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The Structural Modelling of Operational Risk via Bayesian Inference: Combining Loss Data with Expert
Year Of Publication: 2006
Month Of Publication: August
Pages: 26
Download Count: 404
View Count:
Comment Num: 0
Language: EN
Who Can Read: Free
Date: 5-28-2007
Publisher: Administrator
To meet the Basel II regulatory requirements for the Advanced Measurement Approaches,the bank’s internal model must include the use of internal data, relevant external data,scenario analysis and factors reflecting the business environment and internal controlsystems. Quantification of operational risk cannot be based only on historical data butshould involve scenario analysis. Historical internal operational risk loss data havelimited ability to predict future behaviour moreover, banks do not have enough internaldata to estimate low frequency high impact events adequately. Historical external data aredifficult to use due to different volumes and other factors. In addition, internal andexternal data have a survival bias, since typically one does not have data of all collapsedcompanies. The idea of scenario analysis is to estimate frequency and severity of riskevents via expert opinions taking into account bank environment factors with reference toevents that have occurred (or may have occurred) in other banks. Scenario analysis isforward looking and can reflect changes in the banking environment. It is important tonot only quantify the operational risk capital but also provide incentives to business unitsto improve their risk management policies, which can be accomplished through scenarioanalysis. By itself, scenario analysis is very subjective but combined with loss data it is apowerful tool to estimate operational risk losses. Bayesian inference is a statisticaltechnique well suited for combining expert opinions and historical data. In this paper, wepresent examples of the Bayesian inference methods for operational risk quantification
Shevchenko, Pavel Sign in to follow this author
Wuethrich, Mario V. Sign in to follow this author
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