Article ID: | iaor200971635 |
Country: | United States |
Volume: | 57 |
Issue: | 5 |
Start Page Number: | 1155 |
End Page Number: | 1168 |
Publication Date: | Sep 2009 |
Journal: | Operations Research |
Authors: | Fukushima Masao, Zhu Shushang |
Keywords: | risk |
This paper considers the worst-case Conditional Value-at-Risk (CVaR) in the situation where only partial information on the underlying probability distribution is available. The minimization of the worst-case CVaR under mixture distribution uncertainty, box uncertainty, and ellipsoidal uncertainty are investigated. The application of the worst-case CVaR to robust portfolio optimization is proposed, and the corresponding problems are cast as linear programs and second-order cone programs that can be solved efficiently. Market data simulation and Monte Carlo simulation examples are presented to illustrate the proposed approach.