论文标题
强大风险评估和扭曲的混合方法的仿真方法
Simulation Methods for Robust Risk Assessment and the Distorted Mix Approach
论文作者
论文摘要
不确定性需要合适的技术进行风险评估。结合了随机近似值和随机平均近似，我们提出了一种有效的算法来计算面对尾巴不确定性的最坏情况下的风险最差的平均值。依赖性是通过扭曲的混合方法对依赖性建模的，该方法可以灵活地将不同的Copulas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。
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.
