Using External Data in Operational Risk

2007 ◽  
Author(s):  
Jim Gustafsson ◽  
Montserrat Guillén ◽  
Jens Perch Nielsen ◽  
Paul Pritchard
Keyword(s):  
Operational Risk ◽  
External Data

The quantification of operational risk using internal data, relevant external data and expert opinion

2007 ◽  
Vol 2 (3) ◽  
pp. 3-27 ◽  
Author(s):  
Dominik Lambrigger ◽  
Pavel Shevchenko ◽  
Mario Wüthrich
Keyword(s):  
Expert Opinion ◽  
Operational Risk ◽  
External Data

Combining underreported internal and external data for operational risk measurement

2008 ◽  
Vol 3 (4) ◽  
pp. 3-24 ◽  
Author(s):  
Montserrat Guillen ◽  
Jim Gustafsson ◽  
Jens Perch Nielsen
Keyword(s):  
Operational Risk ◽  
Risk Measurement ◽  
External Data

Operational risk capital charges (Basel II): factoring in external loss data to the internal datasets

2015 ◽  
Vol 16 (5) ◽  
pp. 519-535
Author(s):  
Lukasz Prorokowski
Keyword(s):  
Financial Services ◽  
Operational Risk ◽  
Basel Ii ◽  
Data Sets ◽  
Risk Capital ◽  
Content Type ◽  
External Data ◽  
Internal Loss ◽  
Loss Data ◽  
Financial Services Firms

Purpose – This paper aims to discuss ideas of factoring in external loss data to the internal loss data sets to obtain a true picture of operational losses for non-bank financial services firms, focusing on a case study of the interdealer brokers business and a specific Basel II category of the operational risk capital charges. As it transpires, financial services firms are increasingly required by regulators to merge external loss data with their internal data sets when using a loss distribution approach. However, there is a significant constrain on the availability and completeness of the external data for non-bank financial services firms. Design/methodology/approach – Embarking on a modified Kaplan-Meier method is a clever way of factoring in external loss data into the internal data set. It allows non-bank financial firms to choose which fragments of the data constitute “the best fit”. In choosing the external data, this paper posits that such firms need to rely on loss-type events that display similar patterns in probabilities of occurrence. This method eliminates over-reliance on the external data that are specific for a different entity. One of the most important assumption underpinning the method presented in this paper is the fact that constant time intervals between the recorded operational loss events are assumed. Hereto, reaching a certain level of loss is used as the event of interest in both groups. For simplification purposes and to eliminate the noise and capture significant losses, we set this level as a multiplicity of the interdealer broker’s loss threshold. Findings – Obtaining external loss data is difficult for the non-bank financial services firms. Furthermore, institutions operating as interdealer brokers are exposed to different levels of operational risk that affect their own Advanced Measurement Approach to capital charges under Basel II. The existing consortium data sets are not suitable for non-bank financial institutions. With this in mind, the non-bank firms should select only the parts of the external data that fit their business environment. Originality/value – This paper should be of interest to any financial services firms that is required by regulators to merge its internal loss data sets with external loss data. Furthermore, this paper makes strong recommendations for regulators who should understand that the contemporary operational risk consortium data sets are not suitable for non-bank financial services firms.

scholarly journals Using External Data in Operational Risk

2007 ◽  
Vol 32 (2) ◽  
pp. 178-189 ◽  
Author(s):  
Montserrat Guillen ◽  
Jim Gustafsson ◽  
Jens Perch Nielsen ◽  
Paul Pritchard
Keyword(s):  
Operational Risk ◽  
External Data
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