China's Stock Market Risk Measure Based On Mcmc Extreme Value Theory In Applications

The changing largely in stock market will produce large loss, and the institutional investors can not carry on these loss. Although the frequency of extreme loss in stock is small, the loss is large. So, the extreme loss can be called catastrophe. It is for the risk management of catastrophe to know their distribution. The usual distribution, such as normal distribution, student-t distribution, can be used to analyze usual data and can not measure catastrophe. So, we have to find right distribution to analyze the catastrophe in order to avoid the extreme data's influence.Extreme statistics is an important branch of the main events on extreme cases of random statistical regularity. Extreme Value Theory has a wide range of application in many fields. There are mainly two types of commonly used model:BMM model and GPD model. The second model is discussed in this paper which analyzes the nature of the generalized Pareto distribution and describes the application of the model. The paper uses different methods to estimate parameters and various risk indicators, at the same time conduct a comparative analysis. In this paper, we fit the loss data of Shanghai Stock index in China by POT model to determine the distribution form of excess loss.The characteristic of catastrophe is low frequency and high severity, so the most challenge is the lack of adequate loss data. This paper makes use of the POT Model, which proved to be effective in extreme loss measurement, to deal with the loss distribution of catastrophe risk under the Loss Distribution Method framework. A MCMC model with Gibbs sampling is established to estimate the parameters required in POT.