Higher moment coherent risk measures

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50 Scopus citations

Abstract

The paper considers modelling of risk-averse preferences in stochastic programming problems using risk measures. We utilize the axiomatic foundation of coherent risk measures and deviation measures in order to develop simple representations that express risk measures via specially constructed stochastic programming problems. Using the developed representations, we introduce a new family of higher-moment coherent risk measures (HMCR), which includes, as a special case, the Conditional Value-at-Risk measure. It is demonstrated that the HMCR measures are compatible with the second order stochastic dominance and utility theory, can be efficiently implemented in stochastic optimization models, and perform well in portfolio optimization case studies.

Original languageEnglish (US)
Pages (from-to)373-387
Number of pages15
JournalQuantitative Finance
Volume7
Issue number4
DOIs
StatePublished - Aug 1 2007
Externally publishedYes

Keywords

  • Portfolio optimization
  • Risk measures
  • Stochastic dominance
  • Stochastic programming

ASJC Scopus subject areas

  • Finance
  • Economics, Econometrics and Finance(all)

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