The Measurement Of Long-Term Risk Of Commercial Banks And The Analysis Of Macroeconomic Factors

In the background of financial liberalization, financial deepening and financial open international, banking institutions as the core of financial system, the links between the various agencies increasingly close. The infiltration and infection of risk between banking institutions have also significantly accelerated. What’s more, the individual banks or local risk are easily trigger a domino effect throughout the banking system, which enhanced the systemic risk level of banking. At the same time, a sudden outbreak of bank risk also could become the major fuse to trigger the financial industry crisis.Therefore, how to effectively measure and to avoid the banking risk, and to reduce the risk of impact on the real economy has become one of the important current issues of financial risk control and financial regulators. The primary task of this research is how to achieve accurate measurement and effective early warning of the bank’s risk.In this paper, we comb numerous domestic and foreign literature which is about the measurement and warning of the systemic risk, in this basis, we use the measurement methods of semi-parametric to simulate the composition of long-term risks and short-term risk of Chinese listed commercial banks.In addition, we have systematic study the relationship between the long-term risk of financial index and macroeconomic.Firstly, based on the semi-APARCH model, this paper uses the smooth volatility trend of yields to simulate the constitution of long-term cumulative risk, and through a variety of deformable models of ARCH or GARCH to analysis the conditional dynamic risk, which is short-term risk. By separating the long-term risks and short-term risks, the model improve the effectiveness of risk monitoring. In the empirical part of this paper, we use the stock daily changes price data of Chinese 16 listed commercial banks, financial index and banking index for the study, and measure the constitution of long-term risks and short-term risks for commercial banks, banking and the financial industry. In addition, the paper mainly use the scale function (Scale function) to measure the long-term risks of commercial banks, the study found that we can use the high limit of this function to strengthen the warning of bank risk.Secondly, based on the VAR model, this paper analyzes the relations between the long-term risk and macroeconomic variables. By selecting GDP growth, CPI growth rate, the weighted average interbank interest rate, money supply growth and real estate price index of the five macroeconomic indicators, this paper construct vector autoregression model, and interpret the macroeconomic reason which may cause the fluctuations of long-term bank risks. Meanwhile, this paper uses the impulse response to analysis the level of impact and duration on long-term risk of banks when there is an attack of key macroeconomic variables.Finally, the empirical results show that:Firstly, during the financial crisis (2008-2009), the risks of Chinese listed commercial banks, banking and financial sector are all at historically high levels; Secondly, in the post-crisis period, Chinese Long-term risk of listed commercial banks dropped significantly, and gradually returned to a lower level and remained relatively stable; Thirdly, in 2013, the risk of banking sector rebounded, and there is a tendency to rise further, for which related banks and regulators should be strengthened early warning and risk prevention; Fourthly, the leverage of commercial bank short-term risk is not significant, but the t distribution preached significant fat tail; Fifthly, the level of correlation between scaling functions which is on behalf of long-term risk of the commercial banks is high. The correlation coefficient is almost close to 1, which further validates the significant system correlation between banking institutions. Sixthly, Granger causality test found that in the 5% significance level, standardization of industrial output growth, money supply growth and real estate price index are the main reasons for the change of long-term risk of financial index.