中國(guó)銀行業(yè)信用風(fēng)險(xiǎn)控制對(duì)宏觀經(jīng)濟(jì)的影響.pdf

1、湘潭大學(xué)碩士學(xué)位論文中國(guó)銀行業(yè)信用風(fēng)險(xiǎn)控制對(duì)宏觀經(jīng)濟(jì)的影響姓名：許曉娜申請(qǐng)學(xué)位級(jí)別：碩士專業(yè)：數(shù)量經(jīng)濟(jì)學(xué)指導(dǎo)教師：王鍵;龔志民20080516II Abstract Credit risk is one of the main risks for commercial bank. In the United States，during the period of economic prosperity from 1993 to 1998

2、，the default rate of Enterprise Credit was lower. After 2000, American economy entered into a recession period, and the default rate increased significantly. This shows that the pro-cyclical of commercial banks makes it

3、over credit delivery during economic prosperity. So all kinds of credit risk often are shown in an economic recession and ultimately lay some hidden dangers to the unstable financial system. This instability will be brou

4、ght the whole macroeconomic directly to recession. All kinds of Credit risk are often shown in the economic recession to aggravate the macroeconomic volatility. On the contrary, in times of economic recession, in order t

5、o stimulate economic growth, the central bank will adopt an expansionary monetary policy. However, as a result of over-estimated to credit risk of commercial banks, they will reduce the credit, which also reduces the eff

6、ect of the expansion policy. This will cause the entire macroeconomic further recession. With the acceleration of the world economic integration trend and the end of the transition period for China's accession to the

7、 WTO, China's banking industry will face more intense competition. In this context, it is very important to strengthen research about the relationship between credit risk and macroeconomic. This article uses the the

8、ories information economic and options to analyze credit risk in China. In empirical analysis, this article uses the credit risks of the two main models: Multiple Discriminant Analysis and Principal Component Logistic re

9、gression model to estimate and forecast corporate default probability. A sample is from China's two stock exchanges in Shanghai and Shenzhen from the second quarter of 2007 to the first quarter of 2008, including 50

10、ST and 50 non-ST companies, and covered 18 industries, 25 provinces and cities. Then this paper also used the financial data of 50 ST and 50 non-ST companies in the Shanghai and Shenzhen Stock Exchanges from 1994 to 2007

11、 to get the time-series data of default rate, and at the same time this paper used the GDP data from 1994 to 2007 as the study samples. Then I study default rate impact on GDP using SVAR model, and finally conclude: GDP

12、growth rate and default rate is not strictly a change in the opposite direction in consecutive years. The average default rate and an average GDP growth rate is changing in the opposite direction, but also from the imp