The Research On The Volatility Of High-frequency Data Extreme Based On Garch Model

Volatility is a indicator that is used to test the quality and efficiency in the financial market. If the volatility of the order of financial market can be precisely obtained, the mobility and allocation of resources of financial market will be greatly enhanced. With the globalization of economy, information contributes to the successive influence on the price of the stock market. The discrete collection of data have a direct effect on the amount of information in market obtained. The higher rate of collecting data, the more information of market will be obtained. High-frequency data is the main researching target of those who have interest in financial market. Especially the traders confirm trading strategy by observing high-frequency or point data. The research on extreme sequence makes us analyze the condition of extreme market and the influence of information exerts on stock market.The high-frequency financial time series are two time orders formed by maximum number and minimum number of different frequency data in certain time interval. It is different from the one that we studied before, which will enable us to make more exact analysis of extreme market condition and the effect that information exert on stock market.The innovation of this paper is to Study the high-frequency data and statistic features of rate of return, to describe information of market and the volatility between extreme data and its rate of return. It is of great theoretical and practical significance to the analysis of our stock market.This study researches on the congregation which is based on the high frequency data extreme of GARCH model. Take the maximum and minimum of Shanghai Shenzhen300index within15minutes in China’s stock market as the objective. GARCH modeling is applied. The first chapter is basically introducing the research status of volatility. Chapter two systematically introduces the theory of model of volatility. The main features of volatility and compare various models theoretically in order to reveal different features of every model. Chapter three mainly use the GARCH model to make model for revenue order and makes the analysis of congregated volatility, high apex and thickness tail and the asymmetry of volatility. The conclusion shows that there is obvious volatility in the stock markets of Shanghai and Shenzhen, there is high apex and thickness tail and congregated volatility in the data of rate of return itself which coincide with normal distribution. Finally, according to the results of the analysis of this study, the study puts up with the improvements on the model.