Based On Copula Function And Es High-end Commercial Banks' Liquidity Risk Measure

Since 2007, the banking of China opened to the whole world which brings the fierce competition and a full range of challenges. And because of the frequent outbreak of financial crisis and its strong contagion effect, the liquidity risk faced by China becomes worsen. Therefore, measurement and management of liquidity risk in commercial banks is imperative. Currently the managers of banks generally use the simple method of static indicators to control the liquidity risk, or use the VaR and L-VaR model to measure it. Whereas, these methods would affect measuring effectiveness result of the nature defects in some extreme environments, such as the financial crisis risk; and would also ignores the correlation between risk factors which may lead to overestimate the liquidity risk of banks.Considering the above problem, I divide the liquidity of banks into two parts, internal and external liquidity risk. And use different models to measure them. First, for the bank's internal liquidity risk measuring, I construct the POT-ES(n) model to measure it. The POT model just fits the tail distribution of the sample data, that solves the handicap of estimating the whole distribution; meanwhile, ES(n) is a even more excellent measure tool to ensure the consistency of the generalized monotone stochastic dominance. As a result, this model is more appropriate to measure the liquidity risk of banks in extreme cases. And also the results of back test show that the value at risk based on such model performs more effectively.For the external liquidity risk measurement, I introduce the copula theory to measure the correlation between risk factors. At the same time, I use the nonparametric kernel density estimation model to fit the marginal distribution. At last, I combine the Monte Carlo simulation to estimate the VaR and ES of liquidity risk based on Copula—Kernel model, and also received the optimal ratio of financing in commercial banks, in order to supply a theorical model for liquidity risk management. After selecting the optimal model, we use the estimated model and other method to analysis the liquidity risk of China's commercial banks based on the Chinese banking sector as well as major commercial banks. The results show that the liquidity risk faced by overall China's commercial banks still keep at a higher level, especially the state-owned commercial banks.