  In this study we suggest a portfolio selection framework based on
option-implied information and multivariate non-Gaussian models. The proposed
models incorporate skewness, kurtosis and more complex dependence structures
among stocks log-returns than the simple correlation matrix. The two models
considered are a multivariate extension of the normal tempered stable (NTS)
model and the generalized hyperbolic (GH) model, respectively, and the
connection between the historical measure P and the risk-neutral measure Q is
given by the Esscher transform. We consider an estimation method that
simultaneously calibrate the time series of univariate log-returns and the
univariate observed volatility smile. To calibrate the models, there is no need
of liquid multivariate derivative quotes. The method is applied to fit a
50-dimensional series of stock returns, to evaluate widely known portfolio risk
measures and to perform a portfolio selection analysis.
