A copula-based data augmentation strategy for the sensitivity analysis of extreme operational losses
针对极端损失观测值少导致Shapley效应估计困难的问题，提出用vine copula扩充极端样本后再计算Shapley效应，以评估宏观金融变量对银行操作损失的重要性，模拟和UniCredit银行数据验证了有效性。
In this work, we aim to assess the importance of macroeconomic and financial variables for operational losses of UniCredit Bank. To achieve this, we consider the Shapley effects as a variance-based measure of importance. However, the small number of observations of extreme losses makes the estimation of the Shapley effects challenging. To address this issue, we proposed augmenting the sample of extreme observations using vine copulas and calculating the Shapley effects on the augmented sample. The effectiveness of this procedure is supported by a numerical simulation. Findings obtained with our methodology applied to the UniCredit Bank data show its usefulness for the risk management of operational losses.