Abstract: Concerning financial data's fat-tail,volatility clustering and asymmetry, we raise two data-driven models: E-GAS-AST model and E-GAS-AST-GPD model,and proceed risk measuring and backtesting with real data. Based on generalized autoregressive score(GAS) model,combining asymmetric student-t (AST) distribution with heavy tail,we propose an new model denoted by E-GAS-AST referring to EGARCH model.Considering describing more of tail features,we propose another E-GAS-AST-GPD model with generalized pareto distribution (GPD).Afterwards,the paper computes VaR and ES by studying distributions of residuals,and backtests them separately.Introducing parameter-driven models,such as semi-parameter generalized autoregressive conditional heteroskedasticity(GARCH) model,EGARCH-t model and GJR-GARCH-t model to produce risk measurement we compare them with two above models proposed.Empirical analysis using Dow Jones Index and Shanghai Stock Exchange Composite Index concerning change point reveals E-GAS-AST model is proper to model financial market and measure risk.