A Research On Integrated Risk Of Chinese Commercial Bank

Today, financial markets showed unprecedented complex and changeable, the Chinese economy continued downward, the Internet banking got the better development, complex situation makes the development of commercial banks filled with unknown challenges. In order to respond to the wave of financial reform, commercial banks began to expand new services. However, with the increasing expansion of business, types of risks faced by commercial banks present a diverse and complex trends, the requirements of risk management is higher than before. In China, our risk management focused on credit risk. But the current economic makes the risks faced by commercial banks are no longer credit risk, Basel II said we need to strengthen the control of the market risk, operational risk, credit risk, liquidity risk. At the same time, we should also take the links between the risks into account. Moreover, in our country, the commercial banks are the core of the financial system, once any risk outbreaks, the steadiness of the financial system will breakdown. Therefore, in this complicated background, it is necessary to measure the integrated risk of commercial banks in China with the relationship of different risks in mind.The paper is arranged as followed, firstly, we presented the papers and research related with topic. In the second chapter, we mainly focuses on the Copula function and VaR method. In the third chapter, we define the concept of these two types of risk and the corresponding selected indicators. Select the credit as credit risk premium indicator, and select the Daily Stock residual as a proxy for market risk. We have to say these two indicators solve the shortness of the banks financial data.In order to verify the view of this paper, we take 11 listed banks as the sample banks, first calculate the marginal distribution function of credit risk and market risk of each bank. And combine the two types of risks with Copula function. Then use Monte Carlo simulation to calculate VaR values with the weight change. Finally, give some suggestion about the development.We list some meaningful conclusions as following. First, the credit risk of 11 commercial banks are non-symmetry and Bias, market risk obey a fat tail of marginal distribution, and GARCH(1,1) fits the data well. Secondly, after the integration of the 11 commercial banks risk, we find most banks are fitting t-Copula function. After screening out the VaR values for different combinations of weights, the results showed that using simple linear aggregation method will overestimate the integration risks.At the same time, the risk characteristics of each bank is not the same, only four banks is mainly faced the credit risk, and the rest bank showed the specific risk integrated risk value. Compared with other literatures, the paper may get some innovations, (1) the indicators of the two risks can solve the shortness of the financial data. (2) Considering the heterogeneity among banks, we have the different solution for each commercial banks. Despite these achievements, there are many shortcomings, such as (1) we did not measure the optical risk (2) the Copula function we used is too simple, (3) the programming for the calculation should be improved.