Robust Empirical Optimization is Almost the Same as Mean-Variance Optimization

We formulate a distributionally robust optimization problem where the deviation of the alternative distribution is controlled by a φ-divergence penalty in the objective, and show that a large class of these problems are essentially equivalent to a mean-variance problem. We also show that while a "small amount of robustness" always reduces the in-sample expected reward, the reduction in the variance is almost an order of magnitude larger

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Year of publication:	2017
Authors:	Gotoh, Jun-ya
Other Persons:	Kim, Michael Jong (contributor) ; Lim, Andrew (contributor)
Publisher:	[2017]: [S.l.] : SSRN
Subject:	Theorie \| Theory \| Portfolio-Management \| Portfolio selection \| Mathematische Optimierung \| Mathematical programming \| Robustes Verfahren \| Robust statistics

Extent:	1 Online-Ressource (21 p)
Type of publication:	Book / Working Paper
Language:	English
Notes:	Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments November 17, 2017 erstellt
Other identifiers:	10.2139/ssrn.3073013 [DOI]
Classification:	C02 - Mathematical Methods ; C13 - Estimation ; C44 - Statistical Decision Theory; Operations Research ; C61 - Optimization Techniques; Programming Models; Dynamic Analysis
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://ebvufind01.dmz1.zbw.eu/10012943301