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The Cascade Bayesian Approach for a controlled integration of internal data, external data and scenarios

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Author Info

  • Bertrand Hassani

    ()
    (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Paris I - Panthéon-Sorbonne, Santander UK - Santander UK)

  • Alexis Renaudin

    ()
    (Aon GRC - Aon Global Risk Consulting -)

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    Abstract

    According to the last proposals of the Basel Committee on Banking Supervision, banks under the Advanced Measurement Approach (AMA) must use four different sources of information to assess their Operational Risk capital requirement. The fourth including "business environment and internal control factors", i.e. qualitative criteria, the three main quantitative sources available to banks to build 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 a first step, scenarios are used to inform the prior distributions and external data informs 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. This latter posterior function enables the estimation of the parameters of the severity distribution selected to represent the Operational Risk event types.

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    Bibliographic Info

    Paper provided by HAL in its series Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) with number halshs-00795046.

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    Date of creation: Feb 2013
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    Handle: RePEc:hal:cesptp:halshs-00795046

    Note: View the original document on HAL open archive server: http://halshs.archives-ouvertes.fr/halshs-00795046
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    Related research

    Keywords: Operational Risk; Loss Distribution Approach; Bayesian inference; Marchov Chain Monte Carlo; Extreme Value Theory; non-parametric statistics; risk measures;

    This paper has been announced in the following NEP Reports:

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