Higher Moment Coherent Risk Measures
نویسنده
چکیده
The paper considers modeling 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.
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Acknowledgement 1 The authors thank Jacques Carette for comments and useful suggestions that have resulted in a valuable improvement of the present manuscript. Financial Support from Eleusi Research Center is gratefully acknowledged.
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