Generalized Precision Matrices for Non-gaussian Distributions: Theory and Portfolio Applications

Karoline Bax (), Alessandro Fulci (), Sandra Paterlini () and Emanuele Taufer ()
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Karoline Bax: University of Trento, Department of Economics and Management
Alessandro Fulci: University of Trento, Department of Economics and Management
Sandra Paterlini: University of Trento, Department of Economics and Management
Emanuele Taufer: University of Trento, Department of Economics and Management

A chapter in Statistical Dependence Modeling, 2026, pp 317-340 from Springer

Abstract: Abstract We introduce a general measure of conditional local dependence for multivariate vectors and use it to define a generalized precision matrix (GPM) that is valid for any statistical distribution. We show that, in the Gaussian case, the GPM coincides with the inverse of the covariance matrix. Additionally, we derive the GPM analytically for the multivariate t-Student, multivariate skew-normal, and multivariate skew-t distributions. Using simulation, we compare the performance of the different estimators, discussing their properties. As a real-world application, we test the GPM within the Markowitz minimum variance portfolio framework, demonstrating that the multivariate skew-t model provides a superior fit during financial crisis periods.

Keywords: Conditional local dependence; Multivariate elliptical distributions; Multivariate skew-symmetric distributions; Precision matrix; Minimum-variance portfolio (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-14252-8_13

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