Improved portfolio selection using market expectations through penalization (2018)
The portfolio selection problem can be viewed as an optimization problem that maximizes a risk-return rela-tion. It has of course, an objective function, decision variables and input parameters: expected returns and covariance between them. These parameters' real values are not achievable for us, therefore, estimations are needed and they are commonly based on historical data; apart from the error that this haul into the problem, it is not all the information that we could use. Options market prices are a rich source of investors' expecta-tions according to their knowledge about options' underlyings, thus, we propose the usage of a new estimator for risk and return that mixes up historical and implicit information into the portfolio selection problem. We implemented the new estimators for the Mean-VAR and Mean-VaR2 problems using an elastic-net model that helps us lessening the risk of all the estimations made.