In financial risk management, coherent risk measures have been proposed as a way to avoid undesirable properties of measures such as value at risk that discourage diversification and do not account for the magnitude of the largest, and therefore most serious, losses. A coherent risk measure equals the maximum expected loss under several different probability measures, and these measures are analogous to ``populations'' or ``systems'' in the ranking-and-selection literature. However, unlike in ranking and selection, here it is the value of the maximum expectation under any of the probability measures, and not the identity of the probability measure that attains it, that is of interest. We propose procedures to form fixed-width, simulation-based confidence intervals for the maximum of several expectations, explore their correctness and computational efficiency, and illustrate them on risk management problems. The availability of efficient algorithms for computing coherent risk measures will encourage their use for improved risk management.

Last modified

• 05/10/2018

Creator

• Vadim Lesnevski

DOI

• https://doi.org/10.21985/N2DH59

Subject

• Industrial Engineering and Management Sciences

Keyword

• generalized scenarios
• simulation
• ranking and selection
• coherent risk measures
• value-at-risk
• risk management

Date created

• 2006-12-05

Resource type

• Dissertation

Rights statement

• In Copyright