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Operational risk has become one of the most popular topics in recent years among both academics and practitioners in the financial industry. The huge operational risk failures that materialized during the recent crisis, such as the $65 billion Ponzi scheme by Mr. Bernard Madoff and the $8 billion bank fraud of Sir Allen Stanford, highlight the need for improved risk management. This book focuses on operational risk management and economic capital estimation methods from both theoretical and practical views. A data sample of operational losses provided by a Central European bank is analyzed using several approaches such as loss distribution approach, extreme value theory, scenario analysis and stress testing. We examine the most appropriate statistical method to measure and model operational loss data distribution. In conclusion, we found out that i) the g&h distribution is the most suitable method for operational risk events modeling and ii) the method based on the combination of historical loss events modeling and scenario analysis provides reasonable capital estimates when measuring the impact of very extreme events on bank operations.

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