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论文编号：	10028
作者编号：	2120162730
上传时间：	2018/6/6 23:33:38
中文题目：	我国上市银行的房地产信贷风险实证研究
英文题目：	An Empirical Study on Real Estate Credit Risk of Listed Banks in China
指导老师：	齐岳
中文关键字：	房地产信贷；商业银行；CPV模型；信贷风险管理
英文关键字：	Real estate credit；Commercial bank；CPV model；Credit risk management
中文摘要：	我国经济进入新常态，也进入了从高速增长向高质量发展的新阶段，而房地产行业是我国国民经济的重要支柱，不仅作为人们生产生活所必需的物质资料，也吸引了更多投资资金。房地产行业易受宏观环境的影响，房地产企业属于资金密集型产业，资金来源大多依赖银行贷款。并且房地产企业对于银行贷款业务有很大的支撑作用，然而银行信贷风险的管理不容忽视。我国商业银行信贷管理方面还有很多欠缺，针对房地产市场的研究也不够深入，因此本文通过对商业银行房地产行业的信贷风险进行研究，有重要的现实意义。 本文的研究目的是提供一套有效测量房地产行业银行违约率的模型，在一定程度上对银行信贷风险进行预测和控制。通过介绍现代国际上常用的四种银行信贷风险度量模型，分别是KMV、CR+、CM、CPV模型，对这四种模型的优缺点进行对比分析，最终根据我国金融市场的发展状况选取了CPV模型进行实证分析。根据CPV模型的基本思想，即银行信贷风险与宏观经济变量存在联系，通过综合宏观经济指数建立函数关系从而计算银行信贷违约率。主要是从国家方面、银行管理以及房地产行业这三个维度，选择出8个宏观经济指标作解释变量，22家上市银行各个季度的房地产不良贷款率作为被解释变量，选取2009年第1季度至2017年第2季度共683组有效数据。通过多重共线性筛选，最终选取5个解释变量进行面板回归分析，即宏观经济一致指数、国房景气指数、一至三年期贷款利率、100美元兑人民币汇率、货币供应量M2。 通过实证分析结果得出结论，房地产行业的银行信贷违约率与宏观经济系数和经济波动有着密切的关系，也在一定程度上验证了CPV模型的合理性，可以用来衡量房地产行业银行信贷风险。根据实证结果提出房地产的银行信贷风险防范措施，从宏观经济层面、房地产信贷风险预警、商业银行针对房地产重点监控等方面提出具体措施。
英文摘要：	As China's economy has entered the new normal, it has transformed from high-speed growth to high-speed development, and the real estate industry is an important pillar of our national economy. It not only serves as a necessary material for people's production and life, but also attracts more investment funds. The real estate industry is vulnerable to the macro-environment, and real estate companies are capital-intensive industries. Most of the funding sources depend on bank loans. Moreover, real estate companies have great support for bank loan business. However, the management of bank credit risk cannot be ignored. There are still many shortcomings in the credit management of commercial banks in China, and the research on the real estate market is not deep enough. Therefore, the study of the credit risk of commercial banks in the real estate industry has important practical significance. The purpose of this paper is to provide a set of models for measuring bank default rates in the real estate industry, and to predict and control bank credit risks to some extent. By introducing the four types of bank credit risk measurement models commonly used in modern international, namely KMV, CR+, CM and CPV models, the advantages and disadvantages of these four models are compared and analyzed. Finally, the CPV model is selected based on the development of China's financial markets. conduct empirical research. According to the basic idea of the CPV model, that is, there is a link between the bank's credit risk and macroeconomic variables, and the bank's credit default rate is calculated by establishing a functional relationship through an integrated macroeconomic index. Mainly from the three dimensions of the country, bank management, and real estate industry, eight macroeconomic indicators were selected as explanatory variables. The non-performing loan ratio of 22 listed banks in various quarters was used as the explanatory variable, and the first quarter of 2009 was selected. There were a total of 683 valid data for the second quarter of 2017. Through multicollinearity screening, we finally selected five explanatory variables for panel regression analysis, namely macroeconomic consensus index, national housing prosperity index, one to three-year loan interest rate, 100 US dollar against RMB exchange rate, and money supply M2. Through empirical analysis results, it is concluded that the bank credit default rate of the real estate industry is closely related to the macroeconomic coefficient and economic fluctuations. It also verifies the rationality of the CPV model to some extent and can be used to measure the credit risk of real estate industry banks. According to the empirical results, the real estate bank credit risk prevention measures are proposed, and specific measures are proposed from the aspects of macroeconomics, real estate credit risk early warning, and commercial banks' focus on real estate monitoring.
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