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Optimization of Convex Risk Functions

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type , Book , Preprint 01 Jan 2004 Germany Publisher:Elsevier BVJournal:SSRN Electronic Journal (eissn: 1556-5068,

Authors: Andrzej Ruszczynski; Alexander Shapiro;

doi: 10.2139/ssrn.675461 , 10.1287/moor.1050.0186

Optimization of Convex Risk Functions

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Abstract

We consider optimization problems involving convex risk functions. By employing techniques of convex analysis and optimization theory in vector spaces of measurable functions, we develop new representation theorems for risk models, and optimality and duality theory for problems with convex risk functions.

Country

Germany

Related Organizations

Humboldt-Universität zu Berlin
Germany
Georgia Institute of Technology
United States
Rutgers, The State University of New Jersey
United States

Keywords

Convex analysis, ddc:510, mean-variance models, duality, 510 Mathematik, Convex analysis, stochastic optimization, risk measures, mean- variance models, duality, risk measures, stochastic optimization

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	354
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 1%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 1%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%