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The Structural Modelling of Operational Risk via Bayesian inference: Combining Loss Data with Expert Opinions

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  • P. V. Shevchenko
  • M. V. Wuthrich

Abstract

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 control systems. Quantification of operational risk cannot be based only on historical data but should involve scenario analysis. Historical internal operational risk loss data have limited ability to predict future behaviour moreover, banks do not have enough internal data to estimate low frequency high impact events adequately. Historical external data are difficult to use due to different volumes and other factors. In addition, internal and external data have a survival bias, since typically one does not have data of all collapsed companies. The idea of scenario analysis is to estimate frequency and severity of risk events via expert opinions taking into account bank environment factors with reference to events that have occurred (or may have occurred) in other banks. Scenario analysis is forward looking and can reflect changes in the banking environment. It is important to not only quantify the operational risk capital but also provide incentives to business units to improve their risk management policies, which can be accomplished through scenario analysis. By itself, scenario analysis is very subjective but combined with loss data it is a powerful tool to estimate operational risk losses. Bayesian inference is a statistical technique well suited for combining expert opinions and historical data. In this paper, we present examples of the Bayesian inference methods for operational risk quantification.

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  • P. V. Shevchenko & M. V. Wuthrich, 2009. "The Structural Modelling of Operational Risk via Bayesian inference: Combining Loss Data with Expert Opinions," Papers 0904.1067, arXiv.org.
    • Handle: RePEc:arx:papers:0904.1067
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    References listed on IDEAS

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    1. Lindskog, Filip & McNeil, Alexander J., 2003. "Common Poisson Shock Models: Applications to Insurance and Credit Risk Modelling," ASTIN Bulletin, Cambridge University Press, vol. 33(2), pages 209-238, November.
    2. Chavez-Demoulin, V. & Embrechts, P. & Neslehova, J., 2006. "Quantitative models for operational risk: Extremes, dependence and aggregation," Journal of Banking & Finance, Elsevier, vol. 30(10), pages 2635-2658, October.
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    Cited by:

    1. Guicang Peng & Jon Tømmerås Selvik & Eirik Bjorheim Abrahamsen & Knut Erik Bang & Tore Markeset, 2024. "Integrating structure time series forecasting and multicriteria decision analysis for adaptive operational risk assessment: an empirical study using real-time data," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(7), pages 3162-3181, July.

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