Research On Option Pricing Of Shanghai 50ETF Based On Realized Volatility In High-frequency Data

A growing number of research shows that,for option pricing models,high frequency data contains a large number of information about the underlying assets’ price movement,price movements in the quantitative business covers more than that when using low frequency data,so in the study of options,more and more scholars began to use the high frequency data,high frequency data in the study of option pricing got more and more attentions.Using intraday high frequency data to calculate volatility can achieve better results than low frequency data.The calculation of volatility is simple,no parameter estimation is required,but the effect of volatility prediction is significant.Therefore,the application of high frequency data has proved important value.High frequency data is used in this paper.Taking Shanghai 50 ETF options as samples,the model of GARV and ARV was reconstructed,and the realized volatility of China’s options market was studied.Firstly,descriptive statistical analysis was conducted on the volatility sequence of Shanghai 50 ETF,and then pricing research was conducted on Shanghai 50 ETF options,and comparison was made with the traditional model.It is expected to provide alternative solutions for quantitative investment scientific decision-making.Meanwhile,the model algorithm selected in this paper is difficult to implement,and the research in this paper is a supplement to the existing research in this field.In this paper,it is found that the distribution of the realized volatility sequence has the characteristics of sharp peak and extreme right deviation.Realized volatility sequences do not accord with normal distribution,and the realized volatility in logarithm,logarithmic realized volatility sequences showed obvious fluctuation characteristics of gathering,and the distributation has charactoristic of normal distribution.Based on the empirical pricing of Shanghai 50 ETF options in June and September respectively in the years from 2016 to 2017,by comparing the pricing trend and spread trend of the three models,it can be found that: Because of GARV and ARV model volatility is constantly changing,can automatically change with the passing of time,the use of realized volatility in option pricing,by using high-frequency data itself can get more information about asset price changes,can fix pricing errors at any time,this also means when using GARV and ARV model for option pricing,can achieve a better effect than the BSM model.