文章没有简单地采用现有文献通常采用的“0.5”概率标准对违约预测模型进行检验,而是对模型预测结果分别进行了“0.5”概率标准的静态检验和ROC曲线的动态检验。ROC曲线动态检验克服了静态检验的不足和缺陷,是一种更科学的检验方法。
4、本文总结了现有各种信用评级方法的不足,并提取现有方法的优点。进而,将离散响应模型和面板数据分析进行有效的结合,并在实践中运用到评级领域。新的模型不仅解决了现有方法在我国运用实际中面临的数据缺乏的困难,更重要的是将由于观察期不同而产生的随机效应融合到模型中。这一思路是信用风险评估工作的重要突破。另一方面,这一新方法结合了传统的logit模型,同时具备了解释直观、结论简洁的优点。因此,这是一种同时具备理论意义和实践可行性的方法。
5、文章建立了一个离散响应面板数据模型的全面框架。这一框架是一个较为全面的离散响应模型和面板数据分析相结合的展示,丰富了离散响应面板数据模型的应用研究工作。对离散型因变量和面板数据模型相结合的研究工作,或许能起到一定程度的推动作用。
‘New Basel Capital Accord’is a guidance documents for commercial bank risk management. Credit risk management is the important content. According‘BaselⅡ’requirements, base on a comprehensive summary of existing measurement methods of credit management, writer used a new measurement technology available for commercial banks credit rating system, a Adequacy test. Meanwhile, writer proposed a new prediction method for measuring the probability of default, the core parameter of credit risk management.
The main contents of this paper and important conclusion:
1. This article develops a new model called the ordinal responsive panel data model which can be used to test and analyze the adequacy of credit rating system. In the separation the factors occur together of the sample individividual during the same period, the article found that the ordinal responsive panel data model, which contains a random intercept and coefficient to random coefficients, is the most reasonable test model. After testing, we have concluded:
First, the existing system indeed includes redundant indicators. The existence of these indicators, in large part because subjective factors. Their presence affects the accuracy of credit ratings. Removing these indicators will be better for enhance the objectivity of the credit rating system, making the credit rating results more adequacy and reasonable.
Secondly, model tested the weight of each index. According to test results, we found out some weights set unreasonable. On the one hand we can re-set the importance of financial ratios to make really important ratios to play important role. The other hand we can make unimportant financial ratios in the rating system to adjust the weight to match its actual ratings impact.
Finally, adding common time effect of macroeconomic factors at different time points on the various enterprises, the model can descript bank credit rating system characteristics from two aspects, cross-sectional and time series. This model will be more reasonable for the existing rating system test.
2. Writer used the binary responsive panel data models for credit default probability estimating and prediction of commercial bank loans. We build three different models, using real data on commercial bank lending to predict the probability of customer default, we have concluded:
Firsty, according to results of the estimation, we picked out financial ratios having a material impact on the corporate default. Financial strength and the importance of these were classified order. This conclusion can provide for banks more comprehensive and more flexible basis for arrangement of commercial loans.
Secondly, after tested each model using the‘0.5’standard static test and ROC curve dynamic test method, we proofed that the binary response panel data model with a random intercept and random coefficient has better predictive ability. Regardless of sample prediction, forecast sample, or population projection, the model shows a more perfect prediction effect. Therefore, this model can accomplish better the bank's customers default probability forecasting, and more effectively implement risk control of commercial banks.
According the‘BaselⅡ’requirements, Thesis integrated a inspection to the existing credit rating system and a forecast loan default probability together, using a new measurement technology. It has a very strong theoretical and practical significance. Comparing to relevant literature, this innovative and unique with the following:
Firstly, the thesis successfully sorts panel data model and multiple responses model together. Writer further developed the work by Wang Heng and Shen Lisheng in 2006. The new model will be used in the test work for existing bank rating system. Time effect of the samples individual is fully taken into account by new model. The new model relaxes the independence assumptions of the old method and is more in line with reality. Therefore, the new model can test better existing credit rating system.
Secondly, writer separated out common factors of individual at the same point from a random error term, and established a corresponding binary response panel data model. Because the relaxation of independence assumption, it makes the model can fit better with the reality, and get more objective estimates and prediction. Thesis uses this method in commercial bank loan default probability forecasts, to meet better the needs of commercial bank risk management.
Thirdly, writer did not simply adopt the "0.5" standard usually using by the existing literature for testing of default forecast models. The results of the model predictions were test separately by the "0.5" standard static test and ROC curve dynamic test. ROC curve dynamic test overcomes the deficiencies and shortcomings of the static test. It is a more scientific method.
Fourthly, the thesis is not simply the empirical analysis of banking data. This thesis summarized the shortcoming of various existing methods and extracted the advantages of existing methods. Furthermore, writer bound the discrete response model and panel data analysis together, and applied it to the rating practice. The new model not only solves difficult of lacking of data, which shows in the practice of existing methods, and more important is integrates random effects result of the different observation period into the model. It should be said that this idea is a important breakthrough of credit risk assessment. On the other hand, this new method is combined into the traditional logit model. It has the advantages which includes the visual interpretation and the conclusion simplicity. Therefore, it is a theoretical and practical of the method.
Fifthly, the thesis establishes a comprehensive framework of discrete response panel data model. It can be said that this framework shows a more comprehensive combination of the discrete response model and panel data. It riches the analysis work of the discrete response panel data model. For research on combination of discrete variables and panel data model, it may be able to play a role for promoting in a certain degree.
本文研究的主要内容和重要结论是:
1、运用排序响应面板数据模型对商业银行现有信用评级系统进行科学检验。在分离了同一时期对样本个体发生共同作用的因素后,文章发现含有随机横截面和随机系数的排序响应面板数据模型是最合理的检验模型。经过模型检验,我们得到结论:
第一,现有系统确实存在冗余指标。冗余指标的存在,很大一部分原因是主观因素的作用。它们的存在将对银行信用评级工作的结果产生一定的影响。剔除这些冗余指标,可以更好地提升信用评级系统的客观性,使得信用评级结果更加科学、合理。
其次,检验模型对各个指标权重进行了检验。模型的检验结果表明,各指标对评级结果的影响程度,与原来的指标权重设置并不一致。一方面可以重新设定财务比率的重要程度,使得真正重要的财务比率得以发挥重要作用,一方面能够使不重要的财务比率在评级系统中的权重得以调整,使其符合其对评级结果真实的影响。
最后,在加入对不同时点对各个企业共同作用的宏观经济因素时间效应后,模型能够从横截面和时间序列两个方面,更完整地描述银行信用评级系统的统计计量特征,从而更合理地对现有评级系统进行合理性检验。
2、运用二元响应面板数据模型对商业银行贷款客户违约概率进行估计和预测。我们建立三个不同的模型,利用真实数据对商业银行贷款客户违约概率进行预测研究,我们得到结论:
第一,根据模型的估计结果,我们筛选出对企业违约有实质影响的财务比率,并对这些财务比率的重要性进行了排序归类。这一结论可以为银行进行商业贷款提供更全面、更灵活的判断依据。
第二,在经过“0.5”标准静态检验法和ROC曲线动态检验法分别对各个模型进行检验后,证明含有随机截距项和随机系数的二元响应面板数据模型具备更理想的预测能力。无论样本内预测、样本外预测、还是样本总体预测,这一模型都表现出了更加理想的预测效果。因此,这一模型可以更好地完成银行对贷款客户的违约概率预测工作,更加有效地实现商业银行的风险控制。
本文从《巴塞尔新资本协议》要求出发,将对现有信用评级系统的检验和贷款客户违约概率预测融为一体,运用了新的计量技术,具有非常强的理论意义和现实意义。和相关文献相比,本文具有如下创新和独到之处:
1、文章成功地将面板数据模型和多元排序响应模型结合起来。在此基础上,进一步发展了王恒和沈利生2006年的工作,将新模型运用在对现有银行评级系统的检验工作上。新模型充分考虑了样本个体的时间效应,放松了原有方法的独立性假设,更加符合现实情况。因此,新模型能够更好地进行现有信用评级系统的检验工作。
2、文章将在同一时点影响样本个体的共同因素从随机误差项分离出来,建立了相应的二元响应面板数据模型。因为放松了对独立性的假设前提,使得模型能够更好地拟合现实情况,得到更加科学、更加客观估计结果和预测结论。文章将这一方法运用在商业银行贷款客户的违约概率预测工作上,能够更好地满足商业银行的风险管理需要。
3、文章没有简单地采用现有文献通常采用的“0.5”概率标准对违约预测模型进行检验,而是对模型预测结果分别进行了“0.5”概率标准的静态检验和ROC曲线的动态检验。ROC曲线动态检验克服了静态检验的不足和缺陷,是一种更科学的检验方法。
4、本文总结了现有各种信用评级方法的不足,并提取现有方法的优点。进而,将离散响应模型和面板数据分析进行有效的结合,并在实践中运用到评级领域。新的模型不仅解决了现有方法在我国运用实际中面临的数据缺乏的困难,更重要的是将由于观察期不同而产生的随机效应融合到模型中。这一思路是信用风险评估工作的重要突破。另一方面,这一新方法结合了传统的logit模型,同时具备了解释直观、结论简洁的优点。因此,这是一种同时具备理论意义和实践可行性的方法。
5、文章建立了一个离散响应面板数据模型的全面框架。这一框架是一个较为全面的离散响应模型和面板数据分析相结合的展示,丰富了离散响应面板数据模型的应用研究工作。对离散型因变量和面板数据模型相结合的研究工作,或许能起到一定程度的推动作用。
‘New Basel Capital Accord’is a guidance documents for commercial bank risk management. Credit risk management is the important content. According‘BaselⅡ’requirements, base on a comprehensive summary of existing measurement methods of credit management, writer used a new measurement technology available for commercial banks credit rating system, a Adequacy test. Meanwhile, writer proposed a new prediction method for measuring the probability of default, the core parameter of credit risk management.
The main contents of this paper and important conclusion:
1. This article develops a new model called the ordinal responsive panel data model which can be used to test and analyze the adequacy of credit rating system. In the separation the factors occur together of the sample individividual during the same period, the article found that the ordinal responsive panel data model, which contains a random intercept and coefficient to random coefficients, is the most reasonable test model. After testing, we have concluded:
First, the existing system indeed includes redundant indicators. The existence of these indicators, in large part because subjective factors. Their presence affects the accuracy of credit ratings. Removing these indicators will be better for enhance the objectivity of the credit rating system, making the credit rating results more adequacy and reasonable.
Secondly, model tested the weight of each index. According to test results, we found out some weights set unreasonable. On the one hand we can re-set the importance of financial ratios to make really important ratios to play important role. The other hand we can make unimportant financial ratios in the rating system to adjust the weight to match its actual ratings impact.
Finally, adding common time effect of macroeconomic factors at different time points on the various enterprises, the model can descript bank credit rating system characteristics from two aspects, cross-sectional and time series. This model will be more reasonable for the existing rating system test.
2. Writer used the binary responsive panel data models for credit default probability estimating and prediction of commercial bank loans. We build three different models, using real data on commercial bank lending to predict the probability of customer default, we have concluded:
Firsty, according to results of the estimation, we picked out financial ratios having a material impact on the corporate default. Financial strength and the importance of these were classified order. This conclusion can provide for banks more comprehensive and more flexible basis for arrangement of commercial loans.
Secondly, after tested each model using the‘0.5’standard static test and ROC curve dynamic test method, we proofed that the binary response panel data model with a random intercept and random coefficient has better predictive ability. Regardless of sample prediction, forecast sample, or population projection, the model shows a more perfect prediction effect. Therefore, this model can accomplish better the bank's customers default probability forecasting, and more effectively implement risk control of commercial banks.
According the‘BaselⅡ’requirements, Thesis integrated a inspection to the existing credit rating system and a forecast loan default probability together, using a new measurement technology. It has a very strong theoretical and practical significance. Comparing to relevant literature, this innovative and unique with the following:
Firstly, the thesis successfully sorts panel data model and multiple responses model together. Writer further developed the work by Wang Heng and Shen Lisheng in 2006. The new model will be used in the test work for existing bank rating system. Time effect of the samples individual is fully taken into account by new model. The new model relaxes the independence assumptions of the old method and is more in line with reality. Therefore, the new model can test better existing credit rating system.
Secondly, writer separated out common factors of individual at the same point from a random error term, and established a corresponding binary response panel data model. Because the relaxation of independence assumption, it makes the model can fit better with the reality, and get more objective estimates and prediction. Thesis uses this method in commercial bank loan default probability forecasts, to meet better the needs of commercial bank risk management.
Thirdly, writer did not simply adopt the "0.5" standard usually using by the existing literature for testing of default forecast models. The results of the model predictions were test separately by the "0.5" standard static test and ROC curve dynamic test. ROC curve dynamic test overcomes the deficiencies and shortcomings of the static test. It is a more scientific method.
Fourthly, the thesis is not simply the empirical analysis of banking data. This thesis summarized the shortcoming of various existing methods and extracted the advantages of existing methods. Furthermore, writer bound the discrete response model and panel data analysis together, and applied it to the rating practice. The new model not only solves difficult of lacking of data, which shows in the practice of existing methods, and more important is integrates random effects result of the different observation period into the model. It should be said that this idea is a important breakthrough of credit risk assessment. On the other hand, this new method is combined into the traditional logit model. It has the advantages which includes the visual interpretation and the conclusion simplicity. Therefore, it is a theoretical and practical of the method.
Fifthly, the thesis establishes a comprehensive framework of discrete response panel data model. It can be said that this framework shows a more comprehensive combination of the discrete response model and panel data. It riches the analysis work of the discrete response panel data model. For research on combination of discrete variables and panel data model, it may be able to play a role for promoting in a certain degree.
引文
[1] International Convergence of Capital Measurement and Capital Standards-A Revised Framework (comprehensive version -June 2006)[R],P21
[2]中国银监会年报[R],2008,P42
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[10]中国银监会年报[R],2008,P45
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