A statistical method to optimize the combination of internal and external data in operational risk measurement

According to the last proposals of the Basel Committee on Banking Supervision, banks are allowed to use the Advanced Measurement Approach (AMA) option for the computation of their capital charge covering operational risks. Among these methods, the Loss Distribution Approach (LDA) is the most sophisticated (see Frachot et al (2001) and Baud et al (2002)). It is widely recognized that calibration on internal data may not suffice for computing an accurate capital charge against operational risk. In other words, internal data should be supplemented with external data. The goal of this paper is to address issues regarding the optimal way to mix internal and external data with regards to frequency and severity. As a result rigorous statistical treatments are required to make internal and external data comparable and to ensure that merging both databases leads to unbiased estimates. We propose a rigorous way to tackle this issue through a statistically optimized methodology,.