Conditional Value-at-Risk Approximation to Value-at-Risk Constrained Programs: A Remedy via Monte Carlo
Published Online:13 Feb 2014https://doi.org/10.1287/ijoc.2013.0572
Supplemental Material
ijoc.2013.0572-sm-ec.pdf (132 KB)
- Cited by
- Expert Systems with Applications, Vol. 299
- Journal of Industrial and Management Optimization, Vol. 21, No. <![CDATA[4]]>
- Nonlinear Analysis: Hybrid Systems, Vol. 54
- 7 May 2022 | Set-Valued and Variational Analysis, Vol. 30, No. 3
- 25 March 2022 | INFORMS Journal on Computing, Vol. 34, No. 4
- 25 November 2018 | Naval Research Logistics (NRL), Vol. 65, No. 5
- 13 March 2017 | Journal of the Operations Research Society of China, Vol. 5, No. 4
- 22 January 2015 | Quantitative Finance Letters, Vol. 3, No. 1
- 18 November 2014 | ACM Transactions on Modeling and Computer Simulation, Vol. 24, No. 4
Volume 26, Issue 2
Spring 2014
Pages 199-413
Article Information
Supplemental Material
Metrics
Information
- Received:July 01, 2012
- Accepted:July 01, 2013
- Published Online:February 13, 2014
Copyright © 2014, INFORMS
Cite as
L. Jeff Hong, Zhaolin Hu, Liwei Zhang (2014) Conditional Value-at-Risk Approximation to Value-at-Risk Constrained Programs: A Remedy via Monte Carlo. INFORMS Journal on Computing 26(2):385-400.
https://doi.org/10.1287/ijoc.2013.0572
Keywords
