Research On Theory And Method To The Measure Of Risk To The Second Board Market Of China

In recent years, with China’s accession to the WTO, the marketization of interest rate, capital account liberalization and the financial derivatives market is established, facing the market risk of financial assets will be increasingly prominent and complex.But along with the economic globalization and financial liberalization, competition and deregulation and financial innovation and technical progress factor, financial market efficiency is obviously improved, and the market dimensions expands quickly also, at the same time the volatility of financial market and the gradual increase in risk and increasingly apparent. The VaR method is currently the world’s widely used risk measure method. It is becoming the financial risk management of international standard. VaR method of measuring risk characteristics with quantitative, comprehensive, popular, because of this, many banks, financial institutions and regulatory bodies are widely used it. This method will be introduced into China’s securities market risk management is of great realistic significance.For Chinese securities market, there is no method at different confidence levels on efficient and accurate estimation of VaR. Based on the VaR estimation method on the basis of comparison and analysis, some of these methods are improved, and the gem ’s day yield data of the index as a sample, drawing on a wide range of domestic and foreign research results, the GEM market risk measure.The article uses the research technique mainly is empirical and comparative research methods. By using these two methods on Chinese stock market risk analysis, In order to gain the different confidence level VaR value, Then measure of the GEM market risks.If you want to compare the accuracy of estimation of VaR, we need to find a suitable model. This model can better fitting series of return rate distribution. Therefore, in this paper, We first analyze statistical characteristics and distribution of the Second board Market, and then select a more reasonable VaR estimation model based on the analysis of the conclusion. The conclusion of research is that through empirical analysis of normality, autocorrelation and ARCH effect, we found that the Chinese stock market returns series has thick tail peak, weak correlation, volatility clustering and other characteristics.Estimation of VaR value in many ways, this paper uses several commonly used estimation method to estimate VaR, such as normal method, GARCH method, historical simulation method and extreme value theory. And then the application of Kupiec failure returns test method on the VaR validity validity test.The results of empirical analysis shows In various confidence levels, using a simple average normal method to estimate VaR are invalid. Using extreme value theory to estimate VaR, It is invalid at high confidence level. While using the historical simulation method and GARCH model to estimate the VaR at lower confidence level is effectively, and invalid in the high confidence level. Therefore, apply these models directly to estimate the value of VaR and can’t get at various confidence levels are valid VaR values, so in this paper we from two aspects of the estimated VaR model is improved.When we use the POT model of extreme value theory to estimate VaR, usually assuming supra-threshold Meet the conditions of independent identical distribution, But in practice, the threshold is often topically related. The result of this calculation will make VaR estimation value and actual value compared with a relatively large deviation. VaR estimation value and the actual value with relatively large deviation. We can use two methods to eliminate the local correlation of exceeding the threshold. One is introduce extreme value index to the POT model. Introduction of extreme value index of improved POT model to estimate the effectiveness of VaR. At the same time, improved the accuracy of POT model estimation. It also leads to the conclusion, on the lower level of confidence, even in the POT model introduced in the extreme value index, the estimation of VaR is invalid. Second method is to use the GARCH model to deal with the return series. Because of the extreme value theory only in the high confidence level effectively, in a low confidence level on its reliability than general VaR estimation method of GARCH model, so we processed the residual sequence using extreme value theory and historical simulation method hybrid method to estimate VaR value. Through the empirical analysis we found, this method estimates the VaR in various confidence levels are effective, and very close to the expected value. So we concluded that ,In different confidence levels, using the general method to estimate the risk of Chinese stock market is effective value, Correction of simple average normal and EWMA, estimates of the conditional volatility, and then estimate the VaR, could be obtained at different confidence level on effective and accurate VaR value. Or use the GARCH processed historical simulation and theory and mixed methods can also get the effective and accurate VaR estimation at various confidence levels.