基于KMV模型的商业银行外部信用评级研究

发布时间：2018-07-28 13:02

【摘要】：近年来,我国中小商业银行(区域性银行、城市银行、村镇银行)大量涌现,它们的信誉水平和资产质量良莠不齐,在这种背景下,推动商业银行外部信用评级不仅具有必要性而且具有紧迫性。另一方面,近年来商业银行纷纷在国内证券市场上市交易,也使很多金融理论模型的应用成为现实,推动商业银行外部信用评级具有了更大的可行性。在全球经济复苏放缓、国内经济增长方式和结构进一步调整、大规模信贷投放导致信贷结构失衡加剧和行业集中度风险上升的今天,认真研究吸取国际评级制度的经验教训,完善我国银行外部信用评级体系,促进信用评级业健康发展,是一个既有理论意义,又有现实意义的课题。众所周知,要对商业银行进行外部信用评级首先要分析和度量商业银行所面临的各项风险。随着金融市场的快速发展,传统的信用风险度量方法及模型对信用风险的揭示已经远远不能满足现实的风险状况,应用现代信用风险度量方法和模型管理信用风险就显得尤为关键。在几种比较常见的信用风险度量模型中,鉴于KMV模型在企业信用风险评估方面的高效和实用性,该模型现在已被越来越多的投资公司使用。本文利用KMV模型对我国上市商业银行面临的信用风险进行度量,以此作为商业银行信用评级的参考角度之一,为我国的商业银行外部信用评级体系的健全做出一些建议。本文首先主要对研究背景和意义进行简单阐述,并总结国内外现有的学术文献,提出拟解决的主要问题和研究内容。然后阐述了银行信用评级理论和现行的评级方法,对我国目前商业银行外部评级的评级主体、评级方法、评级指标进行介绍、归类、比较和评价。又通过对KMV模型的理论基础、基本框架及计算过程的介绍,对选取的我国十六家A股上市商业银行的股票数据作为样本,借助EXCEL和mathcad数学软件进行实证分析,运用KMV模型比较它们各自的违约距离和预期违约概率,并对结果进行比较分析,以此说明我国应用KMV模型的可行性。最后总结我国目前的银行业评级的问题并针对我国银行信用评级的发展提出了相应的对策和建议。
[Abstract]:In recent years, a large number of small and medium-sized commercial banks (regional banks, city banks, village banks) have emerged in our country. It is not only necessary and urgent to promote the external credit rating of commercial banks. On the other hand, in recent years, commercial banks have been listed and traded in the domestic securities market, which also makes the application of many financial theory models become a reality, which makes it more feasible to promote the external credit rating of commercial banks. At a time when the global economic recovery is slowing, the pattern and structure of domestic economic growth have been further adjusted, and large-scale credit lending has led to an increase in the imbalance in the credit structure and the risk of industrial concentration, we have carefully studied and learned from the experience and lessons of the international rating system, Perfecting the external credit rating system of Chinese banks and promoting the healthy development of credit rating industry is a subject of both theoretical and practical significance. As we all know, the external credit rating of commercial banks must first analyze and measure the risks faced by commercial banks. With the rapid development of the financial market, the traditional credit risk measurement methods and models have been far from satisfying the actual risk situation. The application of modern credit risk measurement methods and models to manage credit risk is particularly critical. In several common credit risk measurement models, the KMV model has been used by more and more investment companies in view of its high efficiency and practicability in enterprise credit risk assessment. This paper uses the KMV model to measure the credit risk faced by the listed commercial banks in our country, as one of the reference angles of the commercial banks' credit rating, and makes some suggestions for the perfection of the external credit rating system of the commercial banks in our country. In this paper, the background and significance of the research are briefly described, and the existing academic literature at home and abroad is summarized, and the main problems to be solved and the research contents are put forward. Then it expounds the theory of bank credit rating and the current rating method, introduces, classifies, compares and evaluates the main body, method and index of external rating of commercial banks in our country. Through the introduction of the theoretical basis, basic framework and calculation process of KMV model, the stock data of 16 A-share listed commercial banks in China are selected as samples, and the empirical analysis is carried out with the help of EXCEL and mathcad mathematical software. The KMV model is used to compare their respective default distance and expected default probability, and the results are compared and analyzed to illustrate the feasibility of applying the KMV model in China. Finally, this paper summarizes the problems of banking rating in China and puts forward corresponding countermeasures and suggestions for the development of bank credit rating in China.
【学位授予单位】：华东师范大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：F832.33;F224

【参考文献】