Recently I found myself reading more about Monte Carlo approach in m.v. portfolio optimization framework.
I already discuss the topic on this forum (if interested please consider the following links - Monte Carlo (resampling) in m.v. portfolio optimization , Quasi Random Monte Carlo in m.v. portfolio optimization ) however the following question arises:
What does it means "draw" or "generate" random returns samples from the assets distribution or from the multivariate distribution?
All literature I have been able to read until now seem not to address the "practical aspects" of the Monte Carlo approach (only its application in different quantitative finance problems). Of course simply calling a random generating function in our code (i.e.
scipy python package) is what probably came first to a reader's mind however I think there is or could be more than this.
Specifically which other techniques/methods one can apply? What is the best practice in the industry?
Maybe one should randomly generate prices instead returns and apply a random walks model (econometric models, i.e. arithmetic, geometric etc.).