Research On Prediction Of Stock Index Price Volatility Based On LSTM Fusion GARCH Model

The management and control of financial risks has always been the focus of the financial community.The volatility of financial assets is an important indicator to quantify risks and its precise prediction has become a research hotspot.At the theoretical level,the study of financial asset volatility is an important branch of the financial market discipline;at the practical level,predicting volatility and grasping its high volatility range is conducive to the country's macro-control and allows investors to avoid risks,as well as preventing trade,Investment and other fields of risk.In the actual research process,the prediction of volatility is not a small challenge.On the one hand,there are many influencing factors and the complex nonlinear relationship between different influencing factors;on the other hand,the reason is the advent of the era of big data and the application of Internet technology.Development has enabled the level of financial data to reach massive units,and the complexity and unstructured forms of data have been further strengthened.As a result,it is difficult to obtain more accurate results using traditional econometric forecasting models.Therefore,this article attempts to combine the traditional econometrics model with the deep learning model,and use the framework of the fusion model to predict the volatility of securities prices,in order to achieve reasonable parameter explanations for the econometric model part of the fusion model,and the deep learning model Non-linear processing capabilities bring the purpose of improving prediction accuracy.The thesis first summarizes the forecast history and current situation of financial market volatility,then introduces the relevant theoretical basis and volatility forecast model,and finally uses a separate model and a fusion model to conduct empirical research respectively.The process uses the Shanghai and Shenzhen 300 index and The 5-minute high-frequency closing price data of the CSI 500 Index.The specific research work and results are as follows:(1)Carry out correlation statistical test on the volatility series of the two indices,and find that the realized volatility can well describe the distribution characteristics of the true volatility,which can be used as a proxy variable;(2)Comparing the fitting prediction ability of the Realized GARCH model under different residual distribution assumptions,the results based on various loss functions prove that when the residuals are assumed to obey the t distribution or the skewed t distribution,the prediction accuracy of the model can be improved;(3)Select the optimal parameters of the LSTM model,and find that the LSTM model has better predictive ability than the Realized GARCH model;(4)After the Realized GARCH model is used to obtain the model parameters,the residuals between the fitted values and the true values in the sample are taken as The LSTM model is input,and the fusion model obtained by adding the two parts of the prediction results is better than the single model.At the end of the article,a conclusion summary and future research prospects are made.It is found that due to the data distribution characteristics of different data sets,there is no undifferentiated optimal model.Model selection and parameter settings can affect the performance of the model,which will affect future fluctuations.Rate modeling research has certain reference significance.