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Worst-case conditional value-at-risk with application to robust portfolio management. (English) Zbl 1233.91254
Summary: 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.

MSC:
91G10 Portfolio theory
91B30 Risk theory, insurance (MSC2010)
Software:
SeDuMi
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