Estimate Value-At-Risk Of China’s Financial Market Based On Quantile Regression Technique

In resent years, with the development of financial globalization, The outbreak of the international financial crisis can also have a very quick impact on China’s financial industry. Banking industry, as an important subject of China’s financial industry, the impact degree of the financial crisis on it can has a direct impact on the financial development of China. As the banking industry is highly contagious, once the outbreak of the crisis, will affect the entire financial system, so it is necessary to take the perspective of various banks, in the estimation and control of their own value at risk, need to measure the marginal risk contribution of a single bank to the entire banking system at the same time, for this purpose, this paper launched a study.In this paper, we talk about the application of quantile regression to the banking system of China and China’s stock market. Firstly, Quantile regression approach is applied to estimate the VaRs of China’s stock market:the risk value of the Shanghai composite index is calculated by using different probability level, different distribution hypothesis and different ARCH models. Secondly, the CoVaR theory based on the quantile regression technique is applied to estimate the systemic risk of banking:We estimate the VaRs of the 12 banks and the marginal contribution of each bank made to the banking overall systemic risk. We make a discussion on the situation of △CoVaR:time-invariant and time-variant. And estimate the time variation conditional by introducing a series of lagged state variables St-1, which can be used to describe the characteristics of the sample data. Then we make the ranking of the VaRs、 CoVaRs and ACoVaR values of 12 banks, and give the conclusions. Thirdly, under frame of time-variant ACoVaR, the research sample is divided into two stages:the stage of crisis occur and spread, and the stage of the time after the crisis. Then estimate the bank’s risk spillover value of 12 banks at both stage and make the ranking, compare and explain the change of the rank of the bank’s Risk Spillover between the two stages.The conclusions of this paper are as follows:1) Quantile regression approach is applied.to estimate the VaRs, under different distribution and different GARCH models, the VaR results of are short different, each model can right describe the market risk of financial assets.2) The observation results show that time-invariant CoVaR value is smaller than time-variant CoVaR value, which shows that without considering the state variables, the impact of the bank’s risk Spillover effect is underestimated. By introduce such variables, the tail risk of each bank is well described.3) According to the bank’s CoVaR and ACoVaRranking, the systemic risk contribution of the 12 banks in our country is analyzed: the four major state-owned commercial banks are not only have big value of their own risk, but also have important contribution to the banking system risk, which is the industry’s excellent model, ICBC’s impact on the banking industry is particularly significant. Shanghai Pudong Development Bank, China Minsheng Bank such small and medium-sized joint stock banks’risk spillover effect size are right followed, and the risk contribution of them can not be ignored. Ningbo bank, Beijing Bank of China these two Urban Cooperative commercial banks who have the list value of risk spillover effect, but in their rapid development process, risk spillover effect to the banking industry is gradually increasing, which is a great warning to China to take risk supervision of such banks seriously.