Determining an Efficient Frontier in a Stochastic Moments Setting

We propose a new approach to portfolio optimization under uncertainty in the assets’ return distribution. The main idea is to optimize penalty functionals which model the investor’s aversion against this uncertainty. We focus on two specific proposals with an important practical feature: the stability of the resulting portfolio composition under changing input variables. We review existing approaches and compare them with ours. We show with an example from the Brazilian market that the penalty function approach does indeed increase stability.