Content

The concept of data representativeness, analysis of representativeness and mitigation of the effect of non-representativeness are emphasized in the Guidelines on PD & LGD estimation. While conceptually straightforward, determining, the required assessments, interpreting their results, mitigating the effects and assessing the validity of a treatment often pose a challenge to model developers and model validators.

Additional complexities arise in case of the estimation of LGD parameters, rating systems covering low-default portfolios, acquisitions or divestments, changes to definition of default

Learning Goals

• Understand and explain the conceptual basis for the guidelines regarding representativeness
• Challenge and verify the completeness, correctness and compliance of representativeness assessments and mitigation
• Set up a process and recognize key decisions for the identification of non-representativeness and quantification of its effects
• Understand trade-offs between different approaches in terms of complexity, explainability, and conservatism

Target Audience

• IRB model developers
• Model validators, Auditors and team leads with similar interests.
• Supervisors and policy advisors wishing to gain insight in the challenges of regulation with regard to representativeness.
• Model users, model risk management officers and Credit Risk Controllers wishing to enhance their understanding of representativeness

Prerequisites

The material will be taught in English. Participants are advised to come equipped with a basic understanding of

• the calculation of minimum capital requirements
• modelling the PD, LGD and EAD/CCF risk parameters.

Schedule

The module will be taught over4 sessions of each 4 hours. The track schedule will be planned in coordination with the client based on the selection of modules.

Example case studies

For this module, examples of case studies are

• Identify major potential sources of uncertainty, given documentation of a PD estimation methodology
• Compare the strengths and weaknesses of alternative MoC estimation approaches related to representativeness
• Explain the relation between model risk and MoC to front office risk management

Please Contact us for details and options.