Operational Risk Capital Models (2nd edition)
First published:
ISBN: 9781782724223
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Exposure-based approaches
Rafael Cavestany and Emilio Lopez Cano
Exposure-based approaches
Introduction
Challenges of operational risk advanced capital models
Part I: Capture and Determination of the Four Data Elements
Collection of operational loss data: ILD and ED
Scenario analysis framework and BEICFs integration
Part II: General Framework for Operational Risk Capital Modelling
Loss data modelling: ILD and ED
Distributions for modelling operational risk capital
Scenario analysis modelling
Exposure-based approaches
BEICFs modelling and integration into the capital model
Hybrid model construction: Integration of ILD, ED and SA
Derivation of the joint distribution and capitalisation of operational risk
Backtesting, stress testing and sensitivity analysis
Regulatory approval report
Evolving from a plain vanilla to a state-of-the-art model
Part III: Use Test, Integrating Capital Results into the Institution’s Day-to-day Risk Management
Strategic and operational business planning and monitoring
Risk/reward evaluation of mitigation and control effectiveness
Appendix 1: Credibility theory
Appendix 2: Mathematical optimisation methods required for operational risk modelling and other risk mitigation processes
Business risk quantification
Previous chapters have introduced methodologies to build operational risk capital (ORC) models based on loss data and scenario analysis modelling. This chapter will presents exposure-based approaches that intend to address perceived weaknesses of loss and scenario modelling-based approaches.
Loss modelling is backward-looking by nature as it is based on historical data, and relies on the assumption that past loss experience has enough information to predict future losses. Moreover, loss data is dominated by high-frequency events, while the risk profile of financial institutions is most commonly dominated by low-frequency, high-severity events. Loss modelling addresses this issue by projecting the loss distribution tail by fitting a distribution function to the observed loss data. The extrapolation relies on the assumption that major losses can be derived from low losses. Although this can be true for many ORCs, it might not provide you with the complete set of possible operational risk events, and loss modelling requires being complemented with other information sources – such as scenario analysis and external loss data.
The main purpose of scenario analysis is to produce
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