Sparse and robust normal and \(t\)-portfolios by penalized \(L_q\)-likelihood minimization

DOI10.1016/j.ejor.2015.08.056zbMath1346.91205OpenAlexW2199123602MaRDI QIDQ322443

Davide Ferrari, Margherita Giuzio, Sandra Paterlini

Publication date: 7 October 2016

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2015.08.056

zbMATH Keywords

investment analysissparsityindex trackingpenalized least squares\(q\)-entropy

Mathematics Subject Classification ID

Statistical methods; risk measures (91G70) Portfolio theory (91G10)

Related Items (5)

Least informative distributions in maximum \(q\)-log-likelihood estimation ⋮ Penalized Lq-likelihood estimators and variable selection in linear regression models ⋮ Genetic algorithm versus classical methods in sparse index tracking ⋮ Sparse estimation via lower-order penalty optimization methods in high-dimensional linear regression ⋮ Doubly reweighted estimators for the parameters of the multivariate t-distribution

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