| Since the reform and opening up,China's financial and securities marketshas been developed by leaps and bounds,and more and more citizens put their moneyinto the market.They are happy or sad because the stock market's ups ordowns.Ordinary investors like volatility of the stock market,and they areworried about volatility of the stock market.For investment institutions andlarge shareholders,stock market volatility is an important basis for them toescape from the risk.For market regulators,stock market volatility has alsobeen an important basis for the effectiveness of market supervision and animportant measure for market policy.Thus volatility modeling and descriptionis the focus of everyone and it has great significance,and the calculation ofthe volatility is the model of VaR'basis and foundation.Therefore,based on gray model and Generalized Auto Regressive ConditionalHeteroscedasticity model and in order to describe the market volatility,Ipropose the metabolism Gray-GARCH mixed model.The previous findings show thatGARCH model's residuals has been changed over time by gray factors,such as pastprice fluctuations and information factor.And this affects the result of GARCHmodel.The gray model has many good characteristics which includes of using smallamount of data that can find a good model and being good at describe grayfactors.Based on the good characteristics we build Gray-GARCH mixed model,anduse it describe volatility.Because this model is the combination of metabolismGM and GARCH model,it has both advantage of the metabolism GM and GARCH model.Andthat is why we can it metabolism Gray-GARCH mixed model.First,this paper introduces the development and the model construction oftime series model and gray model.Then the metabolism Gray-GARCH mixed model wasestablished in the paper.By using Eviews and Matlab,this paper analysis Dow JonesChinese88index data.The results shows that the metabolism Gray-GARCH mixedmodel is closer to the actual market and describes volatility more accuratelycompared with GARCH model,and it establishes a reliable basis and data assurancefor the accuracy of VaR model. |
