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Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Robust covariance estimators for mean-variance portfolio optimization with transaction lots
Authors: Rosadi, Dedi
Setiawan, Ezra Putranda
Templ, Matthias
Filzmoser, Peter
et. al: No
DOI: 10.1016/j.orp.2020.100154
Published in: Operations Research Perspectives
Volume(Issue): 7
Issue: 100154
Issue Date: 2020
Publisher / Ed. Institution: Elsevier
ISSN: 2214-7160
Language: English
Subjects: Finance; Markowitz portfolio; Transaction lot; Robust estimation; Genetic algorithm
Subject (DDC): 510: Mathematics
Abstract: This study presents an improvement to the mean-variance portfolio optimization model, by considering both the integer transaction lots and a robust estimator of the covariance matrices. Four robust estimators were tested, namely the Minimum Covariance Determinant, the S, the MM, and the Orthogonalized Gnanadesikan–Kettenring estimator. These integer optimization problems were solved using genetic algorithms. We introduce the lot turnover measure, a modified portfolio turnover, and the Robust Sharpe Ratio as the measure of portfolio performance. Based on the simulation studies and the empirical results, this study shows that the robust estimators outperform the classical MLE when data contain outliers and when the lots have moderate sizes, e.g. 500 shares or less per lot.
Fulltext version: Published version
License (according to publishing contract): CC BY 4.0: Attribution 4.0 International
Departement: School of Engineering
Organisational Unit: Institute of Data Analysis and Process Design (IDP)
Appears in collections:Publikationen School of Engineering

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