Operational Risk Capital Models (2nd edition)
First published:
ISBN: 9781782724223
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Part II: General Framework for Operational Risk Capital Modelling
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
Operational risk modelling requires a robust determination of its inputs, as presented in Part I. This includes the definition of criteria for the collection of, and quality control for, internal losses, sourcing of external data, a strong process for the capture of scenario analysis and the incorporation of business environment and internal control factors (BEICFs) into the capital model.
Once quality inputs are available, they can be used in the appropriate modelling processes to obtain adequate capital estimations. Part II describes the modelling processes that permit a quality capital estimation and describes the circumstances in which these processes should be applied.
Most institutions calculate their operational risk capital requirements based on either a loss distribution approach (LDA) – mainly based on loss data – or a scenario-based approach (SBA). A more advanced approach is the hybrid model (HM), where loss data and scenario analysis are combined into a single model to compute capital using, in effect the four data elements requested in the Basel Committee on Banking Supervision “Operational Risk – Supervisory Guidelines for Advanced Measurement Approaches”
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