Financial Data Analysis Based On Model Averaging Methods

Descriptive tasks and predictive tasks are two major tasks in the analysis of financial data.The so-called descriptive tasks are to generate a description of the relationship of data based on multivariate statistical analysis methods and predictive tasks are generally the number of things on the analysis,usually through the establishment of the model of the actual data structure approximation to the known explanatory variables of the observed value of the target variable to predict the future value.The latter task is the most basic problem in financial data analysis.For the prediction problem,the traditional financial data analysis methods mainly include time series analysis methods and regression analysis methods.Although both of them can predict the future data by establishing the model,the uncertainty in the model selection process tends to increase the forecast model bias.Model averaging method has a wide range of applications,which can provide important technical support for forecast analysis,avoid risk and bias,is the effective method of studing financial data.Model averaging method is a method to estimate the prediction or prediction from different models by averaging the weight to minimize the prediction error.As a complex data analysis method,it can effectively make up the process of model selection and reduce the risk of estimation or forecasting,the combination of weight selection is the key issue of this approach.In this paper,we mainly study the financial data based on the frequentist model averaging method.First,we briefly introduce several model selection criteria and the evaluation index of models and several traditional financial data analysis methods and their characteristics,then respectively in the infinite order autoregression model,autoregression conditional heteroskedasticity model and linear covariance model based on the model averaging method to analysis financial data to get the ideal results.