Hieronder staat het script waarmee de Ljung-Box test is afgenomen voor de set, de p-waardes voor de sets zijn gevonden, waarmee de ARMA-modellen en alle mogelijke type GARCH-modellen zijn geprobeerd.

Q1.3<-matrix(NA,nrow=32,ncol=1)
Q1.4<-matrix(NA,nrow=32,ncol=1)
Q1.5<-matrix(NA,nrow=32,ncol=1)

for (i in 1:32){ 
	Q1.3[i] <- Box.test(robusta$X3rd,lag=i,type="Ljung-Box")$p.value 
	Q1.4[i] <- Box.test(robusta$X4th,lag=i,type="Ljung-Box")$p.value 
	Q1.5[i] <- Box.test(robusta$X5th,lag=i,type="Ljung-Box")$p.value 
}

adf.test(robusta$X3rd)

        Augmented Dickey-Fuller Test

data:  robusta$X3rd
Dickey-Fuller = -1.882, Lag order = 10, p-value = 0.6283
alternative hypothesis: stationary

adf.test(diff(robusta$X3rd,lag=1))

        Augmented Dickey-Fuller Test

data:  diff(robusta$X3rd, lag = 1)
Dickey-Fuller = -10.7103, Lag order = 10, p-value = 0.01
alternative hypothesis: stationary

Warning message:
In adf.test(diff(robusta$X3rd, lag = 1)) :
  p-value smaller than printed p-value
adf.test(robusta$X4th)

        Augmented Dickey-Fuller Test

data:  robusta$X4th
Dickey-Fuller = -1.7981, Lag order = 10, p-value = 0.6638
alternative hypothesis: stationary

adf.test(diff(robusta$X4th,lag=1))

        Augmented Dickey-Fuller Test

data:  diff(robusta$X4th, lag = 1)
Dickey-Fuller = -10.7435, Lag order = 10, p-value = 0.01
alternative hypothesis: stationary

Warning message:
In adf.test(diff(robusta$X4th, lag = 1)) :
  p-value smaller than printed p-value

adf.test(robusta$X5th)

        Augmented Dickey-Fuller Test

data:  robusta$X5th
Dickey-Fuller = -1.7731, Lag order = 10, p-value = 0.6743
alternative hypothesis: stationary

adf.test(diff(robusta$X5th,lag=1))

        Augmented Dickey-Fuller Test

data:  diff(robusta$X5th, lag = 1)
Dickey-Fuller = -10.7174, Lag order = 10, p-value = 0.01
alternative hypothesis: stationary

Warning message:
In adf.test(diff(robusta$X5th, lag = 1)) :
  p-value smaller than printed p-value

PP.test(robusta$X3rd)

        Phillips-Perron Unit Root Test

data:  robusta$X3rd
Dickey-Fuller = -1.8833, Truncation lag parameter = 7, p-value = 0.6277

PP.test(diff(robusta$X3rd,lag=1))

        Phillips-Perron Unit Root Test

data:  diff(robusta$X3rd, lag = 1)
Dickey-Fuller = -32.9293, Truncation lag parameter = 7, p-value = 0.01

PP.test(robusta$X4th)

        Phillips-Perron Unit Root Test

data:  robusta$X4th
Dickey-Fuller = -1.8207, Truncation lag parameter = 7, p-value = 0.6542

PP.test(diff(robusta$X4th,lag=1))

        Phillips-Perron Unit Root Test

data:  diff(robusta$X4th, lag = 1)
Dickey-Fuller = -33.2036, Truncation lag parameter = 7, p-value = 0.01

PP.test(robusta$X5th)

        Phillips-Perron Unit Root Test

data:  robusta$X5th
Dickey-Fuller = -1.7874, Truncation lag parameter = 7, p-value = 0.6683

PP.test(diff(robusta$X5th,lag=1))

        Phillips-Perron Unit Root Test

data:  diff(robusta$X5th, lag = 1)
Dickey-Fuller = -33.3588, Truncation lag parameter = 7, p-value = 0.01

Q2.3<-matrix(NA,nrow=32,ncol=1)
Q2.4<-matrix(NA,nrow=32,ncol=1)
Q2.5<-matrix(NA,nrow=32,ncol=1)

for (i in 1:32){ 
	Q2.3[i] <- Box.test(diff(robusta$X3rd,lag=1),lag=i,type="Ljung-Box")$p.value 
	Q2.4[i] <- Box.test(diff(robusta$X4th,lag=1),lag=i,type="Ljung-Box")$p.value 
	Q2.5[i] <- Box.test(diff(robusta$X5th,lag=1),lag=i,type="Ljung-Box")$p.value 
}

par(mfrow=c(2,1))
plot(Q1.3,main="p-waardes behorende bij de ACF van de robusta-set, 3 maanden",ylim=c(-0.1,0.1),col="blue")
plot(Q2.3,main="p-waardes behorende bij de ACF van de robusta-set, 3 maanden, met lag=1",ylim=c(-0.1,0.1),col="red")

par(mfrow=c(2,1))
plot(Q1.4,main="p-waardes behorende bij de ACF van de robusta-set, 4 maanden",ylim=c(-0.1,0.1),col="blue")
plot(Q2.4,main="p-waardes behorende bij de ACF van de robusta-set, 4 maanden, met lag=1",ylim=c(-0.1,0.1),col="red")

par(mfrow=c(2,1))
plot(Q1.5,main="p-waardes behorende bij de ACF van de robusta-set, 5 maanden",ylim=c(-0.1,0.1),col="blue")
plot(Q2.5,main="p-waardes behorende bij de ACF van de robusta-set, 5 maanden, met lag=1",ylim=c(-0.1,0.1),col="red")

arburg1.3<-ar.burg(x=diff(robusta$X3rd,lag=1),aic=TRUE)
arburg2.3<-ar.burg(x=diff(robusta$X3rd,lag=1),aic=FALSE)
armle1.3<-ar.mle(x=diff(robusta$X3rd,lag=1),aic=TRUE)
armle2.3<-ar.mle(x=diff(robusta$X3rd,lag=1),aic=FALSE)
arols1.3<-ar.ols(x=diff(robusta$X3rd,lag=1),aic=TRUE)
arols2.3<-ar.ols(x=diff(robusta$X3rd,lag=1),aic=FALSE)
aryw1.3<-ar.yw(x=diff(robusta$X3rd,lag=1),aic=TRUE)
aryw2.3<-ar.yw(x=diff(robusta$X3rd,lag=1),aic=FALSE)

arburg1.3$order;arburg2.3$order
[1] 17
[1] 31
armle1.3$order;armle2.3$order
[1] 1
[1] 12
arols1.3$order;arols2.3$order
[1] 1
[1] 31
aryw1.3$order;aryw2.3$order
[1] 17
[1] 31

porde.3<- c(0,armle1.3$order,armle2.3$order,arburg1.3$order,arburg2.3$order)
qmax<- 10
Matrix.3 <- matrix(NA,nrow=qmax*(length(porde.3)),ncol=7)

for (j in 1:length(porde.3)){p=porde.3[j];
for (q in 1:qmax+1){
i=1;q=q-1;
teller=(q+1)+(qmax*(j-1));
n=length(robusta$X3rd)-i
myfit <- arima(x=robusta$X3rd,order=c(p,i,q),include.mean=F)
loglik <- myfit$loglik
theaic <- aic(myfit$loglik,p,q,n)
theaicc <- aicc(myfit$loglik,p,q,n)
thebic <- bic(myfit$loglik,p,q,n)
Matrix.3[teller,1]=p; Matrix.3[teller,2]=i; Matrix.3[teller,3]=q;
Matrix.3[teller,4]=loglik; Matrix.3[teller,5]=theaic; 
Matrix.3[teller,6]=theaicc;Matrix.3[teller,7]=thebic;
}
}

for (i in 45:50){
	Matrix.3[i,1]=31;Matrix.3[i,2]=1;
	Matrix.3[i,3]=i-41;Matrix.3[i,4]=-Inf
	Matrix.3[i,5]=Inf;Matrix.3[i,6]=Inf;
	Matrix.3[i,7]=Inf
}

myfit<-arima(x=robusta$X3rd,order=c(0,1,0),include.mean=F)
Matrix.3[1,1]=0;Matrix.3[1,2]=1;
Matrix.3[1,3]=0;Matrix.3[1,4]=myfit$loglik;

Matrix.3[1,5]=aic(Matrix.3[1,4],Matrix.3[1,1],Matrix.3[1,3],length(robusta$X3rd)-1)
Matrix.3[1,6]=aicc(Matrix.3[1,4],Matrix.3[1,1],Matrix.3[1,3],length(robusta$X3rd)-1)
Matrix.3[1,7]=bic(Matrix.3[1,4],Matrix.3[1,1],Matrix.3[1,3],length(robusta$X3rd)-1)

arburg1.4<-ar.burg(x=diff(robusta$X4th,lag=1),aic=TRUE)
arburg2.4<-ar.burg(x=diff(robusta$X4th,lag=1),aic=FALSE)
armle1.4<-ar.mle(x=diff(robusta$X4th,lag=1),aic=TRUE)
armle2.4<-ar.mle(x=diff(robusta$X4th,lag=1),aic=FALSE)
arols1.4<-ar.ols(x=diff(robusta$X4th,lag=1),aic=TRUE)
arols2.4<-ar.ols(x=diff(robusta$X4th,lag=1),aic=FALSE)
aryw1.4<-ar.yw(x=diff(robusta$X4th,lag=1),aic=TRUE)
aryw2.4<-ar.yw(x=diff(robusta$X4th,lag=1),aic=FALSE)

arburg1.4$order;arburg2.4$order
[1] 1
[1] 31
armle1.4$order;armle2.4$order
[1] 1
[1] 12
arols1.4$order;arols2.4$order
[1] 1
[1] 31
aryw1.4$order;aryw2.4$order
[1] 1
[1] 31

porde.4<- c(0,armle1.4$order,armle2.4$order,arburg2.4$order)
qmax<- 10
Matrix.4 <- matrix(NA,nrow=qmax*(length(porde.4)),ncol=7)

for (j in 1:length(porde.4)){p=porde.4[j];
for (q in 1:qmax+1){
i=1;q=q-1;
teller=(q+1)+(qmax*(j-1));
n=length(robusta$X4th)-i
myfit <- arima(x=robusta$X4th,order=c(p,i,q),include.mean=F)
loglik <- myfit$loglik
theaic <- aic(myfit$loglik,p,q,n)
theaicc <- aicc(myfit$loglik,p,q,n)
thebic <- bic(myfit$loglik,p,q,n)
Matrix.4[teller,1]=p; Matrix.4[teller,2]=i; Matrix.4[teller,3]=q;
Matrix.4[teller,4]=loglik; Matrix.4[teller,5]=theaic; Matrix.4[teller,6]=theaicc;Matrix.4[teller,7]=thebic;
}
}

for (i in 39:40){
	Matrix.4[i,1]=31;Matrix.4[i,2]=1;
	Matrix.4[i,3]=i-31;Matrix.4[i,4]=-Inf
	Matrix.4[i,5]=Inf;Matrix.4[i,6]=Inf;
	Matrix.4[i,7]=Inf
}

myfit<-arima(x=robusta$X4th,order=c(0,1,0),include.mean=F)
Matrix.4[1,1]=0;Matrix.4[1,2]=1;
Matrix.4[1,3]=0;Matrix.4[1,4]=myfit$loglik;

Matrix.4[1,5]=aic(Matrix.4[1,4],Matrix.4[1,1],Matrix.4[1,3],length(robusta$X4th)-1)
Matrix.4[1,6]=aicc(Matrix.4[1,4],Matrix.4[1,1],Matrix.4[1,3],length(robusta$X4th)-1)
Matrix.4[1,7]=bic(Matrix.4[1,4],Matrix.4[1,1],Matrix.4[1,3],length(robusta$X4th)-1)

arburg1.5<-ar.burg(x=diff(robusta$X5th,lag=1),aic=TRUE)
arburg2.5<-ar.burg(x=diff(robusta$X5th,lag=1),aic=FALSE)
armle1.5<-ar.mle(x=diff(robusta$X5th,lag=1),aic=TRUE)
armle2.5<-ar.mle(x=diff(robusta$X5th,lag=1),aic=FALSE)
arols1.5<-ar.ols(x=diff(robusta$X5th,lag=1),aic=TRUE)
arols2.5<-ar.ols(x=diff(robusta$X5th,lag=1),aic=FALSE)
aryw1.5<-ar.yw(x=diff(robusta$X5th,lag=1),aic=TRUE)
aryw2.5<-ar.yw(x=diff(robusta$X5th,lag=1),aic=FALSE)

arburg1.5$order;arburg2.5$order
[1] 1
[1] 31
armle1.5$order;armle2.5$order
[1] 1
[1] 12
arols1.5$order;arols2.5$order
[1] 1
[1] 31
aryw1.5$order;aryw2.5$order
[1] 1
[1] 31

porde.5<- c(0,armle1.5$order,armle2.5$order,arburg2.5$order)
qmax<- 10
Matrix.5 <- matrix(NA,nrow=qmax*(length(porde.5)),ncol=7)

for (j in 1:length(porde.5)){p=porde.5[j];
for (q in 1:qmax+1){
i=1;q=q-1;
teller=(q+1)+(qmax*(j-1));
n=length(robusta$X5th)-i
myfit <- arima(x=robusta$X5th,order=c(p,i,q),include.mean=F)
loglik <- myfit$loglik
theaic <- aic(myfit$loglik,p,q,n)
theaicc <- aicc(myfit$loglik,p,q,n)
thebic <- bic(myfit$loglik,p,q,n)
Matrix.5[teller,1]=p; Matrix.5[teller,2]=i; Matrix.5[teller,3]=q;
Matrix.5[teller,4]=loglik; Matrix.5[teller,5]=theaic; Matrix.5[teller,6]=theaicc;Matrix.5[teller,7]=thebic;
}
}

for (i in 39:40){
	Matrix.5[i,1]=31;Matrix.5[i,2]=1;
	Matrix.5[i,3]=i-31;Matrix.5[i,4]=-Inf
	Matrix.5[i,5]=Inf;Matrix.5[i,6]=Inf;
	Matrix.5[i,7]=Inf
}

myfit<-arima(x=robusta$X5th,order=c(0,1,0),include.mean=F)
Matrix.5[1,1]=0;Matrix.5[1,2]=1;
Matrix.5[1,3]=0;Matrix.5[1,4]=myfit$loglik;

Matrix.5[1,5]=aic(Matrix.5[1,4],Matrix.5[1,1],Matrix.5[1,3],length(robusta$X5th)-1)
Matrix.5[1,6]=aicc(Matrix.5[1,4],Matrix.5[1,1],Matrix.5[1,3],length(robusta$X5th)-1)
Matrix.5[1,7]=bic(Matrix.5[1,4],Matrix.5[1,1],Matrix.5[1,3],length(robusta$X5th)-1)

min(Matrix.3[,5]);min(Matrix.3[,6]);min(Matrix.3[,7]);
[1] 12519.08
[1] 12519.82
[1] 12534.17

min(Matrix.4[,5]);min(Matrix.4[,6]);min(Matrix.4[,7]);
[1] 12391.51
[1] 12391.52
[1] 12400.36

min(Matrix.5[,5]);min(Matrix.5[,6]);min(Matrix.5[,7]);
[1] 12348.89
[1] 12348.9
[1] 12357

pmaxi=8;qmaxi=8
Matrix2.3rd.1.s=matrix(NA,nrow=(pmaxi+1)*qmaxi,ncol=6)

for (p in 1:(pmaxi+1)){
	p=p-1;
	for (q in 2:(qmaxi+1)){
		q=q-1;
		n=length(diff(robusta$X3rd,lag=1));
		teller=q+p*qmaxi
		spec=ugarchspec(variance.model=list(model="sGARCH",garchOrder=c(p,q)),
		mean.model=list(armaOrder=c(17,3)));
		fit=ugarchfit(spec=spec,data=diff(robusta$X3rd,lag=1));
		loglik <- fit@fit$LLH;
		theaic <- aic(loglik,p+17,q+3,n)
		theaicc <- aicc(loglik,p+17,q+3,n)
		thebic <- bic(loglik,p+17,q+3,n)
		Matrix2.3rd.1.s[teller,1]=p;Matrix2.3rd.1.s[teller,2]=q;
		Matrix2.3rd.1.s[teller,3]=loglik;Matrix2.3rd.1.s[teller,4]=theaic;
		Matrix2.3rd.1.s[teller,5]=theaicc;Matrix2.3rd.1.s[teller,6]=thebic;
	}
}

pmaxi=8;qmaxi=8
Matrix2.4th.1.s=matrix(NA,nrow=(pmaxi+1)*qmaxi,ncol=6)

for (p in 1:(pmaxi+1)){
	p=p-1;
	for (q in 2:(qmaxi+1)){
		q=q-1;
		n=length(diff(robusta$X4th,lag=1));
		teller=q+p*qmaxi
		spec=ugarchspec(variance.model=list(model="sGARCH",garchOrder=c(p,q)),
		mean.model=list(armaOrder=c(0,1)));
		fit=ugarchfit(spec=spec,data=diff(robusta$X4th,lag=1));
		loglik <- fit@fit$LLH;
		theaic <- aic(loglik,p,q+1,n)
		theaicc <- aicc(loglik,p,q+1,n)
		thebic <- bic(loglik,p,q+1,n)
		Matrix2.4th.1.s[teller,1]=p;Matrix2.4th.1.s[teller,2]=q;
		Matrix2.4th.1.s[teller,3]=loglik;Matrix2.4th.1.s[teller,4]=theaic;
		Matrix2.4th.1.s[teller,5]=theaicc;Matrix2.4th.1.s[teller,6]=thebic;
	}
}

pmaxi=8;qmaxi=8
Matrix2.5th.1.s=matrix(NA,nrow=(pmaxi+1)*qmaxi,ncol=6)

for (p in 1:(pmaxi+1)){
	p=p-1;
	for (q in 2:(qmaxi+1)){
		q=q-1;
		n=length(diff(robusta$X5th,lag=1));
		teller=q+p*qmaxi
		spec=ugarchspec(variance.model=list(model="sGARCH",garchOrder=c(p,q)),
		mean.model=list(armaOrder=c(0,1)));
		fit=ugarchfit(spec=spec,data=diff(robusta$X5th,lag=1));
		loglik <- fit@fit$LLH;
		theaic <- aic(loglik,p,q+1,n)
		theaicc <- aicc(loglik,p,q+1,n)
		thebic <- bic(loglik,p,q+1,n)
		Matrix2.5th.1.s[teller,1]=p;Matrix2.5th.1.s[teller,2]=q;
		Matrix2.5th.1.s[teller,3]=loglik;Matrix2.5th.1.s[teller,4]=theaic;
		Matrix2.5th.1.s[teller,5]=theaicc;Matrix2.5th.1.s[teller,6]=thebic;
	}
}

pmaxi=8;qmaxi=8
Matrix2.3rd.1.i=matrix(NA,nrow=(pmaxi+1)*qmaxi,ncol=6)

for (p in 1:(pmaxi+1)){
	p=p-1;
	for (q in 2:(qmaxi+1)){
		q=q-1;
		n=length(diff(robusta$X3rd,lag=1));
		teller=q+p*qmaxi
		spec=ugarchspec(variance.model=list(model="iGARCH",garchOrder=c(p,q)),
		mean.model=list(armaOrder=c(17,3)));
		fit=ugarchfit(spec=spec,data=diff(robusta$X3rd,lag=1));
		loglik <- fit@fit$LLH;
		theaic <- aic(loglik,p+17,q+3,n)
		theaicc <- aicc(loglik,p+17,q+3,n)
		thebic <- bic(loglik,p+17,q+3,n)
		Matrix2.3rd.1.i[teller,1]=p;Matrix2.3rd.1.i[teller,2]=q;
		Matrix2.3rd.1.i[teller,3]=loglik;Matrix2.3rd.1.i[teller,4]=theaic;
		Matrix2.3rd.1.i[teller,5]=theaicc;Matrix2.3rd.1.i[teller,6]=thebic;
	}
}

pmaxi=8;qmaxi=8
Matrix2.4th.1.i=matrix(NA,nrow=(pmaxi+1)*qmaxi,ncol=6)

for (p in 1:(pmaxi+1)){
	p=p-1;
	for (q in 2:(qmaxi+1)){
		q=q-1;
		n=length(diff(robusta$X4th,lag=1));
		teller=q+p*qmaxi
		spec=ugarchspec(variance.model=list(model="iGARCH",garchOrder=c(p,q)),
		mean.model=list(armaOrder=c(0,1)));
		fit=ugarchfit(spec=spec,data=diff(robusta$X4th,lag=1));
		loglik <- fit@fit$LLH;
		theaic <- aic(loglik,p,q+1,n)
		theaicc <- aicc(loglik,p,q+1,n)
		thebic <- bic(loglik,p,q+1,n)
		Matrix2.4th.1.i[teller,1]=p;Matrix2.4th.1.i[teller,2]=q;
		Matrix2.4th.1.i[teller,3]=loglik;Matrix2.4th.1.i[teller,4]=theaic;
		Matrix2.4th.1.i[teller,5]=theaicc;Matrix2.4th.1.i[teller,6]=thebic;
	}
}

pmaxi=8;qmaxi=8
Matrix2.5th.1.i=matrix(NA,nrow=(pmaxi+1)*qmaxi,ncol=6)

for (p in 1:(pmaxi+1)){
	p=p-1;
	for (q in 2:(qmaxi+1)){
		q=q-1;
		n=length(diff(robusta$X5th,lag=1));
		teller=q+p*qmaxi
		spec=ugarchspec(variance.model=list(model="iGARCH",garchOrder=c(p,q)),
		mean.model=list(armaOrder=c(0,1)));
		fit=ugarchfit(spec=spec,data=diff(robusta$X5th,lag=1));
		loglik <- fit@fit$LLH;
		theaic <- aic(loglik,p,q+1,n)
		theaicc <- aicc(loglik,p,q+1,n)
		thebic <- bic(loglik,p,q+1,n)
		Matrix2.5th.1.i[teller,1]=p;Matrix2.5th.1.i[teller,2]=q;
		Matrix2.5th.1.i[teller,3]=loglik;Matrix2.5th.1.i[teller,4]=theaic;
		Matrix2.5th.1.i[teller,5]=theaicc;Matrix2.5th.1.i[teller,6]=thebic;
	}
}

qmaxi=6
Matrix2.3rd.1.ap=matrix(NA,nrow=qmaxi,ncol=6)

for (q in 2:(qmaxi+1)){
	q=q-1;
	n=length(diff(robusta$X3rd,lag=1));
	teller=q
	spec=ugarchspec(variance.model=list(model="apARCH",garchOrder=c(0,q)),
	mean.model=list(armaOrder=c(17,3)));
	fit=ugarchfit(spec=spec,data=diff(robusta$X3rd,lag=1));
	loglik <- fit@fit$LLH;
	theaic <- aic(loglik,17,q+3,n)
	theaicc <- aicc(loglik,17,q+3,n)
	thebic <- bic(loglik,17,q+3,n)
	Matrix2.3rd.1.ap[teller,1]=0;Matrix2.3rd.1.ap[teller,2]=q;
	Matrix2.3rd.1.ap[teller,3]=loglik;Matrix2.3rd.1.ap[teller,4]=theaic;
	Matrix2.3rd.1.ap[teller,5]=theaicc;Matrix2.3rd.1.ap[teller,6]=thebic;
}

qmaxi=6
Matrix2.4th.1.ap=matrix(NA,nrow=qmaxi,ncol=6)

for (q in 2:(qmaxi+1)){
	q=q-1;
	n=length(diff(robusta$X4th,lag=1));
	teller=q
	spec=ugarchspec(variance.model=list(model="apARCH",garchOrder=c(0,q)),
	mean.model=list(armaOrder=c(0,1)));
	fit=ugarchfit(spec=spec,data=diff(robusta$X4th,lag=1));
	loglik <- fit@fit$LLH;
	theaic <- aic(loglik,0,q+1,n)
	theaicc <- aicc(loglik,0,q+1,n)
	thebic <- bic(loglik,0,q+1,n)
	Matrix2.4th.1.ap[teller,1]=0;Matrix2.4th.1.ap[teller,2]=q;
	Matrix2.4th.1.ap[teller,3]=loglik;Matrix2.4th.1.ap[teller,4]=theaic;
	Matrix2.4th.1.ap[teller,5]=theaicc;Matrix2.4th.1.ap[teller,6]=thebic;
}

qmaxi=6
Matrix2.5th.1.ap=matrix(NA,nrow=qmaxi,ncol=6)

for (q in 2:(qmaxi+1)){
	q=q-1;
	n=length(diff(robusta$X5th,lag=1));
	teller=q
	spec=ugarchspec(variance.model=list(model="apARCH",garchOrder=c(0,q)),
	mean.model=list(armaOrder=c(0,1)));
	fit=ugarchfit(spec=spec,data=diff(robusta$X5th,lag=1));
	loglik <- fit@fit$LLH;
	theaic <- aic(loglik,0,q+1,n)
	theaicc <- aicc(loglik,0,q+1,n)
	thebic <- bic(loglik,0,q+1,n)
	Matrix2.5th.1.ap[teller,1]=0;Matrix2.5th.1.ap[teller,2]=q;
	Matrix2.5th.1.ap[teller,3]=loglik;Matrix2.5th.1.ap[teller,4]=theaic;
	Matrix2.5th.1.ap[teller,5]=theaicc;Matrix2.5th.1.ap[teller,6]=thebic;
}

pmaxi=8;qmaxi=8
Matrix2.3rd.2.t=matrix(NA,nrow=(pmaxi+1)*qmaxi,ncol=6)

for (p in (pmaxi+1):(pmaxi+1)){
	p=p-1;
	for (q in 7:(qmaxi+1)){
		q=q-1;
		n=length(diff(robusta$X3rd,lag=1));
		teller=q+p*qmaxi
		spec=ugarchspec(variance.model=list(model="fGARCH",submodel="TGARCH",garchOrder=c(p,q)),
		mean.model=list(armaOrder=c(17,3)));
		fit=ugarchfit(spec=spec,data=diff(robusta$X3rd,lag=1));
		loglik <- fit@fit$LLH;
		theaic <- aic(loglik,p+17,q+3,n)
		theaicc <- aicc(loglik,p+17,q+3,n)
		thebic <- bic(loglik,p+17,q+3,n)
		Matrix2.3rd.2.t[teller,1]=p;Matrix2.3rd.2.t[teller,2]=q;
		Matrix2.3rd.2.t[teller,3]=loglik;Matrix2.3rd.2.t[teller,4]=theaic;
		Matrix2.3rd.2.t[teller,5]=theaicc;Matrix2.3rd.2.t[teller,6]=thebic;
	}
}
for (i in 66:72){
	Matrix2.3rd.2.t[i,1]=8;Matrix2.3rd.2.t[i,2]=i-64;
	Matrix2.3rd.2.t[i,3]=-Inf;Matrix2.3rd.2.t[i,4]=Inf;
	Matrix2.3rd.2.t[i,5]=Inf;Matrix2.3rd.2.t[i,6]=Inf; 
}

pmaxi=8;qmaxi=8
Matrix2.4th.2.t=matrix(NA,nrow=(pmaxi+1)*qmaxi,ncol=6)

for (p in 1:(pmaxi+1)){
	p=p-1;
	for (q in 2:(qmaxi+1)){
		q=q-1;
		n=length(diff(robusta$X4th,lag=1));
		teller=q+p*qmaxi
		spec=ugarchspec(variance.model=list(model="fGARCH",submodel="TGARCH",garchOrder=c(p,q)),
		mean.model=list(armaOrder=c(0,1)));
		fit=ugarchfit(spec=spec,data=diff(robusta$X4th,lag=1));
		loglik <- fit@fit$LLH;
		theaic <- aic(loglik,p,q+1,n)
		theaicc <- aicc(loglik,p,q+1,n)
		thebic <- bic(loglik,p,q+1,n)
		Matrix2.4th.2.t[teller,1]=p;Matrix2.4th.2.t[teller,2]=q;
		Matrix2.4th.2.t[teller,3]=loglik;Matrix2.4th.2.t[teller,4]=theaic;
		Matrix2.4th.2.t[teller,5]=theaicc;Matrix2.4th.2.t[teller,6]=thebic;
	}
}

for (i in 66:72){
	Matrix2.4th.2.t[i,1]=8;Matrix2.4th.2.t[i,2]=i-64;
	Matrix2.4th.2.t[i,3]=-Inf;Matrix2.4th.2.t[i,4]=Inf;
	Matrix2.4th.2.t[i,5]=Inf;Matrix2.4th.2.t[i,6]=Inf; 
}

pmaxi=8;qmaxi=8
Matrix2.5th.2.t=matrix(NA,nrow=(pmaxi+1)*qmaxi,ncol=6)

for (p in 1:(pmaxi+1)){
	p=p-1;
	for (q in 2:(qmaxi+1)){
		q=q-1;
		n=length(diff(robusta$X5th,lag=1));
		teller=q+p*qmaxi
		spec=ugarchspec(variance.model=list(model="fGARCH",submodel="TGARCH",garchOrder=c(p,q)),
		mean.model=list(armaOrder=c(0,1)));
		fit=ugarchfit(spec=spec,data=diff(robusta$X5th,lag=1));
		loglik <- fit@fit$LLH;
		theaic <- aic(loglik,p,q+1,n)
		theaicc <- aicc(loglik,p,q+1,n)
		thebic <- bic(loglik,p,q+1,n)
		Matrix2.5th.2.t[teller,1]=p;Matrix2.5th.2.t[teller,2]=q;
		Matrix2.5th.2.t[teller,3]=loglik;Matrix2.5th.2.t[teller,4]=theaic;
		Matrix2.5th.2.t[teller,5]=theaicc;Matrix2.5th.2.t[teller,6]=thebic;
	}
}

for (i in 66:72){
	Matrix2.5th.2.t[i,1]=8;Matrix2.5th.2.t[i,2]=i-64;
	Matrix2.5th.2.t[i,3]=-Inf;Matrix2.5th.2.t[i,4]=Inf;
	Matrix2.5th.2.t[i,5]=Inf;Matrix2.5th.2.t[i,6]=Inf; 
}

pmaxi=8;qmaxi=8
Matrix2.3rd.2.av=matrix(NA,nrow=(pmaxi+1)*qmaxi,ncol=6)

for (p in 1:(pmaxi+1)){
	p=p-1;
	for (q in 2:(qmaxi+1)){
		q=q-1;
		n=length(diff(robusta$X3rd,lag=1));
		teller=q+p*qmaxi
		spec=ugarchspec(variance.model=list(model="fGARCH",submodel="AVGARCH",garchOrder=c(p,q)),
		mean.model=list(armaOrder=c(17,3)));
		fit=ugarchfit(spec=spec,data=diff(robusta$X3rd,lag=1));
		loglik <- fit@fit$LLH;
		theaic <- aic(loglik,p+17,q+3,n)
		theaicc <- aicc(loglik,p+17,q+3,n)
		thebic <- bic(loglik,p+17,q+3,n)
		Matrix2.3rd.2.av[teller,1]=p;Matrix2.3rd.2.av[teller,2]=q;
		Matrix2.3rd.2.av[teller,3]=loglik;Matrix2.3rd.2.av[teller,4]=theaic;
		Matrix2.3rd.2.av[teller,5]=theaicc;Matrix2.3rd.2.av[teller,6]=thebic;
	}
}

pmaxi=8;qmaxi=8
Matrix2.4th.2.av=matrix(NA,nrow=(pmaxi+1)*qmaxi,ncol=6)

for (p in 1:(pmaxi+1)){
	p=p-1;
	for (q in 2:(qmaxi+1)){
		q=q-1;
		n=length(diff(robusta$X4th,lag=1));
		teller=q+p*qmaxi
		spec=ugarchspec(variance.model=list(model="fGARCH",submodel="AVGARCH",garchOrder=c(p,q)),
		mean.model=list(armaOrder=c(0,1)));
		fit=ugarchfit(spec=spec,data=diff(robusta$X4th,lag=1));
		loglik <- fit@fit$LLH;
		theaic <- aic(loglik,p,q+1,n)
		theaicc <- aicc(loglik,p,q+1,n)
		thebic <- bic(loglik,p,q+1,n)
		Matrix2.4th.2.av[teller,1]=p;Matrix2.4th.2.av[teller,2]=q;
		Matrix2.4th.2.av[teller,3]=loglik;Matrix2.4th.2.av[teller,4]=theaic;
		Matrix2.4th.2.av[teller,5]=theaicc;Matrix2.4th.2.av[teller,6]=thebic;
	}
}

pmaxi=8;qmaxi=8
Matrix2.5th.2.av=matrix(NA,nrow=(pmaxi+1)*qmaxi,ncol=6)

for (p in 1:(pmaxi+1)){
	p=p-1;
	for (q in 2:(qmaxi+1)){
		q=q-1;
		n=length(diff(robusta$X5th,lag=1));
		teller=q+p*qmaxi
		spec=ugarchspec(variance.model=list(model="fGARCH",submodel="AVGARCH",garchOrder=c(p,q)),
		mean.model=list(armaOrder=c(0,1)));
		fit=ugarchfit(spec=spec,data=diff(robusta$X5th,lag=1));
		loglik <- fit@fit$LLH;
		theaic <- aic(loglik,p,q+1,n)
		theaicc <- aicc(loglik,p,q+1,n)
		thebic <- bic(loglik,p,q+1,n)
		Matrix2.5th.2.av[teller,1]=p;Matrix2.5th.2.av[teller,2]=q;
		Matrix2.5th.2.av[teller,3]=loglik;Matrix2.5th.2.av[teller,4]=theaic;
		Matrix2.5th.2.av[teller,5]=theaicc;Matrix2.5th.2.av[teller,6]=thebic;
	}
}

pmaxi=8;qmaxi=8
Matrix2.3rd.2.na=matrix(NA,nrow=(pmaxi+1)*qmaxi,ncol=6)

for (p in 1:(pmaxi+1)){
	p=p-1;
	for (q in 2:(qmaxi+1)){
		q=q-1;
		n=length(diff(robusta$X3rd,lag=1));
		teller=q+p*qmaxi
		spec=ugarchspec(variance.model=list(model="fGARCH",submodel="NAGARCH",garchOrder=c(p,q)),
		mean.model=list(armaOrder=c(17,3)));
		fit=ugarchfit(spec=spec,data=diff(robusta$X3rd,lag=1));
		loglik <- fit@fit$LLH;
		theaic <- aic(loglik,p+17,q+3,n)
		theaicc <- aicc(loglik,p+17,q+3,n)
		thebic <- bic(loglik,p+17,q+3,n)
		Matrix2.3rd.2.na[teller,1]=p;Matrix2.3rd.2.na[teller,2]=q;
		Matrix2.3rd.2.na[teller,3]=loglik;Matrix2.3rd.2.na[teller,4]=theaic;
		Matrix2.3rd.2.na[teller,5]=theaicc;Matrix2.3rd.2.na[teller,6]=thebic;
	}
}

Hulpna.3<-c(18,46,54,60,63,67,69,70)
for (i in 1:length(Hulpna.3)){ 
	Matrix2.3rd.2.na[Hulpna.3[i],3]=-Inf;Matrix2.3rd.2.na[Hulpna.3[i],4]=Inf;
	Matrix2.3rd.2.na[Hulpna.3[i],5]=Inf;Matrix2.3rd.2.na[Hulpna.3[i],6]=Inf;
}

pmaxi=8;qmaxi=8
Matrix2.4th.2.na=matrix(NA,nrow=(pmaxi+1)*qmaxi,ncol=6)

for (p in 1:(pmaxi+1)){
	p=p-1;
	for (q in 2:(qmaxi+1)){
		q=q-1;
		n=length(diff(robusta$X4th,lag=1));
		teller=q+p*qmaxi
		spec=ugarchspec(variance.model=list(model="fGARCH",submodel="NAGARCH",garchOrder=c(p,q)),
		mean.model=list(armaOrder=c(0,1)));
		fit=ugarchfit(spec=spec,data=diff(robusta$X4th,lag=1));
		loglik <- fit@fit$LLH;
		theaic <- aic(loglik,p,q+1,n)
		theaicc <- aicc(loglik,p,q+1,n)
		thebic <- bic(loglik,p,q+1,n)
		Matrix2.4th.2.na[teller,1]=p;Matrix2.4th.2.na[teller,2]=q;
		Matrix2.4th.2.na[teller,3]=loglik;Matrix2.4th.2.na[teller,4]=theaic;
		Matrix2.4th.2.na[teller,5]=theaicc;Matrix2.4th.2.na[teller,6]=thebic;
	}
}

Hulpna.4 <- c(33,50)
for (i in 1:2){
	Matrix2.4th.2.na[Hulpna.4[i],3]=-Inf;Matrix2.4th.2.na[Hulpna.4[i],4]=Inf;
	Matrix2.4th.2.na[Hulpna.4[i],5]=Inf;Matrix2.4th.2.na[Hulpna.4[i],6]=Inf;
}

pmaxi=8;qmaxi=8
Matrix2.5th.2.na=matrix(NA,nrow=(pmaxi+1)*qmaxi,ncol=6)

for (p in 1:(pmaxi+1)){
	p=p-1;
	for (q in 2:(qmaxi+1)){
		q=q-1;
		n=length(diff(robusta$X5th,lag=1));
		teller=q+p*qmaxi
		spec=ugarchspec(variance.model=list(model="fGARCH",submodel="NAGARCH",garchOrder=c(p,q)),
		mean.model=list(armaOrder=c(0,1)));
		fit=ugarchfit(spec=spec,data=diff(robusta$X5th,lag=1));
		loglik <- fit@fit$LLH;
		theaic <- aic(loglik,p,q+1,n)
		theaicc <- aicc(loglik,p,q+1,n)
		thebic <- bic(loglik,p,q+1,n)
		Matrix2.5th.2.na[teller,1]=p;Matrix2.5th.2.na[teller,2]=q;
		Matrix2.5th.2.na[teller,3]=loglik;Matrix2.5th.2.na[teller,4]=theaic;
		Matrix2.5th.2.na[teller,5]=theaicc;Matrix2.5th.2.na[teller,6]=thebic;
	}
}

pmaxi=8;qmaxi=8
Matrix2.3rd.2.n=matrix(NA,nrow=(pmaxi+1)*qmaxi,ncol=6)

for (p in 1:(pmaxi+1)){
	p=p-1;
	for (q in 2:(qmaxi+1)){
		q=q-1;
		n=length(diff(robusta$X3rd,lag=1));
		teller=q+p*qmaxi
		spec=ugarchspec(variance.model=list(model="fGARCH",submodel="NGARCH",garchOrder=c(p,q)),
		mean.model=list(armaOrder=c(17,3)));
		fit=ugarchfit(spec=spec,data=diff(robusta$X3rd,lag=1));
		loglik <- fit@fit$LLH;
		theaic <- aic(loglik,p+17,q+3,n)
		theaicc <- aicc(loglik,p+17,q+3,n)
		thebic <- bic(loglik,p+17,q+3,n)
		Matrix2.3rd.2.n[teller,1]=p;Matrix2.3rd.2.n[teller,2]=q;
		Matrix2.3rd.2.n[teller,3]=loglik;Matrix2.3rd.2.n[teller,4]=theaic;
		Matrix2.3rd.2.n[teller,5]=theaicc;Matrix2.3rd.2.n[teller,6]=thebic;
	}
}
for (i in 72){
	Matrix2.3rd.2.n[i,1]=8;Matrix2.3rd.2.n[i,2]=i-64;
	Matrix2.3rd.2.n[i,3]=-Inf;Matrix2.3rd.2.n[i,4]=Inf;
	Matrix2.3rd.2.n[i,5]=Inf;Matrix2.3rd.2.n[i,6]=Inf; 
}


pmaxi=8;qmaxi=8
Matrix2.4th.2.n=matrix(NA,nrow=(pmaxi+1)*qmaxi,ncol=6)

for (p in 1:(pmaxi+1)){
	p=p-1;
	for (q in 2:(qmaxi+1)){
		q=q-1;
		n=length(diff(robusta$X4th,lag=1));
		teller=q+p*qmaxi
		spec=ugarchspec(variance.model=list(model="fGARCH",submodel="NGARCH",garchOrder=c(p,q)),
		mean.model=list(armaOrder=c(0,1)));
		fit=ugarchfit(spec=spec,data=diff(robusta$X4th,lag=1));
		loglik <- fit@fit$LLH;
		theaic <- aic(loglik,p,q+1,n)
		theaicc <- aicc(loglik,p,q+1,n)
		thebic <- bic(loglik,p,q+1,n)
		Matrix2.4th.2.n[teller,1]=p;Matrix2.4th.2.n[teller,2]=q;
		Matrix2.4th.2.n[teller,3]=loglik;Matrix2.4th.2.n[teller,4]=theaic;
		Matrix2.4th.2.n[teller,5]=theaicc;Matrix2.4th.2.n[teller,6]=thebic;
	}
}

pmaxi=8;qmaxi=8
Matrix2.5th.2.n=matrix(NA,nrow=(pmaxi+1)*qmaxi,ncol=6)

for (p in 1:(pmaxi+1)){
	p=p-1;
	for (q in 2:(qmaxi+1)){
		q=q-1;
		n=length(diff(robusta$X5th,lag=1));
		teller=q+p*qmaxi
		spec=ugarchspec(variance.model=list(model="fGARCH",submodel="NGARCH",garchOrder=c(p,q)),
		mean.model=list(armaOrder=c(0,1)));
		fit=ugarchfit(spec=spec,data=diff(robusta$X5th,lag=1));
		loglik <- fit@fit$LLH;
		theaic <- aic(loglik,p,q+1,n)
		theaicc <- aicc(loglik,p,q+1,n)
		thebic <- bic(loglik,p,q+1,n)
		Matrix2.5th.2.n[teller,1]=p;Matrix2.5th.2.n[teller,2]=q;
		Matrix2.5th.2.n[teller,3]=loglik;Matrix2.5th.2.n[teller,4]=theaic;
		Matrix2.5th.2.n[teller,5]=theaicc;Matrix2.5th.2.n[teller,6]=thebic;
	}
}

for (i in 70:72){
	Matrix2.5th.2.n[i,1]=8;Matrix2.5th.2.n[i,2]=i-64;
	Matrix2.5th.2.n[i,3]=-Inf;Matrix2.5th.2.n[i,4]=Inf;
	Matrix2.5th.2.n[i,5]=Inf;Matrix2.5th.2.n[i,6]=Inf; 
}

pmaxi=8;qmaxi=8
Matrix2.3rd.2.gjr=matrix(NA,nrow=(pmaxi+1)*qmaxi,ncol=6)

for (p in 1:(pmaxi+1)){
	p=p-1;
	for (q in 2:(qmaxi+1)){
		q=q-1;
		n=length(diff(robusta$X3rd,lag=1));
		teller=q+p*qmaxi
		spec=ugarchspec(variance.model=list(model="fGARCH",submodel="GJRGARCH",garchOrder=c(p,q)),
		mean.model=list(armaOrder=c(17,3)));
		fit=ugarchfit(spec=spec,data=diff(robusta$X3rd,lag=1));
		loglik <- fit@fit$LLH;
		theaic <- aic(loglik,p+17,q+3,n)
		theaicc <- aicc(loglik,p+17,q+3,n)
		thebic <- bic(loglik,p+17,q+3,n)
		Matrix2.3rd.2.gjr[teller,1]=p;Matrix2.3rd.2.gjr[teller,2]=q;
		Matrix2.3rd.2.gjr[teller,3]=loglik;Matrix2.3rd.2.gjr[teller,4]=theaic;
		Matrix2.3rd.2.gjr[teller,5]=theaicc;Matrix2.3rd.2.gjr[teller,6]=thebic;
	}
}

for (i in 65:72){
	Matrix2.3rd.2.gjr[i,1]=8;Matrix2.3rd.2.gjr[i,2]=i-64;
	Matrix2.3rd.2.gjr[i,3]=-Inf;Matrix2.3rd.2.gjr[i,4]=Inf;
	Matrix2.3rd.2.gjr[i,5]=Inf;Matrix2.3rd.2.gjr[i,6]=Inf; 
}

pmaxi=8;qmaxi=8
Matrix2.4th.2.gjr=matrix(NA,nrow=(pmaxi+1)*qmaxi,ncol=6)

for (p in 1:(pmaxi+1)){
	p=p-1;
	for (q in 2:(qmaxi+1)){
		q=q-1;
		n=length(diff(robusta$X4th,lag=1));
		teller=q+p*qmaxi
		spec=ugarchspec(variance.model=list(model="fGARCH",submodel="GJRGARCH",garchOrder=c(p,q)),
		mean.model=list(armaOrder=c(0,1)));
		fit=ugarchfit(spec=spec,data=diff(robusta$X4th,lag=1));
		loglik <- fit@fit$LLH;
		theaic <- aic(loglik,p,q+1,n)
		theaicc <- aicc(loglik,p,q+1,n)
		thebic <- bic(loglik,p,q+1,n)
		Matrix2.4th.2.gjr[teller,1]=p;Matrix2.4th.2.gjr[teller,2]=q;
		Matrix2.4th.2.gjr[teller,3]=loglik;Matrix2.4th.2.gjr[teller,4]=theaic;
		Matrix2.4th.2.gjr[teller,5]=theaicc;Matrix2.4th.2.gjr[teller,6]=thebic;
	}
}

for (i in 65:72){
	Matrix2.4th.2.gjr[i,1]=8;Matrix2.4th.2.gjr[i,2]=i-64;
	Matrix2.4th.2.gjr[i,3]=-Inf;Matrix2.4th.2.gjr[i,4]=Inf;
	Matrix2.4th.2.gjr[i,5]=Inf;Matrix2.4th.2.gjr[i,6]=Inf; 
}

pmaxi=8;qmaxi=8
Matrix2.5th.2.gjr=matrix(NA,nrow=(pmaxi+1)*qmaxi,ncol=6)

for (p in 1:(pmaxi+1)){
	p=p-1;
	for (q in 2:(qmaxi+1)){
		q=q-1;
		n=length(diff(robusta$X5th,lag=1));
		teller=q+p*qmaxi
		spec=ugarchspec(variance.model=list(model="fGARCH",submodel="GJRGARCH",garchOrder=c(p,q)),
		mean.model=list(armaOrder=c(0,1)));
		fit=ugarchfit(spec=spec,data=diff(robusta$X5th,lag=1));
		loglik <- fit@fit$LLH;
		theaic <- aic(loglik,p,q+1,n)
		theaicc <- aicc(loglik,p,q+1,n)
		thebic <- bic(loglik,p,q+1,n)
		Matrix2.5th.2.gjr[teller,1]=p;Matrix2.5th.2.gjr[teller,2]=q;
		Matrix2.5th.2.gjr[teller,3]=loglik;Matrix2.5th.2.gjr[teller,4]=theaic;
		Matrix2.5th.2.gjr[teller,5]=theaicc;Matrix2.5th.2.gjr[teller,6]=thebic;
	}
}

min(Matrix2.3rd.1.s[,4]);min(Matrix2.3rd.1.s[,5]);min(Matrix2.3rd.1.s[,6]);
[1] 12348.5
[1] 12349.72
[1] 12467.26
min(Matrix2.4th.1.s[,4]);min(Matrix2.4th.1.s[,5]);min(Matrix2.4th.1.s[,6]);
[1] 12214.79
[1] 12214.86
[1] 12235.5
min(Matrix2.5th.1.s[,4]);min(Matrix2.5th.1.s[,5]);min(Matrix2.5th.1.s[,6]);
[1] 12160.37
[1] 12160.41
[1] 12180.96
min(Matrix2.3rd.1.i[,4]);min(Matrix2.3rd.1.i[,5]);min(Matrix2.3rd.1.i[,6]);
[1] 12333.2
[1] 12334.16
[1] 12456.68
min(Matrix2.4th.1.i[,4]);min(Matrix2.4th.1.i[,5]);min(Matrix2.4th.1.i[,6]);
[1] 12217.28
[1] 12217.53
[1] 12241.02
min(Matrix2.5th.1.i[,4]);min(Matrix2.5th.1.i[,5]);min(Matrix2.5th.1.i[,6]);
[1] 12136.23
[1] 12136.72
[1] 12186.49
min(Matrix2.3rd.1.ap[,4]);min(Matrix2.3rd.1.ap[,5]);min(Matrix2.3rd.1.ap[,6]);
[1] 12489.27
[1] 12490.09
[1] 12602.47
min(Matrix2.4th.1.ap[,4]);min(Matrix2.4th.1.ap[,5]);min(Matrix2.4th.1.ap[,6]);
[1] 12387.58
[1] 12387.6
[1] 12403.01
min(Matrix2.5th.1.ap[,4]);min(Matrix2.5th.1.ap[,5]);min(Matrix2.5th.1.ap[,6]);
[1] 12345.94
[1] 12345.96
[1] 12361.38
min(Matrix2.3rd.2.t[,4]);min(Matrix2.3rd.2.t[,5]);min(Matrix2.3rd.2.t[,6]);
[1] 12350.61
[1] 12351.49
[1] 12468.95
min(Matrix2.4th.2.t[,4]);min(Matrix2.4th.2.t[,5]);min(Matrix2.4th.2.t[,6]);
[1] 12214.72
[1] 12214.75
[1] 12235.3
min(Matrix2.5th.2.t[,4]);min(Matrix2.5th.2.t[,5]);min(Matrix2.5th.2.t[,6]);
[1] 12157.64
[1] 12157.67
[1] 12178.22
min(Matrix2.3rd.2.av[,4]);min(Matrix2.3rd.2.av[,5]);min(Matrix2.3rd.2.av[,6]);
[1] 12316.61
[1] 12318.54
[1] 12458.3
min(Matrix2.4th.2.av[,4]);min(Matrix2.4th.2.av[,5]);min(Matrix2.4th.2.av[,6]);
[1] 12195.94
[1] 12196.23
[1] 12229.04
min(Matrix2.5th.2.av[,4]);min(Matrix2.5th.2.av[,5]);min(Matrix2.5th.2.av[,6]);
[1] 12145.41
[1] 12145.74
[1] 12173.14
min(Matrix2.3rd.2.na[,4]);min(Matrix2.3rd.2.na[,5]);min(Matrix2.3rd.2.na[,6]);
[1] 12326.71
[1] 12328.76
[1] 12463.74
min(Matrix2.4th.2.na[,4]);min(Matrix2.4th.2.na[,5]);min(Matrix2.4th.2.na[,6]);
[1] 12200.31
[1] 12200.48
[1] 12228.8
min(Matrix2.5th.2.na[,4]);min(Matrix2.5th.2.na[,5]);min(Matrix2.5th.2.na[,6]);
[1] 12146.72
[1] 12146.86
[1] 12173.78
min(Matrix2.3rd.2.n[,4]);min(Matrix2.3rd.2.n[,5]);min(Matrix2.3rd.2.n[,6]);
[1] 12353.6
[1] 12354.49
[1] 12471.94
min(Matrix2.4th.2.n[,4]);min(Matrix2.4th.2.n[,5]);min(Matrix2.4th.2.n[,6]);
[1] 12214.67
[1] 12214.7
[1] 12235.25
min(Matrix2.5th.2.n[,4]);min(Matrix2.5th.2.n[,5]);min(Matrix2.5th.2.n[,6]);
[1] 12159.35
[1] 12159.38
[1] 12179.93
min(Matrix2.3rd.2.gjr[,4]);min(Matrix2.3rd.2.gjr[,5]);min(Matrix2.3rd.2.gjr[,6]);
[1] 12350.86
[1] 12352.46
[1] 12474.41
min(Matrix2.4th.2.gjr[,4]);min(Matrix2.4th.2.gjr[,5]);min(Matrix2.4th.2.gjr[,6]);
[1] 12212.09
[1] 12212.12
[1] 12232.67
min(Matrix2.5th.2.gjr[,4]);min(Matrix2.5th.2.gjr[,5]);min(Matrix2.5th.2.gjr[,6]);
[1] 12157.79
[1] 12157.82
[1] 12178.37