关于优化我国商业银行房地产开发贷款组合的实证研究

发布时间：2018-02-27 18:12

本文关键词： KMV模型 Copula函数组合信用风险房地产贷款组合优化　出处：《上海师范大学》2013年硕士论文　论文类型：学位论文

【摘要】：在金融领域中，信用风险一直是商业银行的最重要也是最难度量的风险，而房地产行业与商业银行以及其他产业关联度高、带动性强，具有先导性和基础性的特点，是衡量国民经济发展的“晴雨表"，也可能是信贷体系的潜在风险点。 2007年美国“次贷”危机及其引发的全球金融危机的根源就是房地产行业的的信用风险危机。因此，对房地产企业银行贷款的信用风险进行度量和研究是非常重要的。这就需要先度量单个房企的信用风险，然后得到非常重要的不同房企之间的违约相依性结构，最后对房地产开发贷款的组合进行优化。本文从商业银行的角度出发，以3家A股上市房企为研究对象，以2006年初到2012年三季度末的股票价格及相关财务数据为基础，首先运用KMV模型计算得出单个房企的违约距离，然后以违约距离为研究变量，采用Copula连接函数拟合3家样本房企的违约距离的相依结构。研究结果表明，3家上市房企两两之间的违约距离服从近乎相同的二元Gumbel-Copula连接函数，，这说明违约距离之间具有一定的上尾相依结构。借助Gumbel-Copula函数的性质，本文进一步构造了3家公司违约距离的三元Gumbel-Copula函数，并将其作为研究3家房企违约相依结构的经验分布函数。最后，本文试探性地运用该分布函数产生的各房企违约距离的随机数，并以每组随机数的加权平均值（权重即为贷款配置比例）定义了一个衡量组合信用风险的新变量dd，并借鉴在险价值VaR的思想来对该贷款资产组合进行了优化，得到了相对最优的贷款配置比例，例如当显著性水平为0.05时，中粮地产、渝开发和大龙地产分别获得大约20%、30%、50%的贷款。此外，随着显著性水平从0.01上升到0.1，中粮地产和渝开发获得的贷款比重也逐步上升，而大龙地产的比重逐步下降。
[Abstract]:In the financial field, credit risk is the most important and the most difficult to measure the risk of commercial banks, and the real estate industry and commercial banks and other industries have a high degree of correlation, with strong, leading and basic characteristics, It is the barometer to measure the development of national economy, and it may also be the potential risk point of credit system. In 2007, the credit risk crisis of the real estate industry was the root of the "subprime" crisis in the United States and the global financial crisis caused by it. It is very important to measure and study the credit risk of bank loans of real estate enterprises. It is necessary to measure the credit risk of individual housing enterprises first, and then get the very important structure of default dependence between different housing enterprises. Finally, the combination of real estate development loans is optimized. From the point of view of commercial banks, this paper takes three A-share listed housing enterprises as the research object, based on the stock prices and related financial data from early 2006 to 2012, and calculates the default distance of individual housing enterprises by using KMV model. Then the Copula connection function is used to fit the dependent structure of the default distance between the three listed housing enterprises. The results show that the default distance between the three listed housing enterprises is almost the same as the binary Gumbel-Copula connection function. With the help of the properties of the Gumbel-Copula function, this paper further constructs the three dimensional Gumbel-Copula function of the default distance of three companies. The empirical distribution function is used to study the dependent structure of default of three housing enterprises. Finally, the random number of the default distance of each house enterprise produced by the distribution function is tentatively used in this paper. Using the weighted average of each group of random numbers (the weight is the loan allocation ratio), a new variable, ddd, is defined to measure the credit risk of the portfolio, and the loan portfolio is optimized by using the idea of VaR at risk value. A relatively optimal loan allocation ratio is obtained. For example, when the significant level is 0.05, Cofco Real Estate, Chongqing Development and Dalong Real Estate respectively get about 20% 30% of loans. In addition, With the significant level rising from 0.01 to 0.1 the proportion of loans obtained by Cofco real estate and Chongqing development increased gradually while the proportion of Dalong real estate decreased gradually.
【学位授予单位】：上海师范大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：F832.45;F224

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