Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/195839 
Year of Publication: 
2018
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 6 [Issue:] 2 [Publisher:] MDPI [Place:] Basel [Year:] 2018 [Pages:] 1-17
Publisher: 
MDPI, Basel
Abstract: 
According to the last proposals of the Basel Committee on Banking Supervision, banks or insurance companies under the advanced measurement approach (AMA) must use four different sources of information to assess their operational risk capital requirement. The fourth includes 'business environment and internal control factors', i.e., qualitative criteria, whereas the three main quantitative sources available to banks for building the loss distribution are internal loss data, external loss data and scenario analysis. This paper proposes an innovative methodology to bring together these three different sources in the loss distribution approach (LDA) framework through a Bayesian strategy. The integration of the different elements is performed in two different steps to ensure an internal data-driven model is obtained. In the first step, scenarios are used to inform the prior distributions and external data inform the likelihood component of the posterior function. In the second step, the initial posterior function is used as the prior distribution and the internal loss data inform the likelihood component of the second posterior function. This latter posterior function enables the estimation of the parameters of the severity distribution that are selected to represent the operational risk event types.
Subjects: 
operational risk
loss distribution approach
Bayesian inference
Markov chain Monte Carlo
extreme value theory
non-parametric statistics
risk measures
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
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