基于KMV模型的我国上市公司信用风险评估实证研究

发布时间：2018-01-15 02:22

本文关键词：基于KMV模型的我国上市公司信用风险评估实证研究　出处：《中南大学》2012年硕士论文　论文类型：学位论文

【摘要】：随着近年来次贷危机和欧债危机的爆发,世界各国金融机构面临的信用风险问题日益严重,各国金融系统中的信用风险已成为人们关注的焦点。我国证券市场经过二十多年的发展取得了巨大的成就,但同时也积累了很多问题,上市公司的信用风险问题是最主要的问题之一。上市公司是证券市场的核心,是国民经济的重要组成部分,上市公司质量的高低、财务状况的好坏将直接影响我国资本市场健康有序的发展和投资者的利益。现代市场经济本质即信用经济,作为国民经济重要组成部分的上市公司,其信用风险问题不容忽视。那么,如何准确度量企业的信用风险,以及哪种信用风险度量方法适用于我国企业已成为金融机构、投资者和政府监管部门关注的焦点,同时也是学术研究领域面临的重大课题。本文采用理论和实证检验相结合的方法,以我国A股市场176家上市公司为研究对象,利用股票市场数据和公司季度财务报告数据,采用信用风险度量模型—KMV模型为计量方法对我国上市公司季度信用风险进行了度量分析。通过计算获得样本企业的违约距离后,本文首先运用四种非参数检验法对ST公司信用违约距离和非ST公司信用违约距离进行了检验。检验结果表明基于KMV模型计算所得到的ST公司与非ST公司之间的违约距离有显著差异。利用KMV模型计算获得的信用违约距离能够很好的区分我国上市公司信用风险状况。然后,本文对各行业在2009年至2012年间的信用违约风险变动情况进行对比分析。研究发现在2009年一季度到2010年一季度之间各行业的违约风险变动情况趋于一致,从2010年第二季度开始,各行业违约风险变化趋势明显不同。最后,本文对运输与仓储业及制造业两个行业上市公司违约距离与宏观经济指标和行业财务指标均值进行逐步多元线性回归分析。研究发现运输与仓储行业上市公司的信用违约风险受利率变动因素影响显著,并与企业财务指标中速动比率变化情况存在显著相关性。制造业上市公司的违约距离与GDP变动情况、利率变动、企业经营性现金净流量(同比增长率)、速动比率变化情况存在显著相关性。由此可知,影响不同行业信用风险大小的因素有很大的差异。
[Abstract]:With the outbreak of subprime mortgage crisis and European debt crisis in recent years, the credit risk problems faced by financial institutions around the world are becoming more and more serious. The credit risk in the financial system of various countries has become the focus of attention. After more than 20 years of development, China's securities market has made great achievements, but at the same time, it has accumulated a lot of problems. The credit risk of listed companies is one of the most important problems. Listed companies are the core of the securities market, an important part of the national economy, and the quality of listed companies. The financial situation will directly affect the healthy and orderly development of our capital market and the interests of investors. The essence of modern market economy is credit economy, as an important part of the national economy listed companies. The problem of credit risk can not be ignored. Then, how to accurately measure the credit risk of enterprises, and which kind of credit risk measurement method is suitable for Chinese enterprises has become a financial institution. Investors and government regulators are also the focus of academic research. This paper takes 176 listed companies in China's A-share market as the research object, using stock market data and quarterly financial report data. Using the credit risk measurement model-KMV model as the measurement method, this paper analyzes the quarterly credit risk of listed companies in China. After calculating the default distance of the sample enterprise. In this paper, four kinds of nonparametric testing methods are used to test the distance between St company credit default and non-St company credit default. The results show that the St company and non-St company are calculated based on KMV model. There are significant differences in the default distance between companies. The credit default distance calculated by KMV model can distinguish the credit risk situation of listed companies in China. And... This paper makes a comparative analysis of the changes of credit default risk between 2009 and 2012 in various industries. The study finds that the default risk changes between the first quarter of 2009 and the first quarter of 2010. The movement tends to converge. Since in the second quarter of 2010, the trend of default risk in different industries is obviously different. Finally. This paper makes a stepwise linear regression analysis of the distance of default and the mean value of macro-economic index and industry financial index of listed companies in transport and storage industry and manufacturing industry. The risk of credit default is significantly affected by the change of interest rate. And there is a significant correlation with the change of the quick ratio in the financial index. The distance of default and the change of GDP, the change of interest rate, the net cash flow of enterprise (year-on-year growth rate) of listed companies in manufacturing industry. There is a significant correlation between the changes of the quick ratio. Therefore, there are great differences in the factors affecting the size of credit risk in different industries.
【学位授予单位】：中南大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：F832.4;F276.6;F224

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