我国房地产上市公司信用风险管理的实证研究—银行对信用风险的度量

发布时间：2018-02-15 03:04

本文关键词： 信用风险 KMV模型违约距离　出处：《西安工程大学》2012年硕士论文　论文类型：学位论文

【摘要】：房地产行业存在着诸多问题，被比喻为癌症患者，我国正在一步步对其进行化疗，虽然没有下猛药，但是给予了全方位的诊断治疗控制，自2009年以来更是加大了控制力度，力图将这一顽症治疗好。在治疗房地产顽症的过程中，控制银行贷款无疑是一剂良药，所以我国也出台了许多关于银行贷款的政策法规，我国的金融机构把思想集中在怎么样才能在我国银行业对房地产上市公司的信用风险进行真真正正有效的管理。而想要参透这一问题，就要关注信用风险的度量方法，并对其有所创新。本文主要是参考国内外对信用风险以往的研究文献和成果，对房地产上市公司的信用风险借助于已经修正了的KMV模型进行了度量研究，同时，在此基础上运用聚类分析方法深入的研究了我国的房地产行业违约距离DD值的有效控制区间，通过已有的实证基础，在我国信用风险管理方面提出了几点建议，以期适用于我国信用风险管理的度量模型早日形成并运用于实践。本文首先对信用风险的相关理论和度量模型进行了介绍，详细分析了现代信用风险度量模型，并对四种现代信用风险度量模型的优缺点进行了比较分析，结果显示：美国创立的KMV模型很适合在我国进行研究；然后利用修正过一些计算方法的KMV模型对研究对象中国的房地产行业做了实证研究，实证分析得出，在信用风险大小的识别能力方面KMV模型表现得非常好；最后运用聚类分析法探讨了房地产行业违约距离的合理控制范围，以使银行在信贷过程前后能及时识别房地产公司的信用风险状况，第一时间做出回应，以免房地产公司发生违约给商业银行带来损失，这是本文研究的经济意义所在。在对当前我国的商业银行行业运用信用风险度量模型存在的制约和困难进行一些探讨的基础上，本文对如何构建规范、有效、可行的信用风险度量模型进行了深入思考，并提出了相应的修正方法，按照本文的研究成果，修正后的KMV模型很适合在我国度量信用风险的大小，，在此基础上分析出违约距离的合理控制范围和违约距离的临界值，这是本文的两个创新点。
[Abstract]:There are many problems in the real estate industry, which are compared to cancer patients. Our country is taking chemotherapy step by step. Although there is no strong medicine, it has been given comprehensive diagnosis and treatment control, and since 2009, it has increased its control. Trying to treat this stubborn disease well. In the process of treating the stubborn disease of real estate, controlling bank loans is undoubtedly a good medicine, so our country has also issued a lot of policies and regulations on bank loans. The financial institutions of our country concentrate their thoughts on how to manage the credit risk of listed real estate companies in China's banking industry. If we want to understand this problem, we should pay close attention to the measurement method of credit risk. This paper mainly refers to the domestic and foreign research literature and achievements on credit risk, and measures the credit risk of listed real estate companies by means of the modified KMV model, and at the same time, the paper makes a research on the credit risk of real estate listed companies with the help of the modified KMV model, at the same time, On this basis, the paper deeply studies the effective control interval of DD value of default distance in China's real estate industry by using cluster analysis method, and puts forward some suggestions on credit risk management through the existing empirical basis. It is expected that the measurement model suitable for credit risk management in China will be established and applied in practice as soon as possible. This paper first introduces the relevant theories and measurement models of credit risk, analyzes the modern credit risk measurement models in detail, and compares the advantages and disadvantages of four modern credit risk measurement models. The results show that the KMV model established by the United States is very suitable for the study in China, and then the real estate industry in China is studied by using the modified KMV model. The KMV model is very good in the recognition of credit risk. Finally, the reasonable control range of default distance in real estate industry is discussed by cluster analysis. In order to enable banks to identify the credit risk of real estate companies in time before and after the credit process, and respond to the situation in the first time, so as to avoid the real estate companies from defaulting on commercial banks, which is the economic significance of this study. On the basis of discussing the constraints and difficulties existing in the application of credit risk measurement model in the commercial banks of our country at present, this paper makes a deep thinking on how to construct a standardized, effective and feasible credit risk measurement model. According to the research results of this paper, the modified KMV model is very suitable to measure the credit risk in China. On this basis, the reasonable control range of default distance and the critical value of default distance are analyzed. These are two innovative points of this paper.
【学位授予单位】：西安工程大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：F832.4;F299.23;F224

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