Simulation of coherent risk measures based on generalized scenarios

Vadim Lesnevski*, Barry L. Nelson, Jeremy Staum

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1756-1769
Number of pages14
JournalManagement Science
Issue number11
StatePublished - Nov 1 2007


  • Coherent risk measures
  • Good deal bounds
  • Ranking and selection
  • Risk management
  • Simulation

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research


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