Scenario generation for stochastic programs with tail risk measure
Scenario generation for stochastic programs with tail risk measure
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发表时间：
2015-11
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通讯作者：
Jamie Fairbrother;Amanda G. Turner;S. Wallace
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作者：
Jamie Fairbrother;Amanda G. Turner;S. Wallace
在随机规划中使用尾部风险度量（如风险价值和条件风险价值）来减轻或减少大损失的可能性。然而，由于尾部风险度量仅取决于分布的上尾，因此这些问题的情景生成是困难的，因为标准方法（例如抽样）通常不足以代表这些区域。我们提出了一个基于问题的方法来生成场景的随机程序，使用尾部风险措施，并证明了这种方法对一类投资组合选择问题。
Tail risk measures such as Value-at-Risk and Conditional Value-at-Risk are used in stochastic programming to mitigate or reduce the probability of large losses. However, because tail risk measures only depend on the upper tail of a distribution, scenario generation for these problems is difficult as standard methods such as sampling will typically inadequately represent these areas. We present a problem-based approach to scenario generation for stochastic programs which use tail risk measures, and demonstrate this approach on a class of portfolio selection problems.