n <- 1100
a <- c(0.1, 0.5, 0.2) # ARCH(2) coefficients
e <- rnorm(n)
x <- double(n)
x[1:2] <- rnorm(2, sd = sqrt(a[1]/(1.0-a[2]-a[3])))
for(i in 3:n) # Generate ARCH(2) process
{
x[i] <- e[i]*sqrt(a[1]+a[2]*x[i-1]^2+a[3]*x[i-2]^2)
}
x <- ts(x[101:1100])
x.arch <- garch(x, order = c(0,2)) # Fit ARCH(2)
summary(x.arch) # Diagnostic tests
plot(x.arch)
data(EuStockMarkets)
dax <- diff(log(EuStockMarkets))[,"DAX"]
dax.garch <- garch(dax) # Fit a GARCH(1,1) to DAX returns
summary(dax.garch) # ARCH effects are filtered. However,
plot(dax.garch) # conditional normality seems to be violated
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