Robust covariance estimators for mean-variance portfolio optimization with transaction lots
Loading...
Author (Corporation)
Publication date
2020
Typ of student thesis
Course of study
Collections
Type
01A - Journal article
Editors
Editor (Corporation)
Supervisor
Parent work
Operations Research Perspectives
Special issue
DOI of the original publication
Link
Series
Series number
Volume
7
Issue / Number
100154
Pages / Duration
Patent number
Publisher / Publishing institution
Elsevier
Place of publication / Event location
Edition
Version
Programming language
Assignee
Practice partner / Client
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 esti- mators outperform the classical MLE when data contain outliers and when the lots have moderate sizes, e.g. 500 shares or less per lot.
Keywords
Subject (DDC)
Event
Exhibition start date
Exhibition end date
Conference start date
Conference end date
Date of the last check
ISBN
ISSN
2214-7160
Language
English
Created during FHNW affiliation
No
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Gold
License
Citation
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