基于KMV模型的商业银行的信用违约风险对比研究

发布时间：2018-02-05 22:38

本文关键词： 上市银行信用风险 KMV模型　出处：《山东大学》2012年硕士论文　论文类型：学位论文

【摘要】：引发国际性信用危机的往往是少数几家存在信用风险的银行,从美国在20世纪初由长期资本管理公司引发的华尔街危机,到雷曼兄弟引发的全球性金融危机,都是由于少数存在信用风险的金融机构导致的。信用违约风险的发生对整个金融系统产生巨大冲击,从而导致了金融危机的发生,因此测算违约风险水平,防范信用违约风险对金融系统的稳定至关重要。在国外银行业信用违约风险的测算方法已经成熟,并建立了详细和庞大的数据库资料,对于从各个角度评价银行信用风险提供了依据。而对我国的银行业样本的信用风险分析,不同学者采用了不同的方法尝试对银行违约风险的测算,主要集中于对几大模型的有效性分析和适应性修正的研究上。评价指标主要是传统的不良资产比率和资本充足率,对银行业信用风险的评价主要采用五级分类法,相比新《巴塞尔协议》对全球银行业信用评级体系的要求还有一定差距,如大多数发达国家所采用的KMV评级。鉴于信用违约风险测算在金融系统的重要作用,本文运用了最新的计量模型KMV模型测算了我国银行的违约风险,选择信用违约距离、资产波动率、股权波动率等几大指标评价信用风险,并对影响我国银行违约风险的因素进行了分析。本文首先选择了已经上市的16家银行4年的数据作为研究对象,并按照因子分类法对其进行分组,分为传统国有商业银行、股份制商业银行及城市商业银行,并在组内根据打分结果做出对比,其次,本文对KMV模型做出几点适应性修正,并提取样本的股权数据和资产数据,分别计算样本的信用违约距离。最后本文结合因子的分组,对三组银行的信用违约距离的计算结果、对银行业的整体走势、对组内各个样本的变化分别进行分析,并对其做出预测。本文的研究结果表明,上市的16家银行整体信用风险状况良好,其中股份制商业银行的信用风险最小,发展较稳定,说明其信用风险机制已经趋于成熟和完善,传统国有银行信用风险其次,但是各家银行的信用风险变化较大,顺周期波动的特征明显。同时暴露出地方性商业银行的信用风险整体较大,说明其信用风险控制机制还很不完善,这也为各地不断成立的地方性金融机构敲响了警钟。
[Abstract]:The international credit crisis is often triggered by a few banks with credit risks, from the Wall Street crisis triggered by long-term capital management companies in the United States in early 20th century to the global financial crisis triggered by Lehman Brothers. The occurrence of credit default risk has a huge impact on the entire financial system, resulting in the occurrence of financial crisis, so calculate the level of default risk. Preventing the risk of credit default is crucial to the stability of the financial system. In foreign banking credit default risk measurement method has been mature, and has established a detailed and huge database. For the evaluation of bank credit risk from various angles, the credit risk analysis of banking samples in China, different scholars have adopted different methods to try to measure the risk of bank default. It mainly focuses on the effectiveness analysis and adaptive modification of several models. The evaluation indicators are mainly the traditional non-performing assets ratio and capital adequacy ratio. The evaluation of banking credit risk mainly adopts the five-level classification method, compared with the requirements of the new Basel Accord on the global banking credit rating system, there is still a certain gap. Such as the KMV rating adopted by most developed countries. In view of the important role of credit default risk measurement in the financial system, this paper uses the latest measurement model KMV model to calculate the default risk of Chinese banks, choose the distance of credit default, asset volatility. Several indicators, such as equity volatility, evaluate credit risk, and analyze the factors that affect the default risk of Chinese banks. Firstly, this paper selects the data of 16 listed banks for 4 years as the research object. According to the classification of factors, it is divided into traditional state-owned commercial banks, joint-stock commercial banks and urban commercial banks. This paper makes several adaptive modifications to the KMV model and extracts the equity data and asset data of the sample to calculate the credit default distance of the sample. Finally this paper combines the grouping of factors. The calculation results of the credit default distance of the three groups of banks, the overall trend of the banking industry, the changes of each sample in the group are analyzed, and the prediction is made. The results of this paper show that the overall credit risk of the 16 listed banks is in good condition, in which the joint-stock commercial banks have the smallest credit risk and the development is relatively stable. It shows that its credit risk mechanism has become mature and perfect, the traditional state-owned bank credit risk is next, but the credit risk of each bank changes greatly. At the same time, the credit risk of the local commercial banks is relatively large, which indicates that the credit risk control mechanism is not perfect. This has also sounded the alarm bell for the local financial institutions that have been set up all over the world.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：F832.33;F224

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