Abstract
This paper focuses on modeling the real operational data of an anonymous Central European Bank. We have utilized two main approaches described in the literature: the Loss Distribution Approach and Extreme Value Theory, in which we have used two estimation methods - the standard maximum likelihood estimation method and the probability weighted moments (PWM).
Our results proved a heavy-tailed pattern of operational risk data as documented by many researchers. Additionally, our research showed that the PWM is quite consistent when the data is limited as it was able to provide reasonable and consistent capital estimates.
Our result show that when using the Advanced Measurement Approach (AMA) rather than the Basic Indicator Approach (BIA) used in Basel II, the researched bank might save approx. 6-7% of its capital requirement on operational risk. From a policy perspective it should be hence noted that banks from emerging markets such as the Central Europe are also able to register operational risk events and the distribution of these risk events can be estimated with a similar success than those from more mature markets.