Study On The Implied Volatility Of CSI 300ETF Options

In 2013,China’s first OTC option appeared,and then on February 9,2015,the Shanghai Stock Exchange listed Huaxia SSE 50 ETF option,which marked that China’s options business has fully covered both on-exchange options and over-the-counter options.Since2019,China’s domestic options market has grown rapidly,with equity expanding SSE 300 ETF options,SZSE 300 ETF options and CICC 300 stock index options,and soybean meal options and sugar options in 2017 have also been listed on Dashang and Zhengshang,which means that China already has a full set of mainstream financial derivatives.A better understanding of the indicators and characteristics of various aspects of options can better meet the needs of investors for risk management.The CSI 300 ETF option contract selects the Huatai Berry CSI 300 ETF(symbol510300),the largest tracking of the CSI 300 Index,as the target,which covers the A-share target more comprehensively.Therefore,this article starts with the CSI 300 ETF option contract and conducts an in-depth study of implied volatility,the "alternative" of option prices.Exploring implied volatility has two market characteristics: the smile volatility curve and the term structure of implied volatility.Then,the implied volatility surface is constructed by third-order spline interpolation method fitting,and the changes of the implied volatility surface in different trading days are studied.Whether it is the implied volatility surface,the implied volatility smile curve or the term structure of implied volatility,it is a set of linear and inseparable data,so if the traditional linear dimensionality reduction method is used,the nonlinear characteristics of the data cannot be extracted,and the application to multi-dimensional implied volatility data has certain limitations.This thesis mainly applies the nonlinear dimensionality reduction algorithm to implied volatility,and compares it with the traditional linear dimensionality reduction algorithm,that is,principal component analysis,isometric feature mapping algorithm(ISOMAP),t-distribution random neighbor embedding algorithm(TSNE)and kernel principal component analysis are used to reduce the dimensionality of data from the changes of implied volatility term structure,implied volatility delivery structure and implied volatility surfaces,respectively.Finally,the empirical results of this thesis show that for implied volatility data,nonlinear dimensionality reduction technology is more effective than traditional linear dimensionality reduction,and after comparing several dimensionality reduction effects,it is found that the kernel principal component analysis has the best dimensionality reduction effect,and the isometric feature mapping(ISOMAP)dimensionality reduction effect is second,indicating that the kernel principal component can explain more nonlinear information in the data.By comparing the effects of various dimensionality reduction methods,this thesis finally selects the optimal nonlinear dimensionality reduction technique as the optimization method for implied volatility data,which is of great significance for the analysis and application of implied volatility data.The empirical conclusion of this thesis aims to provide reference experience for the optimization of implied volatility data,because the data after dimensionality reduction of kernel principal components can contain more nonlinear information in the original,so replacing the original data with the reduced data will have better results in other data studies on implied volatility(such as the prediction of implied volatility surfaces).