TFarenhorst
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4 years ago update: @bergberg , ik heb een opzetje, kan je review en update naar website?
Content: In 2004 the Basel Committee on Banking Supervision (BCBS) finalised a new framework for capital adequacy regulation based on three pillars. This framework was further revised in 2013 as is now known as “Basel III”, and formed the basis for the capital adequacy legislation of the European Union, called “The Capital Requirement Directive IV” or “CRD IV” which was implemented in January 2014 . This Directive is mandatory for all European banks and investment firms. Banks are required to hold sufficient capital to protect them against Unexpected Loss (UL). In the Basel III framework the amount of capital that a specific bank should hold is calculated on the basis of risk weighted assets formulas. Derivation of these risk-weighted assets is dependent on the estimates of the following risk components: Probability of Default (PD), Loss Given Default (LGD) and Exposure at Default (EAD) and - in some cases - Effective Maturity (EM).
This course covers diverse topics in the whole model development process: from clarifying the model requirements, to submitting to model validation/regulators; from the basic to some more advanced ways of modeling and testing an AIRB rating system: Probability of Default (PD), Loss Given Default (LGD ) and Exposure At Default (EAD) models.
Course Outline
- Introduction to credit risk management and regulatory environment
- High-level Model development process
- Data preparation and DQ assessment
- Risk Differentiation
- Risk parameter calibration
- Margin of Conservatism (MoC)
- Probability of Default (PD)
- Loss Given Default (LGD)
- Exposure at Default (CCF)
- Impact assessment
Learning objectives After this course, you will be able to
- Know how to read the relevant regulation for credit risk models
- Develop probability of default (PD), loss given default (LGD), and exposure at default (EAD) models
- Appropriate quantify the margin of conservatism
- Effectively interact with model validator, internal audit and regulators
- build up the good quality audit trail during the modelling process
- Validate, backtest, and benchmark credit risk models
Target Audience The course is intended for
- IRB model developers and team leads
- IRB model validators
- Audits
- Specialist supervisors
- Credit risk managers
- Data scientist who wants to know how to use data for advanced credit risk modelling
Prerequisites Participants are advised to come equipped with basic understanding of:
- Knowledge of basic risk management.
- Statistics and probability at university level
- Programming experience with python (or similar language)
bergberg
Content and Learning Goals are empty
Target Audience: maybe add more, e.g.：
Date and duration: maybe it is good to say that it will consists of 8 modules (half day per module) and it will last one months. For the exact training starts date please contact us.