Quantifying and Stress Testing Operational Risk with Peer Banks' Data

One of the main challenges that banks face in modeling operational risk is the instability of risk estimates caused by heavy-tailed and insufficient loss data. To address these issues, we propose a loss scaling method to combine a bank's internal loss data with external loss data of other banks. Using supervisory operational loss data from large U.S. bank holding companies, we find that the severity of tail losses is related to bank size, while smaller losses are not. Based on this finding we propose scaling tail losses using total assets as a scaling factor. We demonstrate that our method of incorporating scaled external data improves the robustness of operational risk estimates. In addition, our scaling method helps depict the relationship between tail operational losses and macroeconomic variables. We demonstrate an application of the method to stress testing operational risk to severe macroeconomic shocks