Operational risk management and measurement has been paid an increasing attention in last years. The main two reasons are the Basel II requirements that were to be complied with by all international active financial institutions by the end of 2006 and recent severe operational risk loss events. This paper focuses on operational risk measurement techniques and on economic capital estimation methods. A data sample of operational losses provided by an anonymous Central European bank is analyzed using several approaches. Multiple statistical concepts such as the Loss Distribution Approach or the Extreme Value Theory are considered. One of the methods used for operational risk management is a scenario analysis. Under this method, custom plausible loss events defined in a particular scenario are merged with the original data sample and their impact on capital estimates and on the financial institution as a whole is evaluated. Two main problems are assessed in this paper – what is the most appropriate statistical method to measure and model operational loss data distribution and what is the impact of hypothetical plausible events on the financial institution. The g&h distribution was evaluated to be the most suitable one for operational risk modeling because its results are consistent even while using a scenario analysis method. The method based on the combination of historical loss events modeling and scenario analysis provides reasonable capital estimates for the financial institution and allows to measure impact of very extreme events and also to mitigate operational risk exposure.
G21 - Banks; Other Depository Institutions; Mortgages ; G32 - Financing Policy; Capital and Ownership Structure ; C15 - Statistical Simulation Methods; Monte Carlo Methods
