Artikel

Robust covariance estimators for mean-variance portfolio optimization with transaction lots

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.

Sprache
Englisch

Erschienen in
Journal: Operations Research Perspectives ; ISSN: 2214-7160 ; Volume: 7 ; Year: 2020 ; Pages: 1-11 ; Amsterdam: Elsevier

Klassifikation
Wirtschaft
Thema
Finance
Markowitz portfolio
Transaction lots
Robust estimation
Genetic algorithm

Ereignis
Geistige Schöpfung
(wer)
Rosadi, Dedi
Setiawan, Ezra Putranda
Templ, Matthias
Filzmoser, Peter
Ereignis
Veröffentlichung
(wer)
Elsevier
(wo)
Amsterdam
(wann)
2020

DOI
doi:10.1016/j.orp.2020.100154
Handle
Letzte Aktualisierung
10.03.2025, 11:44 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Rosadi, Dedi
  • Setiawan, Ezra Putranda
  • Templ, Matthias
  • Filzmoser, Peter
  • Elsevier

Entstanden

  • 2020

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