Uncertainty requires suitable techniques for risk assessment. Combining stochastic approximation and stochastic average approximation, we propose an efficient algorithm to compute the worst case average value at risk in the face of tail uncertainty. Dependence is modelled by the distorted mix method that flexibly assigns different copulas to different regions of multivariate distributions. We illustrate the application of our approach in the context of financial markets and cyber risk.

中文翻译：

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稳健风险评估的模拟方法和扭曲混合方法

不确定性需要合适的风险评估技术。结合随机近似和随机平均近似，我们提出了一种有效的算法来计算面对尾部不确定性时面临风险的最坏情况平均值。依赖是通过扭曲混合方法建模的，该方法灵活地将不同的 copula 分配给多元分布的不同区域。我们说明了我们的方法在金融市场和网络风险的背景下的应用。