The importance of operational risk management in financial and commodity markets has increased significantly over the last few decades. This paper demonstrates the application of a nonhomogeneous Poisson model and dynamic extreme value theory (EVT) incorporating covariates on estimating frequency, severity and risk measures for operational risk. Compared with a classical EVT approach, the dynamic EVT gives a better performance with respect to the statistical fit and realism. It is also flexible enough to handle different types of empirical data. In our model, we include firm-specific covariates associated with internal control weaknesses (ICWs) and show empirically that firms with higher incidences of selected ICWs have higher time-varying severities for operational risk. Our methodology provides risk managers and regulators with a tool that uncovers the nonobvious patterns hidden in operational risk data.

中文翻译：

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根据协变量对操作风险建模：实证调查

在过去的几十年里，操作风险管理在金融和商品市场中的重要性显着增加。本文展示了非齐次泊松模型和动态极值理论 (EVT) 的应用，其中包含协变量在估计操作风险的频率、严重性和风险度量方面。与经典 EVT 方法相比，动态 EVT 在统计拟合和真实性方面具有更好的性能。它也足够灵活，可以处理不同类型的经验数据。在我们的模型中，我们包括与内部控制弱点 (ICW) 相关的公司特定协变量，并凭经验表明，选定 ICW 发生率较高的公司具有更高的操作风险时变严重性。