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Working Paper
The Information Value of Past Losses in Operational Risk
Operational risk is a substantial source of risk for US banks. Improving the performance of operational risk models allows banks’ management to make more informed risk decisions by better matching economic capital and risk appetite, and allows regulators to enhance their understanding of banks’ operational risk. We show that past operational losses are informative of future losses, even after controlling for a wide range of financial characteristics. We propose that the information provided by past losses results from them capturing hard to quantify factors such as the quality of ...
Working Paper
Benchmarking Operational Risk Models
The 2004 Basel II accord requires internationally active banks to hold regulatory capital for operational risk, and the Federal Reserve's Comprehensive Capital Analysis and Review (CCAR) requires banks to project operational risk losses under stressed scenarios. As a result, banks subject to these rules have measured and managed operational risk more rigorously. But some types of operational risk - particularly legal risk - are challenging to model because such exposures tend to be fat-tailed. Tail operational risk losses have significantly impacted banks' balance sheets and income ...
Working Paper
Predicting Operational Loss Exposure Using Past Losses
Operational risk models, such as the loss distribution approach, frequently use past internal losses to forecast operational loss exposure. However, the ability of past losses to predict exposure, particularly tail exposure, has not been thoroughly examined in the literature. In this paper, we test whether simple metrics derived from past loss experience are predictive of future tail operational loss exposure using quantile regression. We find evidence that past losses are predictive of future exposure, particularly metrics related to loss frequency.
Discussion Paper
Coming to Terms with Operational Risk
The term ?operational risk? often evokes images of catastrophic events like hurricanes and earthquakes. For financial institutions, however, operational risk has a broader scope, encompassing losses related to fraud, rogue trading, product misrepresentation, computer and system failures, and cyberattacks, among other things. In this blog post, we discuss how operational risk has come into greater focus over the past two decades?to the point that it now accounts for more than a quarter of financial institutions? regulatory capital.
Working Paper
Are the Largest Banking Organizations Operationally More Risky?
This study demonstrates that, among large U.S. bank holding companies (BHCs), the largest ones are exposed to more operational risk. Specifically, they have higher operational losses per dollar of total assets, a result largely driven by the BHCs' failure to meet professional obligations to clients and/or faulty product design. Operational risk at the largest U.S. institutions is also found to: (i) be particularly persistent, (ii) have a counter-cyclical component (higher losses occur during economic downturns) and (iii) materialize through more frequent tail-risk events. We illustrate two ...
Working Paper
Climate Risks in the U.S. Banking Sector: Evidence from Operational Losses and Extreme Storms
Using supervisory data from large U.S. bank holding companies (BHCs), we document that BHCs suffer more operational losses during episodes of extreme storms. Among different operational loss types, losses due to external fraud, BHCs’ failure to meet obligations to clients and faulty business practices, damage to physical assets, and business disruption drive this relation. Event study estimations corroborate our baseline findings. We further show that BHCs with past exposure to extreme storms reduce operational losses from future exposure to storms. Overall, our findings provide new ...
Working Paper
Benchmarking Operational Risk Stress Testing Models
The Federal Reserve?s Comprehensive Capital Analysis and Review (CCAR) requires large bank holding companies (BHCs) to project losses under stress scenarios. In this paper, we propose multiple benchmarks for operational loss projections and document the industry distribution relative to these benchmarks. The proposed benchmarks link BHCs? loss projections with both financial characteristics and metrics of historical loss experience. These benchmarks capture different measures of exposure and together provide a comprehensive view of the reasonability of model outcomes. Furthermore, we employ ...