随机优势
一致性风险度量
风险度量
预期短缺
随机规划
光谱风险度量
投资组合优化
动态风险度量
度量(数据仓库)
数学优化
力矩(物理)
随机优化
公理
风险价值
文件夹
数学金融学
计算机科学
数学
计量经济学
风险管理
经济
数据挖掘
金融经济学
管理
物理
经典力学
几何学
标识
DOI:10.1080/14697680701458307
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI