Non-smooth optimization methods for computation of the Conditional Value-at-risk and portfolio optimization

We examine numerical performance of various methods of calculation of the Conditional Value-at-risk (CVaR), and portfolio optimization with respect to this risk measure. We concentrate on the method proposed by Rockafellar and Uryasev in (Rockafellar, R.T. and Uryasev, S., 2000, Optimization of cond...

Full description

Saved in:
Bibliographic Details
Published inOptimization Vol. 55; no. 5-6; pp. 459 - 479
Main Authors Beliakov, Gleb, Bagirov, Adil
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis Group 01.10.2006
Taylor & Francis LLC
Subjects
Conditional Value-at-risk
Expected shortfall
Linear programming
Mathematics Subject Classifications 2000: 90C05
Non-smooth optimization
Numerical optimization
Optimization
Optimization techniques
Portfolio optimization
Risk assessment
Studies
Online AccessGet full text
ISSN0233-1934
1029-4945
DOI10.1080/02331930600816353

Cover

More Information
Summary:We examine numerical performance of various methods of calculation of the Conditional Value-at-risk (CVaR), and portfolio optimization with respect to this risk measure. We concentrate on the method proposed by Rockafellar and Uryasev in (Rockafellar, R.T. and Uryasev, S., 2000, Optimization of conditional value-at-risk. Journal of Risk, 2, 21-41), which converts this problem to that of convex optimization. We compare the use of linear programming techniques against a non-smooth optimization method of the discrete gradient, and establish the supremacy of the latter. We show that non-smooth optimization can be used efficiently for large portfolio optimization, and also examine parallel execution of this method on computer clusters.
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0233-1934
1029-4945
DOI:10.1080/02331930600816353