基于KMV模型的公司信用风险实证研究

发布时间：2018-01-14 13:41

本文关键词：基于KMV模型的公司信用风险实证研究　出处：《山东大学》2014年硕士论文　论文类型：学位论文

更多相关文章： KMV模型 信用风险 违约距离 违约率

【摘要】：随着市场的发展和金融业的扩张,“信用风险”因其传染性和可控性等特殊性质,对一个企业的生存和发展起着极大的制约作用,尤其是近年来,随着新的金融工具的迅速发展,人们对信用风险在可利用性和可预测性的要求越来越高。为了满足市场的需求,我们应当对信用风险进行更加精确的分析和更为精准的预测。如今,人们越来越多的意识到,利用数学工具尤其数学建模对信用风险进行量化分析具有很好的前瞻性和实用价值,这也是目前市场发展的主流趋势,它的发展给风险控制方向带来了革命后的改变,也让我们对违约等信用风险有了更为敏感的感知力。在现行的几个主流模型中,KMV模型因为其对市场的贴合度高,预测性好等优势,较为精准的反应了信用风险的变动,能将可能出现违约的公司用数据的方式真实显露出来,给投资者提供更好的参考意见,越来越受到行业的关注。本文套用了国外机构定义的KMV模型,但主旨倾向于利用模型研究国内不同情况的公司的信用风险。利用的是诸家上市公司的股票价格.财务报表等信息,得到了公司信用风险和违约率的具体刻画,并通过数据间的比较和整理,得到以下三个基本结论： 1、通过对KMV模型的实证研究,证实了KMV模型在我国市场中的有效性和可实用性。 2、通过对公司间资产状况和违约距离的比较,发现公司资产规模和违约距离间有正相关关系。 3、通过对不同行业公司间资产状况和违约距离的比较,发现同行业的违约距离在一段时间内的变化趋势有一定相似性,得出违约距离与行业属性有关的结论。根据以上三条结论,本文有一下建议： 1.建议各公司尤其是金融业公司加强对KMV模型的应用,更好的预测风险、规避风险。 2.建议公司投资行业多元化,对大中小型企业的投资合理分配,避免因投资过于集中而出现巨大损失。 3.建议政府加大对中小型企业的扶持力度,保障中小型企业健康发展。另外,本文还简要介绍了KMV模型的一些改良方案和几个更精确的计算方法,以备读者参考。
[Abstract]:With the development of the market and the expansion of the financial industry, "credit risk", because of its special nature of infectivity and controllability, plays a great role in restricting the survival and development of an enterprise, especially in recent years. With the rapid development of new financial instruments, the demand for the availability and predictability of credit risk is becoming higher and higher in order to meet the needs of the market. We should make more accurate analysis and forecast of credit risk. Nowadays, more and more people realize that the quantitative analysis of credit risk with mathematical tools, especially mathematical modeling, has a good forward-looking and practical value, which is also the mainstream trend of market development. Its development not only changes the direction of risk control after revolution, but also makes us more sensitive to credit risk such as default. In the current several mainstream models KMV model because of its high adhesion to the market good predictability and other advantages more accurate response to the changes in credit risk. The ability to expose companies that might default in the form of data, providing investors with better advice, is getting more and more industry attention. This paper applies the KMV model defined by foreign institutions, but the purpose of this paper is to use the model to study the credit risk of companies in different situations in China, and to use the stock price, financial statements and other information of listed companies. The specific description of corporate credit risk and default rate is obtained, and through the comparison and collation of the data, the following three basic conclusions are obtained: 1. The validity and practicability of KMV model in Chinese market are verified by the empirical study of KMV model. 2. By comparing the asset status and default distance between companies, we find that there is a positive correlation between asset size and default distance. 3. By comparing the asset status and default distance between different industries, it is found that the variation trend of default distance in the same industry is similar over a period of time. The conclusion that the distance of breach of contract is related to the property of the industry. According to the above three conclusions, this paper has the following suggestions: 1. It is suggested that companies, especially financial companies, should strengthen the application of KMV model to better predict risk and avoid risk. 2. It is suggested that the company should diversify its investment industry and allocate its investment to large, medium and small enterprises so as to avoid huge losses due to excessive concentration of investment. 3. It is suggested that the government should strengthen the support to small and medium-sized enterprises to ensure the healthy development of small and medium-sized enterprises. In addition, this paper also briefly introduces some improved schemes of KMV model and several more accurate calculation methods. For the reader's reference.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F224;F832.51

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