The Quantification of Operational Risk Using Internal Data, Relevant External Data and Expert Opinion

The Journal of Operational Risk 2(3), pp.3-27, 2007.

30 Pages Posted: 24 Nov 2014

See all articles by Dominik Lambrigger

Dominik Lambrigger

ETH Zurich

Pavel V. Shevchenko

Macquarie University; Macquarie University, Macquarie Business School

Mario V. Wuthrich

RiskLab, ETH Zurich

Date Written: July 4, 2007

Abstract

To quantify an operational risk capital charge under Basel II, many banks adopt a Loss Distribution Approach. Under this approach, quantification of the frequency and severity distributions of operational risk involves the bank's internal data, expert opinions and relevant external data. In this paper we suggest a new approach, based on a Bayesian inference method, that allows for a combination of these three sources of information to estimate the parameters of the risk frequency and severity distributions.

Keywords: Operational Risk, Basel II, Loss Distribution Approach, Bayesian inference, Advanced Measurement Approach, Quantitative Risk Management, generalized inverse Gaussian distribution

JEL Classification: C00, G00

Suggested Citation

Lambrigger, Dominik and Shevchenko, Pavel V. and Wuthrich, Mario V., The Quantification of Operational Risk Using Internal Data, Relevant External Data and Expert Opinion (July 4, 2007). The Journal of Operational Risk 2(3), pp.3-27, 2007.. Available at SSRN: https://ssrn.com/abstract=2529531

Dominik Lambrigger

ETH Zurich ( email )

Rämistrasse 101
ZUE F7
Zürich, 8092
Switzerland

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

Register to save articles to
your library

Register

Paper statistics

Downloads
227
Abstract Views
740
rank
132,885
PlumX Metrics