ACADSTAFF UGM

CREATION
Title : Robust covariance estimators for mean-variance portfolio optimization with transaction lots
Author :

Prof. Dr.rer.nat. Dedi Rosadi, S.Si., M.Sc. (1) EZRA PUTRANDA S (2) Matthias Templ (3) Peter Filzmoser (4)

Date : 6 2020
Keyword : Finance,Markowitz portfolio,Transaction lots,Robust estimation,Genetic algorithm Finance,Markowitz portfolio,Transaction lots,Robust estimation,Genetic algorithm
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.
Group of Knowledge : Statistik
Original Language : English
Level : Internasional
Status :
Published
Document
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