ccc stocks.r

mat <- read.csv(file="I:/New Project/Stocks/akcie_diff.csv",head=TRUE,sep=",")

library(tseries)

p1 = mat[,1]
p2 = mat[,2]
p3 = mat[,3]
p4 = mat[,4]
p5 = mat[,5]
p6 = mat[,6]
p = cbind(p1,p2,p3,p4,p5,p6)
y = p
y[,1] = y[,1]-mean(y[,1])
y[,2] = y[,2]-mean(y[,2])
y[,3] = y[,3]-mean(y[,3])
y[,4] = y[,4]-mean(y[,4])
y[,5] = y[,5]-mean(y[,5])
y[,6] = y[,6]-mean(y[,6])


T = length(y[,1])

library(ccgarch)
library(fGarch)

f1 = garchFit(~ garch(1,1), data=y[,1],include.mean=FALSE)
f1 = f1@fit$coef
f2 = garchFit(~ garch(1,1), data=y[,2],include.mean=FALSE)
f2 = f2@fit$coef
f3 = garchFit(~ garch(1,1), data=y[,3],include.mean=FALSE)
f3 = f3@fit$coef
f4 = garchFit(~ garch(1,1), data=y[,4],include.mean=FALSE)
f4 = f4@fit$coef
f5 = garchFit(~ garch(1,1), data=y[,5],include.mean=FALSE)
f5 = f5@fit$coef
f6 = garchFit(~ garch(1,1), data=y[,6],include.mean=FALSE)
f6 = f6@fit$coef

a = c(f1[1],f2[1],f3[1],f4[1],f5[1],f6[1])
A = diag(c(f1[2],f2[2],f3[2],f4[2],f5[2],f6[2]))
B = diag(c(f1[3],f2[3],f3[3],f4[3],f5[3],f6[3]))
cccpara = diag(c(1,1,1,1,1,1))

cccresults = eccc.estimation(a=a, A=A, B=B, R=cccpara ,dvar=y, model="diagonal")

cccresults$out
A=cccresults$out
B=cccresults$h
write.table(A,file="I:/New Project/Stocks/ccc_stocks_final.csv",sep = ",",col.names = NA,qmethod = "double")
write.table(B,file="I:/New Project/Stocks/ccc_stocks_variance_diag.csv",sep = ",",col.names = NA,qmethod = "double")