Quantitative modeling of operational risk losses when combining internal and external data

Jens Perch Nielsen (), Montserrat Guillen, Catalina Bolance () and Jim Gustafsson
Additional contact information
Jens Perch Nielsen: City University, http://www.city.ac.uk
Catalina Bolance: University of Barcelona, http://www.ub.edu
Jim Gustafsson: Ernst & Young, http://ey.com

Journal of Financial Transformation, 2012, vol. 35, 179-185

Abstract: We present an overview of methods to estimate risk arising from operational losses. Our approach is based on the study of the statistical severity distribution of a single loss. We analyze the fundamental issues that arise in practice when modeling operational risk data. We address the statistical problem of estimating an operational risk distribution, both in standard abundant data situations and when our available data is challenged from the inclusion of external data or because of underreporting. Our presentation includes an application to show that failure to account for underreporting may lead to a substantial underestimation of operational risk measures. The use of external data information can easily be incorporated in our modeling approach.

Keywords: operational risk; quantitative modeling; operational loss; risk modeling (search for similar items in EconPapers)
JEL-codes: G21 G28 G32 (search for similar items in EconPapers)
Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:ris:jofitr:1538

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