  The Markowitz model is still the cornerstone of modern portfolio theory. In
particular, when focusing on the minimum-variance portfolio, the covariance
matrix or better its inverse, the so-called precision matrix, is the only input
required. So far, most scholars worked on improving the estimation of the
input, however little attention has been given to the limitations of the
inverse covariance matrix when capturing the dependence structure in a
non-Gaussian setting. While the precision matrix allows to correctly understand
the conditional dependence structure of random vectors in a Gaussian setting,
the inverse of the covariance matrix might not necessarily result in a reliable
source of information when Gaussianity fails. In this paper, exploiting the
local dependence function, different definitions of the generalized precision
matrix (GPM), which holds for a general class of distributions, are provided.
In particular, we focus on the multivariate t-Student distribution and point
out that the interaction in random vectors does not depend only on the inverse
of the covariance matrix, but also on additional elements. We test the
performance of the proposed GPM using a minimum-variance portfolio set-up by
considering S\&P 100 and Fama and French industry data. We show that portfolios
relying on the GPM often generate statistically significant lower out-of-sample
variances than state-of-art methods.
