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The computation of the worst conditional expectation

机译:最坏条件期望的计算

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Recent advances in risk theory identify risk as a measure related to the tail of a probability distribution function, since it represents the "worst" outcomes of the random variable. Measures like value-at-risk (VaR), conditional VaR, expected shortfall and so on have become familiar operational tools for many financial applications. In this paper, one of these measures, the worst conditional expectation with threshold α of a discrete random variable Z, shortly WCE_α(Z), is considered. It has been found that its computation can be formulated as a fractional integer programming problem with a single linear constraint, but its complexity is NP-hard, therefore it must be solved by implicit enumeration. Due to its similarity with the knapsack problem, it has been found that a good upper bound and a sharp data structure allow the implementation of a branch&bound that is able to solve realistic size problems in less than one hundredth of a second.
机译:风险理论的最新进展将风险识别为与概率分布函数尾部相关的度量,因为它代表了随机变量的“最差”结果。诸如风险价值(VaR),有条件的VaR,预期的短缺等措施已成为许多金融应用程序熟悉的操作工具。在本文中,考虑了其中一种措施,即离散随机变量Z的阈值为α的最差条件期望值,即WCE_α(Z)。已经发现,可以将其计算公式化为具有单个线性约束的分数整数规划问题,但是其复杂性是NP难的,因此必须通过隐式枚举来解决。由于其与背包问题的相似性,已发现良好的上限和清晰的数据结构允许实现能够在不到百分之一秒的时间内解决实际大小问题的分支定界法。

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