The Cascade Bayesian Approach: Prior Transformation for a Controlled Integration of Internal Data, External Data and Scenarios

Bertrand K. Hassani and Alexis Renaudin
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Bertrand K. Hassani: University College London Computer Science, 66-72 Gower Street, London WC1E 6EA, UK
Alexis Renaudin: Aon Benfield, The Aon Centre, 122 Leadenhall Street, London EC3V 4AN, UK

Risks, 2018, vol. 6, issue 2, 1-17

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.

Keywords: operational risk; loss distribution approach; Bayesian inference; Markov chain Monte Carlo; extreme value theory; non-parametric statistics; risk measures (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2018
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