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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 several approaches to assess the conservatism of BHCs? stress loss projections and our estimates for the conservatism of loss projections for the median bank range from the 90th percentile to above the 99th percentile of the operational loss distribution.
AUTHORS: Curti, Filippo; Migueis, Marco; Stewart, Rob T.
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 statements, even post crisis. So, operational risk practitioners, bank analysts, and regulators must develop reasonable methods to assess the efficacy of operational risk models and associated equity financing. We believe benchmarks should be used extensively to justify model outputs, improve model stability, and maintain capital reasonableness. Since any individual benchmark can be misleading, we outline a set of principles for using benchmarks effectively and describe how these principles can be applied to operational risk models. Also, we provide some examples of the benchmarks that have been used by US regulators in assessing Advanced Measurement Approach (AMA) capital reasonableness and that can be used in CCAR to assess the reasonableness of operational risk loss projections. We believe no single model's output and no single benchmark offers a comprehensive view, but that practitioners, analysts, and regulators must use models combined with rigorous benchmarks to determine operational risk capital and assess its adequacy.
AUTHORS: Stewart, Rob T.; Le, Minh; Curti, Filippo; Migueis, Marco; Ergen, Ibrahim
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.
AUTHORS: Curti, Filippo; Migueis, Marco
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.
AUTHORS: Mihov, Atanas; Curti, Filippo; Afonso, Gara