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GARCH模型的参数.doc

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GARCH=C(1)+C(2)*RESID(-1)^2+C(3)*GARCH(-1)VariableCoefficientStd.Errorz-StatisticProb.VarianceEquationC1.31E-064.16E-073.1433690.0017RESID(-1)^20.0589560.0064079.2024130.0000GARCH(-1)0.9333690.007214129.37570.0000R-squared-0.001087Meandependentvar0.000421AdjustedR-squared-0.000267S.D.dependentvar0.012759S.E.ofregression0.012760Akaikeinfocriterion-6.110837Sumsquaredresid0.198649Schwarzcriterion-6.098279Loglikelihood3730.610Hannan-Quinncriter.-6.106110Durbin-Watsonstat2.092936 对GARCH（1,1）模型做出的LM检验，发现F统计量和卡方统计量都大于0.05，所以不存在ARCG效应 HeteroskedasticityTest:ARCHF-statistic1.628845Prob.F(1,1217)0.2021Obs*R-squared1.629342Prob.Chi-Square(1)0.2018 下面是GARCH(1，2)和GARCH（2,1）模型阐述估计 GARCH（1,2） GARCH=C(1)+C(2)*RESID(-1)^2+C(3)*GARCH(-1)+C(4)*GARCH(-2)VariableCoefficientStd.Errorz-StatisticProb.VarianceEquationC7.87E-073.87E-072.0361990.0417RESID(-1)^20.0339050.0139222.4354280.0149GARCH(-1)1.4755680.2173026.7904050.0000GARCH(-2)-0.5137710.202024-2.5431240.0110R-squared-0.001087Meandependentvar0.000421AdjustedR-squared-0.000267S.D.dependentvar0.012759S.E.ofregression0.012760Akaikeinfocriterion-6.113214Sumsquaredresid0.198649Schwarzcriterion-6.096471Loglikelihood3733.061Hannan-Quinncriter.-6.106912Durbin-Watsonstat2.092936 GARCH（2,1）模型 GARCH=C(1)+C(2)*RESID(-1)^2+C(3)*RESID(-2)^2+C(4)*GARCH(-1)VariableCoefficientStd.Errorz-StatisticProb.VarianceEquationC1.67E-065.31E-073.1440960.0017RESID(-1)^20.0037690.0169180.2227910.8237RESID(-2)^20.0738540.0192683.8328910.0001GARCH(-1)0.9139570.00955495.658410.0000R-squared-0.001087Meandependentvar0.000421AdjustedR-squared-0.000267S.D.dependentvar0.012759S.E.ofregression0.012760Akaikeinfocriterion-6.115724Sumsquaredresid0.198649Schwarzcriterion-6.098981Loglikelihood3734.592Hannan-Quinncriter.-6.109422Durbin-Watsonstat2.092936 EARCH模型 LOG(GARCH)=C(1)+C(2)*ABS(RESID(-1)/@SQRT(GARCH(-1)))+C(3)*RESID(-1)/@SQRT(GARCH(-1))+C(4)*LOG(GARCH(-1))VariableCoefficientStd.Errorz-StatisticProb.VarianceEquationC(1)-0.2006500.036753-5.4593420.0000C(2)0.14222