Consider some ARCH() process, say ARCH(), where with a Gaussian (strong) white noise . > n=500 > a1=0.8 > a2=0.0 > w= 0.2 > set.seed(1) > eta=rnorm(n) > epsilon=rnorm(n) > sigma2=rep(w,n) > for(t in 3:n){ + sigma2=w+a1*epsilon^2+a2*epsilon^2 + epsilon=eta*sqrt(sigma2) + } > par(mfrow=c(1,1)) > plot(epsilon,type="l",ylim=c(min(epsilon)-.5,max(epsilon))) > lines(min(epsilon)-1+sqrt(sigma2),col="red") (the red line is the conditional variance process). > par(mfrow=c(1,2)) > acf(epsilon,lag=50,lwd=2)...