Analysis And Forecast Of Implied Volatility

The implied volatility of options has a strong reference value and economic significance for analyzing the potential risks of the options market and the volatility of the options market in the future.The volatility index,as a more intuitive indicator of the implied volatility,represents investors' expectations of the volatility of the options market in the next 30 days.On November 28,2016,the Shanghai Stock Exchange released the Chinese IVIX.However,on February 22,2018,the Chinese IVIX stopped publishing.In this context,analyzing and predicting the implied volatility of options has strong theoretical and practical significance.From the perspective of behavioral finance,this article takes investor sentiment as an irrational factor to consider,and explores how investor sentiment affects the implied volatility of options.Then,a variety of machine learning models are introduced to convert the problem of implied volatility of options into classification problems in pattern recognition for analysis and prediction.This article constructs an investor sentiment index from two perspectives,an investor sentiment index based on traditional indicators and an investor sentiment index based on Baidu index.This article selects the three indexes of the Shanghai Stock Exchange 50 ETF option contract volume,the number of open contracts,and the subscription ratio,and uses the principal component analysis method to construct an investor sentiment index based on traditional indicators.Secondly,this article crawls the post and comment content from the Oriental Fortune 50 ETF option bar,uses text mining to select keywords,and also uses the principal component analysis method to synthesize Baidu indexes of these keywords to build investor sentiment based on Baidu index.index.On the basis of constructing the investor sentiment index,this paper uses the mixed frequency data perspective to construct daily,weekly and monthly investor sentiment at three different frequencies,and uses the MIDAS model to empirically analyze the investor sentiment index's impact on options.Including the impact of volatility.The results show that both mixed emotions have a significant effect on the implied volatility of options.Investor sentiment based on traditional indicators has an inverse effect on implied volatility of options.Investor sentiment based on Baidu index has an implicit effect on options.Volatility has a positive effect.Compared with the traditional regression model,the MIDAS model has stronger interpretation ability.After verifying that the two investor sentiment indexes are important factors affecting the implied volatility of options,this article then includes actual volatility data,historical volatility data,volatility-related indicator data,options market data,and investor sentiment.A total of 26 characteristics in this dimension are important factors affecting the implied volatility of options.Based on the data from June 8,2015 to February 14,2018,based on the research,an integrated machine learning algorithm including Logistics,SVM,and Random Forest was constructed.Based on the existing sentiment index,the implicit volatility of options was analyzed.Fluctuations were predicted.The results show that the prediction effect based on the machine learning ensemble algorithm is significantly better than the traditional GARCH model,and the prediction accuracy rate of the option's implied volatility is as high as 80.35%,which indicates that the machine learning ensemble algorithm has a better prediction ability.Finally,based on the theoretical and empirical research of the article,the article puts forward corresponding policy recommendations to investors and government regulators.