GARCH模型实验-时间序列要点

1、金融时间序列分析探究中国A股市场收益率的波动情况基于GARCH模型- -C(2)0.1090350.0224114.8652770.0000C(3)-0.0140340.010169-1.3799750.1676C(4)0.9672610.01657358.364330.0000R-squared-0.000066Meandependentvar0.010480AdjustedR-squared0.000979S.D.dependentvar1.292140S.E.ofregression1.291507Akaikeinfocriterion3.328490Sumsquaredresid159

2、6.268Schwarzcriterion3.348819Loglikelihood-1588.682Hannan-Quinncriter.3.336233Durbin-Watsonstat2.020182由Y系数C（3）不显著，因此不能利用非对称模型对样本数据进行估计。表4.12ARCH-M模型的估计结果DependentVariable:RMethod:ML-ARCH(Marquardt)-NormaldistributionDate:12/16/14Time:22:31Sample:1957Includedobservations:957Convergenceachievedafter1

3、0iterationsPresamplevariance:backcast(parameter=0.7)GARCH=C(2)+C(3)*RESID(-12+C(4)*GARCH(-1)VariableCoefficientStd.Errorz-StatisticProb.SQRT(GARCH)0.0098310.0323220.3041440.7610VarianceEquationC0.0462650.0223992.0655070.0389RESID(-1)A20.0385360.0092134.1827450.0000GARCH(-1)0.9348290.01941748.144210.

4、0000R-squared0.000115Meandependentvar0.010480AdjustedR-squared0.000115S.D.dependentvar1.292140S.E.ofregression1.292066Akaikeinfocriterion3.328742Sumsquaredresid1595.978Schwarzcriterion3.349072Loglikelihood-1588.803Hannan-Quinncriter.3.336485Durbin-Watsonstat2.020552均指项不显著，因此不考虑ARCH-M模型，也不考虑该模型与推广其他G

5、ARCH模型的结合形式。综上可知，GARCH（1，1）效果最好，故在此选择GARCH（1，1）模型。这也说明沪深300指数的收益率影响中不存在非对称性。4.6模型的预测选择GARCH(1,1)模型进行预测，预测结果如下。RF1?2RF1?2S.EForecast:RF1Actual:RForecastsample:930960Includedobservations:28RootMeanSquaredError2.061061MeanAbsoluteError1.503282MeanAbs.PercentError100.0000TheilInequalityCoefficient1.000000BiasProportion0.180765Va