r语言garchcopulavar模型附代码数据

r语言garchcopulavar模型附代码数据数据处理思路#1.原始数据为4组时间序列；#读取软件包library("fGarch")library("quantmod")library(ghyp)library(copula)#设置工作目录##读取数据data=read.csv("Data.csv")head(data)# Pound Jpan Usd Eur#1 -0.016689192 -0.006422036 -0.004161304 0.001084608#2 0.000000000 0.005993930 0.000000000 -0.034008741#3 0.000000000 -0.006850273 0.008322209 -0.013969242#4 0.012517495 0.010275005 0.000000000 -0.001120290#5 0.012513888 -0.007277877 0.020798548 -0.011676878#6 -0.008342191 0.002140679 0.012474350 0.007202157data=na.omit(data)2.对每组数据进行基本检验(自回归，异方差，自相关，稳定性，正态性)然后进行ARCH(1,1)建模，得到四个边缘分布；#自编函数进行基本检验testfun=function(yield){#绘制时序图ts.plot(yield)#基本统计量summary(yield)sd(yield)var(yield)# /*偏度、峰度*/n<-length(yield)m<-mean(yield)s<-sd(yield)g1<-n/((n-1)*(n-2))*sum((yield-m)八3)/s八3g2<-((n*(n+1))/((n-1)*(n-2)*(n-3))*sum((yield-m)八4)/s八4-(3*(n-1)八2)/

r语言garchcopulavar模型附代码数据((n-2)*(n-3)))#偏度g1#峰度g2# /*作图*/hist(yield,freq=F)lines(density(yield))#QQ图(正态性)qqnorm(yield)qqline(yield)library(tseries)# /*JB检验*/(检验正态性)print(jarque.bera.test(yield))# /*自相关性检验*/print(Box.test(yield,type="Ljung-Box"))然后用自相关图检查序列的平稳性，，最后发现一阶差分后的序列是平稳的#检验自相关偏相关系数acf(yield)pacf(yield)下面对平稳性序列建立模型，偏相关系数在滞后！期后很快地趋向于0，所以取p=1,自相关系数图形具有拖尾性，所以初步判断诲(1)模型# /*单位根检验*/稳定性检验print(adf.test(yield))print(pp.test(yield))# /*ARCH-LM检验结果*/异方差检验library(FinTS)print(ArchTest(yield,lags=12,demean=FALSE))# 建立/*GARCH*/模型library(fGarch);library(rugarch)#/*GARCH(1,1)-norm*/garch_norm<-garchFit(yield〜garch(1,1),trace=FALSE)garch_normspec<-ugarchspec(variance.model=list(garchOrder=c(1,1)),mean.model=list(armaOrder=c(0,0)))fit<-ugarchfit(spec=spec,data=yield)

r语言garchcopulavar模型附代码数据fit#对每一组数据进行分析yield=data[,1]testfun(yield)r语言garchcopulavar模型附代码数据TimeHistogramofyieldId

lle[r语言garchcopulavar模型附代码数据NormalQ-QPloto। । । । । ।r-3-2-10 1 2 3TheoreticalQuantiles##JarqueBeraTest##data:yield#X-squared=614.62,df=2,p-value<2.2e-16###Box-Ljungtest##data:yield#X-squared=0.51149,df=1,p-value=0.4745

r语言garchcopulavar模型附代码数据Seriesyield#Warninginadf.test(yield):p-valuesmallerthanprintedp-value##AugmentedDickey-FullerTest##data:yield#Dickey-Fuller=-13.844,Lagorder=13,p-value=0.01#alternativehypothesis:stationary#Warninginpp.test(yield):p-valuesmallerthanprintedp-value##Phillips-PerronUnitRootTest##data:yield#Dickey-FullerZ(alpha)=-2511.3,Truncationlagparameter=9,#p-value=0.01#alternativehypothesis:stationary###ARCHLM-test;Nullhypothesis:noARCHeffects##data:yield#Chi-squared=137.66,df=12,p-value<2.2e-16#Loadingrequiredpackage:parallel

r语言garchcopulavar模型附代码数据##Attachingpackage:'rugarch'#Thefollowingobjectismaskedfrom'package:stats':##sigmaSeriesyieldTOC\o"1-5"\h\zS/ । .o0 5 10 15 20 25 30 35Lag##* *#* GARCHModelFit *#* *##ConditionalVarianceDynamics# #GARCHModel:sGARCH(1,1)#MeanModel:ARFIMA(0,0,0)#Distribution:norm##OptimalParameters# ##EstimateStd.ErrortvaluePr(>|t|)##mu-0.0003060.000404-0.75660.44929##omega0.0000050.0000041.30700.19123##alpha10.0269570.0050415.34780.00000##beta10.9639890.002210436.18680.00000####RobustStandardErrors:r语言garchcopulavar模型附代码数据####mu##omega################alpha1beta1Estimate-0.0003060.0000050.0269570.963989Std.Error0.0004300.0000250.0312150.005525tvalue-0.711640.189450.86359174.47964Pr(>|t|)0.476690.849740.387820.00000LogLikelihood:####mu##omega################alpha1beta1Estimate-0.0003060.0000050.0269570.963989Std.Error0.0004300.0000250.0312150.005525tvalue-0.711640.189450.86359174.47964Pr(>|t|)0.476690.849740.387820.00000LogLikelihood:6477.686InformationCriteria##Akaike##Bayes##Shibata-4.8275-4.8187-4.8275##Hannan-Quinn-4.8243####WeightedLjung-BoxTestonStandardizedResiduals################statisticLag[1] 0.00832Lag[2*(p+q)+(p+q)-1][2] 1.48204Lag[4*(p+q)+(p+q)-1][5] 4.83395d.o.f=0H0:Noserialcorrelationp-value0.92730.36520.1668##WeightedLjung-BoxTestonStandardizedSquaredResiduals##############Lag[1]Lag[2*(p+q)+(p+q)-1][5]Lag[4*(p+q)+(p+q)-1][9]d.o.f=2statistic6.928.1111.59p-value0.0085220.0276720.022506##WeightedARCHLMTests## ####ARCH##ARCH##ARCHLag[3]Lag[5]Lag[7]Statistic0.29372.03345.6010Shape0.5001.4402.315Scale2.0001.6671.543## ####ARCH##ARCH##ARCHLag[3]Lag[5]Lag[7]Statistic0.29372.03345.6010Shape0.5001.4402.315Scale2.0001.6671.543P-Value0.58780.46390.1704##########NyblomstabilitytestJointStatistic:4.4761IndividualStatistics:##mu##omega##alpha1##beta1##0.320130.760210.091710.23634##AsymptoticCriticalValues(10%5%1%)r语言garchcopulavar模型附代码数据##JointStatistic: 1.071.241.6##IndividualStatistic: 0.350.470.75####SignBiasTest## ##t-valueprobsig##SignBias2.02860.04260**##NegativeSignBias2.53880.01118**##PositiveSignBias0.29350.76914##JointEffect6.99890.07193*######AdjustedPearsonGoodness-of-FitTest:## ##groupstatisticp-value(g-1)##120105.74.951e-14##230216.21.590e-30##340284.35.053e-39##450404.91.711e-57######Elapsedtime:0.784045yield=data[,2]testfun(yield)r语言garchcopulavar模型附代码数据HistogramofyieldId

lle[r语言garchcopulavar模型附代码数据NormalQ-QPlot-3 -2 -1 0 1 2 3TheoreticalQuantiles###JarqueBeraTest##data:yield#X-squared=622.46,df=2,p-value<2.2e-16###Box-Ljungtest##data:yield#X-squared=8.6698,df=1,p-value=0.003235

r语言garchcopulavar模型附代码数据Seriesyield#Warninginadf.test(yield):p-valuesmallerthanprintedp-value##AugmentedDickey-FullerTest##data:yield#Dickey-Fuller=-13.404,Lagorder=13,p-value=0.01#alternativehypothesis:stationary#Warninginpp.test(yield):p-valuesmallerthanprintedp-value

r语言garchcopulavar模型附代码数据##Phillips-PerronUnitRootTest##data:yield#Dickey-FullerZ(alpha)=-2799.7,Truncationlagparameter=9,#p-value=0.01#alternativehypothesis:stationary###ARCHLM-test;Nullhypothesis:noARCHeffects##data:yield#Chi-squared=200.84,df=12,p-value<2.2e-16#TOC\o"1-5"\h\z#* *#* GARCHModelFit *#* *##ConditionalVarianceDynamics# #GARCHModel:sGARCH(1,1)#MeanModel:ARFIMA(0,0,0)#Distribution:norm###OptimalParameters##

r语言garchcopulavar模型附代码数据## EstimateStd.ErrortvaluePr(>|t|)##mu -0.000378 0.000162-2.326060.020015##omega##alpha1##beta1##omega##alpha1##beta10.0000010.0734540.9184890.0541461.356590.174911##Robust####mu##omega##alpha1##beta1StandardErrors:EstimateStd.Error##Robust####mu##omega##alpha1##beta1StandardErrors:EstimateStd.Error-0.0003780.0000010.0734540.9184890.0045510.0002243.6091323.598937tvalue-0.0830400.0044030.0203520.255211Pr(>|t|)0.933820.996490.983760.79856####LogLikelihood:8875.036###InformationCriteria## ###Akaike -6.6152#Bayes -6.6064#Shibata -6.6152##Hannan-Quinn-6.6121####WeightedLjung-BoxTestonStandardizedResiduals## # statistic p-value#Lag[1] 1.670 0.1963#Lag[2*(p+q)+(p+q)-1][2] 2.129 0.2422#Lag[4*(p+q)+(p+q)-1][5] 3.179 0.3754#d.o.f=0#H0:Noserialcorrelation###WeightedLjung-BoxTestonStandardizedSquaredResiduals##############Lag[1]Lag[2*(p+q)+(p+q)-1][5]Lag[4*(p+q)+(p+q)-1][9]d.o.f=2statistic1.3651.7812.051p-value0.24270.67110.8988#####ARCHLag[3]#ARCHLag[5]#ARCHLag[7]###WeightedARCHLMTests## StatisticShapeScaleP-Value0.59470.5002.0000.44060.71501.4401.6670.81890.81942.3151.5430.9411##Nyblomstabilitytest## ##JointStatistic:83.8698r语言garchcopulavar模型附代码数据#IndividualStatistics:#mu0.1258#omega8.1451#alpha10.1628#beta10.2932##AsymptoticCriticalValues(10%5%1%)#JointStatistic: 1.071.241.6#IndividualStatistic: 0.350.470.75##SignBiasTest# # t-value prob sig#SignBias 0.8300 0.4066#NegativeSignBias0.00960.9923#PositiveSignBias0.85000.3954#JointEffect 3.9034 0.2721###AdjustedPearsonGoodness-of-FitTest:# ##groupstatisticp-value(g-1)##120134.12.473e-19##230178.12.286e-23##340156.35.577e-16##450207.42.232e-21###Elapsedtime:0.686039yield=data[,3]testfun(yield)r语言garchcopulavar模型附代码数据QOc5I0 500WOO150{) 2000 2500TimeHistogramofyieldId

lle[r语言garchcopulavar模型附代码数据NormalQ-QPlot-3-2-10 1 2 3TheoreticalQuantiles###JarqueBeraTest##data:yield#X-squared=1139.4,df=2,p-value<2.2e-16###Box-Ljungtest##data:yield#X-squared=1.7147,df=1,p-value=0.1904r语言garchcopulavar模型附代码数据Seriesyield0 5 10 152D25 30 35Lag#Warninginadf.test(yield):p-valuesmallerthanprintedp-value##AugmentedDickey-FullerTest##data:yield#Dickey-Fuller=-13.046,Lagorder=13,p-value=0.01#alternativehypothesis:stationary#Warninginpp.test(yield):p-valuesmallerthanprintedp-valuer语言garchcopulavar模型附代码数据0 5 10 15 20 25 30 35Lag##Phillips-PerronUnitRootTest##data:yield#Dickey-FullerZ(alpha)=-2592.1,Truncationlagparameter=9,#p-value=0.01#alternativehypothesis:stationary###ARCHLM-test;Nullhypothesis:noARCHeffects##data:yield#Chi-squared=186.82,df=12,p-value<2.2e-16#TOC\o"1-5"\h\z#* *#* GARCHModelFit *#* *##ConditionalVarianceDynamics# #GARCHModel:sGARCH(1,1)#MeanModel:ARFIMA(0,0,0)#Distribution:norm###OptimalParameters##

r语言garchcopulavar模型附代码数据## EstimateStd.ErrortvaluePr(>|t|)##mu -0.000280 0.000409-0.685010.493340##omega##alpha1##beta1##omega##alpha1##beta10.0000030.0407330.9537840.004312 9.44690 0.0000000.004676203.976870.000000##StandardErrors:EstimateStandardErrors:EstimatePr(>|t|)0.474770.351070.000000.00000##Robust####mu##omega##alpha1##beta1-0.0002800.0000030.0407330.953784Std.Error0.0003920.0000040.0049570.006562tvalue-0.714740.932518.21679145.34066####LogLikelihood:6305.272####InformationCriteria## ###Akaike -4.6989#Bayes -4.6901#Shibata -4.6989##Hannan-Quinn-4.6958####WeightedLjung-BoxTestonStandardizedResiduals## # statistic p-value#Lag[1] 1.487 0.2227#Lag[2*(p+q)+(p+q)-1][2] 2.793 0.1596#Lag[4*(p+q)+(p+q)-1][5] 4.167 0.2340#d.o.f=0#H0:NoserialcorrelationStandardizedSquaredResiduals#StandardizedSquaredResiduals##statisticp-value##Lag[1] 0.22180.6377##Lag[2*(p+q)+(p+q)-1][5] 0.62450.9369##Lag[4*(p+q)+(p+q)-1][9] 1.21580.9755##WeightedLjung-BoxTeston## ##d.o.f=2####WeightedARCHLMTests## # Statistic Shape Scale P-Value#ARCH Lag[3] 0.003795 0.500 2.000 0.9509#ARCH Lag[5] 0.558535 1.440 1.667 0.8662#ARCH Lag[7] 0.860015 2.315 1.543 0.9352##Nyblomstabilitytest# #JointStatistic:11.858r语言garchcopulavar模型附代码数据#IndividualStatistics:#mu0.04612#omega1.68786#alpha10.21234#beta10.13921##AsymptoticCriticalValues(10%5%1%)#JointStatistic: 1.071.241.6#IndividualStatistic: 0.350.470.75##SignBiasTest# # t-value prob sig#SignBias 0.50882 0.6109#NegativeSignBias0.029040.9768#PositiveSignBias0.956150.3391#JointEffect3.239740.3561###AdjustedPearsonGoodness-of-FitTest:# ##groupstatisticp-value(g-1)##120224.44.516e-37##230414.97.179e-70##340530.31.819e-87##450669.57.785e-110###Elapsedtime:0.5700321yield=data[,4]testfun(yield)r语言garchcopulavar模型附代码数据TimeHistogramofyieldr语言garchcopulavar模型附代码数据NormalQ-QPlot-3 -2 -1 0 1 2 3TheoreticalQuantiles###JarqueBeraTest##data:yield#X-squared=265.7,df=2,p-value<2.2e-16###Box-Ljungtest##data:yield#X-squared=12.253,df=1,p-value=0.0004644r语言garchcopulavar模型附代码数据SeriesyieldCDOCD5 10 15 20 25 30 35Lag#Warninginadf.test(yield):p-valuesmallerthanprintedp-value##AugmentedDickey-FullerTest##data:yield#Dickey-Fuller=-13.616,Lagorder=13,p-value=0.01#alternativehypothesis:stationary#Warninginpp.test(yield):p-valuesmallerthanprintedp-value

r语言garchcopulavar模型附代码数据##Phillips-PerronUnitRootTest##data:yield#Dickey-FullerZ(alpha)=-2410.8,Truncationlagparameter=9,#p-value=0.01#alternativehypothesis:stationary###ARCHLM-test;Nullhypothesis:noARCHeffects##data:yield#Chi-squared=146.83,df=12,p-value<2.2e-16#TOC\o"1-5"\h\z#* *#* GARCHModelFit *#* *##ConditionalVarianceDynamics# #GARCHModel:sGARCH(1,1)#MeanModel:ARFIMA(0,0,0)#Distribution:norm##OptimalParameters# r语言garchcopulavar模型附代码数据# EstimateStd.Error#mu -0.000575 0.000300#omega 0.000005 0.000002#alphal 0.047347 0.004708#beta1 0.934878 0.006087##RobustStandardErrors:# EstimateStd.Error#mu -0.000575 0.000334#omega 0.000005 0.000006#alpha1 0.047347 0.012544#beta1 0.934878 0.010121##LogLikelihood:7255.899#tvaluePr(>|t|)-1.91510.0554812.43740.01479510.05710.000000153.58900.000000tvaluePr(>|t|)-1.721940.0850810.897570.3694173.774410.00016092.368670.000000##InformationCriteria## ###Akaike -5.4078#Bayes -5.3990#Shibata -5.4078#Hannan-Quinn-5.4046##WeightedLjung-BoxTestonStandardizedResiduals# # statistic p-value#Lag[1] 10.02 0.001547#Lag[2*(p+q)+(p+q)-1][2] 10.18 0.001714#Lag[4*(p+q)+(p+q)-1][5] 11.34 0.004141#d.o.f=0#H0:Noserialcorrelation##WeightedLjung-BoxTestonStandardizedSquaredResiduals## ####Lag[1]##Lag[2*(p+q)+(p+q)-1][5]##Lag[4*(p+q)+(p+q)-1][9]##d.o.f=2statisticp-value3.9520.046835.9390.092976.8330.21355####WeightedARCHLMTests##################StatisticShapeScaleP-ValueARCHLag[3]2.3210.5002.0000.1276ARCHLag[5]3.0691.4401.6670.2799ARCHLag[7]3.2102.3151.5430.4749NyblomstabilitytestJointStatistic:1.5527r语言garchcopulavar模型附代码数据#IndividualStatistics:#mu1.0709#omega0.1964#alpha10.1429#beta10.1513##AsymptoticCriticalValues(10%5%1%)#JointStatistic: 1.071.241.6#IndividualStatistic: 0.350.470.75##SignBiasTest# # t-value prob sig#SignBias 1.2545 0.2098#NegativeSignBias0.96500.3346#PositiveSignBias0.69060.4899#JointEffect 4.1751 0.2432###AdjustedPearsonGoodness-of-FitTest:# ##groupstatisticp-value(g-1)##12030.350.04752##23033.060.27549##34042.380.32717##45053.610.30205###Elapsedtime:0.7420433.利用得到的四组边缘分布，测度两两之间的相关性后，选择合适检uLa函数，建立四元CopuLa函数，并检验拟合程度；y2<-datahead(y2)# Pound Jpan Usd Eur#1 -0.016689192 -0.006422036 -0.004161304 0.001084608#2 0.000000000 0.005993930 0.000000000 -0.034008741#3 0.000000000 -0.006850273 0.008322209 -0.013969242#4 0.012517495 0.010275005 0.000000000 -0.001120290#5 0.012513888 -0.007277877 0.020798548 -0.011676878#6 -0.008342191 0.002140679 0.012474350 0.0072021572DdistributionofyieLds:cdf<-pobs(y2)

r语言garchcopulavar模型附代码数据测度两两之间的相关性plot(cdf)O建立四元Copula函数t.cop<-tCopula(dim=4,param=0.5,df=2,df.fixed=TRUE)fit<-fitCopula(data=cdf,copula=t.cop)检验拟合程度summary(fit)#$method#[1]"maximumpseudo-likelihood"##$loglik#[1]-839.8142##$convergence#[1]0##$coefficients#