I need to compute the efficient frontier with different risk measure and to use a bootstrapping technique to simulate possible outcome. However, now I'm stuck: what I want to do is to generate via a loop (which will be integrated later into a function) multiple efficient frontier, each one associated to a possible future outcome, and to plot them on the same figure in such a way to see how they may change as the simulation goes on. Here is the loop that I wrote so far:
for (i in 1:B) {
idx <- sample(1:N, N, replace = TRUE)
new.x <- x[idx, ]
µ.b <- apply(X = new.x, 2, FUN = mean)
range.b[, i] <- seq(from = min(µ.b), to = max(µ.b), length.out = steps)
sigma.b <- apply(X = new.x, 2, FUN = sd)
riskCov.b[, i] <- sapply(range.b[, i], function(targetReturn) {
w <- MV_QP(new.x, targetReturn, Sigma)
sd(c(new.x %*% w))
})
xlim.b <- range(c(sigma.b, riskCov.b[, 1]), na.rm = TRUE)
ylim.b <- range(µ.b)
par(new = TRUE)
plot(x = riskCov.b[, i], y = range.b[, i], type = "l", xlim = xlim.b, ylim = ylim.b, xlab = "Risk", ylab = "Return", main = "Resampling EFs")
}
but the problem is that the elements on the x and y axis are rewriting each time the loop runs. How can this problem be solved?