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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-21933
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dc.contributor.authorRosadi, Dedi-
dc.contributor.authorSetiawan, Ezra Putranda-
dc.contributor.authorTempl, Matthias-
dc.contributor.authorFilzmoser, Peter-
dc.date.accessioned2021-03-04T15:05:28Z-
dc.date.available2021-03-04T15:05:28Z-
dc.date.issued2020-
dc.identifier.issn2214-7160de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/21933-
dc.description.abstractThis 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.de_CH
dc.language.isoende_CH
dc.publisherElsevierde_CH
dc.relation.ispartofOperations Research Perspectivesde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectFinancede_CH
dc.subjectMarkowitz portfoliode_CH
dc.subjectTransaction lotde_CH
dc.subjectRobust estimationde_CH
dc.subjectGenetic algorithmde_CH
dc.subject.ddc510: Mathematikde_CH
dc.titleRobust covariance estimators for mean-variance portfolio optimization with transaction lotsde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.1016/j.orp.2020.100154de_CH
dc.identifier.doi10.21256/zhaw-21933-
zhaw.funding.euNode_CH
zhaw.issue100154de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume7de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedStatistik und Quantitative Financede_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Rosadi, D., Setiawan, E. P., Templ, M., & Filzmoser, P. (2020). Robust covariance estimators for mean-variance portfolio optimization with transaction lots. Operations Research Perspectives, 7(100154). https://doi.org/10.1016/j.orp.2020.100154
Rosadi, D. et al. (2020) ‘Robust covariance estimators for mean-variance portfolio optimization with transaction lots’, Operations Research Perspectives, 7(100154). Available at: https://doi.org/10.1016/j.orp.2020.100154.
D. Rosadi, E. P. Setiawan, M. Templ, and P. Filzmoser, “Robust covariance estimators for mean-variance portfolio optimization with transaction lots,” Operations Research Perspectives, vol. 7, no. 100154, 2020, doi: 10.1016/j.orp.2020.100154.
ROSADI, Dedi, Ezra Putranda SETIAWAN, Matthias TEMPL und Peter FILZMOSER, 2020. Robust covariance estimators for mean-variance portfolio optimization with transaction lots. Operations Research Perspectives. 2020. Bd. 7, Nr. 100154. DOI 10.1016/j.orp.2020.100154
Rosadi, Dedi, Ezra Putranda Setiawan, Matthias Templ, and Peter Filzmoser. 2020. “Robust Covariance Estimators for Mean-Variance Portfolio Optimization with Transaction Lots.” Operations Research Perspectives 7 (100154). https://doi.org/10.1016/j.orp.2020.100154.
Rosadi, Dedi, et al. “Robust Covariance Estimators for Mean-Variance Portfolio Optimization with Transaction Lots.” Operations Research Perspectives, vol. 7, no. 100154, 2020, https://doi.org/10.1016/j.orp.2020.100154.


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