I'm working on a Monte Carlo function for valuing several assets with partially correlated returns. Currently, I just generate a covariance matrix and feed to the the
rmvnorm() function in R. (Generates correlated random values.)
However, looking at the distributions of returns of an asset, it is not normally distributed.
This is really a two part question:
1) How can I estimate some kind of PDF or CDF when all I have is some real-world data without a known distribution?
2) How can I generate correlated values like rmvnorm, but for this unknown (and non-normal) distribution?
The distributions do not appear to fit any known distribution. I think it would be very dangerous to assume a parametric and then use that for monte carlo estimation.
Isn't there some kind of bootstrap or "empirical monte carlo" method I can look at?