Using External Data in Operational Risk
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Using External Data in Operational Risk a Montserrat Guillen , Jim Gustafssonb, Jens Perch Nielsenc and d Paul Pritchard a University of Barcelona, Econometrics, RFA-IREA, Diagonal 690, Barcelona E-08034, Spain. E-mail: [email protected] b Codan Insurance, Copenhagen, Denmark. E-mail: [email protected] c University of Copenhagen & Festina Lente, Copenhagen, Denmark. E-mail: [email protected] d Royal&SunAlliance, London, U.K. E-mail: [email protected]
We present a method to combine expert opinion on the likelihood of under-reporting with an operational risk data set. Under-reporting means that not all losses are identified and therefore an incorrect distributional assumption may be made, and ultimately an incorrect assessment made of capital required. Our approach can be applied to help insurers and other financial services companies make better assessments of capital requirements for operational risk using either external or internal sources. We conclude that operational risk capital evaluation can be significantly biased if under-reporting is ignored. The Geneva Papers (2007) 32, 178–189. doi:10.1057/palgrave.gpp.2510129 Keywords: operational risk; under-reporting function; loss data; capital requirements
Introduction Many financial services companies are now utilizing loss data for the purposes of calculating operational risk capital requirements, potentially arising from either regulatory requirements or indeed from a desire to integrate capital sensitive management within their organizations. Instinctively, the use of internal loss experience directly or as a means of deriving distribution parameters from which simulations can be made is most appealing. However, several factors mitigate against its effectiveness when considered alone: firstly, the data is backward looking based on historical events – the company profile may have changed, and should any large losses have occurred it is likely that controls will have been improved to prevent a reoccurrence. A greater problem nonetheless is that the regularly encountered losses may provide limited information on the size and frequency of large, rarely occurring losses that are the major factor in determining capital requirements. With this in mind organizations have recognized the value of obtaining loss data from outside their company, either through data sharing consortia or through publicly reported losses. In this paper, we focus on the use of publicly reported (also commonly called external) loss data to supplement internal loss experience. Utilizing publicly reported operational losses for modeling purposes involves a number of considerations. The losses chosen for modeling should be representative of the organization as far as is practicable. This should facilitate the use of both
Montserrat Guillen et al. Using External Data in Operational Risk
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internal and external data in the modeling since they could then show similar behavior in terms of size distribution (i.e., they come from the same distribution). This might involve, for ex
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