Comparison of robust optimization models for portfolio optimization

Warning The system is temporarily closed to updates for reporting purpose.

Arabacı, Polen (2020) Comparison of robust optimization models for portfolio optimization. [Thesis]

[thumbnail of 10354717_Arabacı__Polen.pdf] PDF
10354717_Arabacı__Polen.pdf

Download (666kB)

Abstract

Using optimization techniques in portfolio selection has attracted significant attention in financial decisions. However, one of the main challenging aspects faced in optimal portfolio selection is that the models are sensitive to the estimations of the uncertain parameters. In this thesis, we focus on the robust optimization problems to incorporate uncertain parameters into the standard portfolio problems. First, we provide an overview of well-known optimization models when risk measures considered are variance, Value-at-Risk, and Conditional Value-at-Risk. Then, we provide reformulations of the robust versions of these portfolio optimization problems as conic programs when the uncertainty sets involve polytopic, ellipsoidal, or budgeted uncertainty for either mean return vector or covariance matrix or both. Finally, we conduct a computational study on two real data sets to evaluate and compare the effectiveness of the robust optimization approaches
Item Type: Thesis
Uncontrolled Keywords: portfolio optimization. -- robust optimization. -- conic programming. -- portföy eniyilemesi. -- gürbüz eniyileme. -- konik programlama.
Subjects: T Technology > T Technology (General) > T055.4-60.8 Industrial engineering. Management engineering
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Industrial Engineering
Faculty of Engineering and Natural Sciences
Depositing User: IC-Cataloging
Date Deposited: 24 Oct 2020 17:59
Last Modified: 26 Apr 2022 10:34
URI: https://research.sabanciuniv.edu/id/eprint/41188

Actions (login required)

View Item
View Item