商业银行基于KMV模型对上市公司客户信用风险度量研究

发布时间：2018-01-09 06:29

本文关键词：商业银行基于KMV模型对上市公司客户信用风险度量研究　出处：《西南政法大学》2012年硕士论文　论文类型：学位论文

【摘要】：防范信用风险一直是商业银行经营管理过程中面临的核心问题。目前国际上“欧债危机”的恶化，美国经济的持续低迷以及“阿拉伯之春”引起的阿拉伯世界各国的政治动荡等不利因素进一步加剧了世界经济的不确定性，这种不确定性将会恶化银行信用的外部环境，使商业银行业面临的信用风险进一步加大。国内通胀引起的原材料与劳动力价格的普遍上涨，增加了企业的成本，进而挤压了企业的利润空间。加之国内利率的市场化以及市场流动性的短缺等诸多因素的影响加大了企业的违约风险。严峻的信用风险形势对我国商业银行信用风险的防范提出了更高的要求。本文以商业银行面临的上市公司信用风险为研究对象，通过改进KMV模型违约点选取的参数，使之更适用于我国金融市场现状以及使银行更准确的对上市公司客户的信用风险进行度量。本文对国内外有关KMV模型以及信用风险度量研究的文献进行归类分析的基础上，介绍了信用风险的度量由定性分析向定量模型发展的过程，通过对比国际上最有代表性的四大信用度量模型，确定KMV模型较为适合中国的金融市场环境。鉴于KMV模型在国外应用的经济环境与我国现行经济状况存在很大的差异，本文对模型违约点的选取及股权市场价值的计算进行了一定的修正，使之适合我国的金融市场现状以及我国信用风险管理现状。然后利用修正后的KMV模型对我国4个行业中的32家上市公司的数据进行实证研究，得出以下几个结论： 1.通过对ST公司与非ST公司三组违约点下的违约距离均值差的比较发现，违约点选取的参数为0.75时，即违约点DP=流动负债+0.75*长期负债时，KMV模型预测效果最显著。 2.文章对两组样本即ST上市公司与非ST上市公司的违约距离进行对比分析，发现非ST上市公司的违约距离要显著的大于ST的上市公司的违约距离，说明经过修正后的模型能够较好的区分ST公司与非ST公司的违约风险。 3.四个行业的违约距离存在明显差异，按违约风险由大到小依次排序为：房地产行业、生物制药行业、汽车行业、电力行业。最后结合实证结果，对四个行业整体违约风险大小及风险产生的原因作出分析，并为商业银行信贷管理提出建议。全文大致分为六个部分：第一部分为绪论，主要阐述了论文选题的背景、意义以及文章的研究思路、研究内容、研究方法和可能的创新之处。第二部分是国内外相关研究现状综述，对国内外关于信用风险度量和管理的理论研究成果进行梳理，并对优秀文献进行简单评述。第三部分是分别对几种信用风险度量方法进行优缺点的分析，重点对KMV模型作了详细介绍，包括模型所依据的理论基础，研究框架和计算步骤。通过比较分析突出了KMV模型的优势。第四部分是模型的修正及实证分析。该部分首先根据中国经济的实际状况对模型进行合理的修正。然后从上市公司中选取32家具有代表性的上市公司（包含ST与非ST公司）作为样本，，运用修正后的KMV模型对样本进行实证分析，并根据实证结果进行比较分析，分析结果表明，KMV模型能较好的识别上市公司的风险。既能够较好的区分ST公司与非ST公司的违约风险，又能够识别不同行业的违约风险，以此说明我国应用KMV模型的可行性。第五部分根据实证结果分析对商业银行信用风险管理提供对策建议。论文最后部分是总结与展望，对全文内容进行总结概括，指出了研究的局限性，并对后续的研究工作提出展望。
[Abstract]:To prevent the credit risk has been the core issue facing the management of commercial banks. The current international debt crisis worsened, political unrest and other unfavorable factors continued downturn in the US economy and the "Arabia spring" by Arabia world further exacerbated the world economic uncertainty, this uncertainty will deteriorate bank credit in the external environment, the credit risk faced by commercial banks. To further increase domestic inflation caused by raw materials and labor costs generally rose, increasing the cost of enterprises, and then squeeze corporate profit margins. Coupled with the impact of the domestic interest rate marketization and market liquidity shortage and other factors increase the enterprise default risk. Put forward higher requirements for credit risk situation of Chinese commercial bank credit risk prevention.
The credit risk of listed companies are taking commercial banks as the research object, through the parameters of the improved KMV model default point is selected, which is more suitable for China's financial market situation and make banks more accurate to the listed company credit risk measurement. The basis of the classified analysis in the literature on relevant research at home and abroad to measure KMV model and credit risk, introduces the measurement of credit risk from qualitative analysis to quantitative model of the development process, through the international comparison of the most representative of the four major credit measure model, the KMV model is more suitable for China financial market environment. In view of the KMV model, there is a big difference in foreign economic environment and I in the current economic situation, this paper made some amendments to the model default point selection and stock market value calculation, which is suitable for China's financial market The status quo and the current situation of credit risk management in China are analyzed. Then the data of 32 listed companies in 4 industries in China are empirically studied by using the revised KMV model.
1., by comparing the distance difference between the three groups of default points of ST company and non ST company, it is found that when the default point selection parameter is 0.75, that is, the default point DP=, the +0.75* liability is the most significant.
The 2. pairs of two samples of ST and non ST listed companies default distance for comparative analysis, found that non ST listed companies default distance are significantly greater than ST distance to default of listed companies, the modified model can distinguish between ST companies and non ST companies default risk.
3., there are obvious differences between the four industries' default distance. According to the risk of default, they are ranked as follows: the real estate industry, the biopharmaceutical industry, the automotive industry, the electric power industry.
Finally, based on the empirical results, this paper makes an analysis of the size of the four industries as a whole and the causes of the risk, and puts forward some suggestions for the credit management of the commercial banks.
The full text is roughly divided into six parts:
The first part is the introduction, which mainly expounds the background, significance and research ideas, research contents, research methods and possible innovations of the thesis.
The second part is the summary of the related research at home and abroad, and the theoretical research results of credit risk measurement and management at home and abroad are reviewed, and the excellent literature is simply commented.
The third part is the analysis of the advantages and disadvantages of several credit risk measurement methods. The KMV model is introduced in detail, including the theoretical basis, research framework and calculation steps based on the model. Through comparative analysis, the advantages of KMV model are highlighted.
The fourth part is the empirical analysis and correction model. Firstly, according to the actual situation of the economic Chinese madereasonable amendment to the model. Then from the listed companies in the selection of 32 representative listed companies (including ST and non ST companies) as a sample, using the modified KMV model to analyze the samples, and according to comparative analysis of the empirical results, the analysis results show that the KMV model can better identify the risk of listed companies. It can distinguish between ST companies and non ST companies default risk, and to identify the different sectors of the risk of default, in order to show the feasibility of the application of KMV model in China.
The fifth part provides countermeasures and suggestions on the credit risk management of commercial banks according to the empirical results.
The last part of the paper is the summary and prospect, summarizes the content of the full text, points out the limitations of the research, and puts forward the prospect of the follow-up research work.

【学位授予单位】：西南政法大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：F832.33

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