金融数据处理(VAR)

VAR本次实验，数据为1995年1月3号至2011年11月30号的上证综合指数，对其进行风险度量。主要方法参考金融时间序列第七章的VAR以及课上的内容。其中波动率的分布有正态分布，t分布，广义误差分布，设定概率有1%,5%。第一步描述性分析K>3,说明对数收益率序列第一步描述性分析K>3,说明对数收益率序列R是尖峰厚尾的。S>0,说明R是左偏的。Series:RSample1/03/199511/30/2011Observations4104Mean0.000315Median0.000600Maximum0.269928Minimum-0.179051Std.Dev.0.018769Skewness0.402008Kurtosis18.90756Jarque-Bera43382.20Probability0.000000第二步自相关检验与单位根检验Date:12/14/11 Time:23:38Sample:1/03/199511/30/2011Includedobservations:4104AutocorrelationPartialCorrelationAC PACQ-StatProb11||10.025 0.0252.49680.114||||2-0.011-0.0123.03060.220||||30.008 0.0093.29370.349||||40.028 0.0276.48080.166||||5-0.001-0.0036.49000.261||||6-0.032-0.03110.6720.099||||7-0.004-0.00310.7280.151||||8-0.018-0.01912.0570.1499-0.019-0.01713.4990.141100.009 0.01213.8550.180在5%的置信水平下，滞后10阶的P值都不显著，即R在10阶滞后没有自相关。NullHypothesis:RhasaunitrootExogenous:ConstantLagLength:0(AutomaticbasedonSIC,MAXLAG=30)t-StatisticProb.*AugmentedDickey-Fullerteststatistic -62.46317 0.0001Testcriticalvalues: 1%level -3.4317595%level -2.86204810%level -2.567084由P值为0.0001，非常显著，拒绝原假设，即R是平稳的序列。第三步VAR结合公式VaR二P(①-1(c)b—卩)，主要解决序列r的均值u波动A0率的标准差o以及不同分布的分位数eT(c),其中P0正化为1。分位数如下：分布置信水平正态分布T分布(4.381220^GED(1.138430)0.95-1.645-2.079-1.6420.99-2.326-3.573-2.679GARCH---GUSSIANDependentVariable:RMethod:ML-ARCH(Marquardt)-NormaldistributionDate:12/15/11 Time:00:07Sample(adjusted):1/04/199511/30/2011Includedobservations:4104afteradjustmentsConvergenceachievedafter30iterationsPresamplevariance:backcast(parameter=0.7)GARCH=C(2)+C(3)*RESID(-1)人2+C(4)*GARCH(-1)VariableCoefficientStd.Errorz-StatisticProb.C0.0002750.0002051.3398710.1803

VarianceEquationC2.77E-063.64E-07 7.6042490.0000RESID(-I)人20.0803400.004435 18.115900.0000GARCH(-1)0.9191140.003082 298.25260.0000R-squared-0.000005Meandependentvar0.000315AdjustedR-squared-0.000736S.D.dependentvar0.018769S.E.ofregression0.018776Akaikeinfocriterion-5.354183Sumsquaredresid1.445387Schwarzcriterion-5.348024Loglikelihood10990.78Hannan-Quinncriter.-5.352003Durbin-Watsonstat1.949585GARCH--tDependentVariable:RMethod:ML-ARCH(Marquardt)-Student'stdistributionDate:12/14/11 Time:23:54Sample(adjusted):1/04/199511/30/2011Includedobservations:4104afteradjustments

Convergenceachievedafter13iterationsPresamplevariance:backcast(parameter=0.7)GARCH=C(2)+C(3)*RESID(-1)人2+C(4)*GARCH(-1)VariableCoefficientStd.Errorz-StatisticProb.C0.0004000.0002002.0007380.0454VarianceEquationC6.36E-061.31E-064.8728650.0000RESID(-I)人20.0989260.0115148.5914760.0000GARCH(-1)0.8893790.01066183.426870.0000T-DIST.DOF4.3812200.30804714.222570.0000R-squared-0.000021Meandependentvar0.000315AdjustedR-squared-0.000996S.D.dependentvar0.018769S.E.ofregression0.018778Akaikeinfocriterion-5.491094Sumsquaredresid1.445410Schwarzcriterion-5.483395Loglikelihood11272.72Hannan-Quinncriter.-5.488368Durbin-Watsonstat1.9495540400.36-.32-.28-.24-.20-.16-.12-.08-1996 1998 2000 2002 2004 2006 2008 2010VART10400.36-.32-.28-.24-.20-.16-.12-.08-1996 1998 2000 2002 2004 2006 2008 2010VART1——VART2EGARCH---GEDDependentVariable:RMethod:ML-ARCH(Marquardt)-Generalizederrordistribution(GED)Date:12/14/11 Time:23:58Sample(adjusted):1/04/199511/30/2011Includedobservations:4104afteradjustmentsConvergenceachievedafter31iterationsPresamplevariance:backcast(parameter=0.7)LOG(GARCH)=C(2)+C(3)*ABS(RESID(-1)/@SQRT(GARCH(-1)))+C⑷*RESID(-1)/@SQRT(GARCH(-1))+C(5)*LOG(GARCH(-1))VariableCoefficientStd.Errorz-StatisticProb.C0.0004070.0001822.2371160.0253VarianceEquationC(2)-0.3094360.040823-7.5799450.0000C(3)0.1971010.01689511.666520.0000C(4)-0.0215260.009720-2.2145880.0268C(5)0.9799430.004435220.95290.0000GEDPARAMETER1.1384300.02066855.082880.0000R-squared-0.000024Meandependentvar0.000315AdjustedR-squared-0.001244S.D.dependentvar0.018769S.E.o