影響我國住房抵押貸款提前還款因素的實證研究
[Abstract]:In recent years, with the rapid development of China's real estate industry and the accelerated process of housing system reform, the housing mortgage loan in China has been developing rapidly. At the same time, as a new bright spot of international financial innovation, (MBS) is a breakthrough point for further deepening the development of housing finance in China. In the process of issuing MBS, it is always one of the core problems to reasonably determine the value of the housing mortgage loan as the underlying asset, and the default risk brought by the borrower's prepayment behavior directly affects the cash flow of the asset pool. Accurate study of prepayment factors is an important prerequisite for the study of MBS. It is urgent to strengthen the research on the prepayment risk of housing mortgage loan. This paper intends to analyze the factors that affect the advance payment of mortgage loans in China by empirical research. First of all, this paper introduces the domestic and foreign factors affecting the prepayment of housing mortgage loans, including the factors affecting prepayment, the relevant model and the risk of prepayment. Then, the sample data of 2000 housing mortgage loans collected by a commercial bank are analyzed, and the quantitative analysis method is introduced to find the main factors affecting prepayment by factor analysis, discriminant analysis and other multivariate statistical methods. After the study, we found that the factors that affect the prepayment of housing mortgage loans in China include interest rate factor, mortgage maturity credit ratio factor, prepayment amount factor, borrower gender factor. Borrower's economic condition factor and borrower's age and marriage factor. Then, through the data collected by discriminant analysis and logical regression model to carry out empirical analysis of the above factors, according to the results of the software calculation, It is effective to construct statistical model to predict prepayment risk. The empirical results show that by constructing statistical model and choosing appropriate explanatory variables, we can not only predict the risk of prepayment, but also predict the risk of prepayment. Furthermore, we can grasp the contribution of each explanatory variable to the prepayment risk according to the coefficient in the model, which can provide some reference for the selection of asset pool for the subsequent implementation of mortgage securitization. Finally, this paper puts forward some preventive suggestions on the risk of mortgage securitization in advance, hoping to promote the further development of housing finance in China.
【學(xué)位授予單位】:華南理工大學(xué)
【學(xué)位級別】:碩士
【學(xué)位授予年份】:2016
【分類號】:F299.23;F832.4
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