The Structural Modelling of Operational Risk Via Bayesian Inference: Combining Loss Data with Expert Opinions

The Journal of Operational Risk 1(3), pp. 3-26, 2006

26 Pages Posted: 24 Nov 2014

See all articles by Pavel V. Shevchenko

Pavel V. Shevchenko

Macquarie University; Macquarie University, Macquarie Business School

Mario V. Wuthrich

RiskLab, ETH Zurich

Date Written: August 18, 2006

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.

Keywords: operational risk, loss distribution approach, Bayesian inference, Basel II Advanced Measurement Approaches, compound process, quantitative risk management

JEL Classification: C11, C13, G00

Suggested Citation

Shevchenko, Pavel V. and Wuthrich, Mario V., The Structural Modelling of Operational Risk Via Bayesian Inference: Combining Loss Data with Expert Opinions (August 18, 2006). The Journal of Operational Risk 1(3), pp. 3-26, 2006. Available at SSRN: https://ssrn.com/abstract=2529529

Macquarie University, Macquarie Business School ( email )

New South Wales 2109
Australia

Mario V. Wuthrich

RiskLab, ETH Zurich ( email )

Department of Mathematics
Ramistrasse 101
Zurich, 8092
Switzerland

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