Credit Risk Modelling for Basel II - (see Professional Certificate in Risk Management programme)

In this course, participants learn how to develop credit risk models in the context of the recent Basel II guidelines. The course provides a sound mix of both theoretical and technical insight, as well as practical implementation details. These are illustrated by several real-life case studies and exercises.

The particpants will learn how to:

• develop probability of default (PD), loss given default (LGD), and exposure at default (EAD) models,
• validate, backtest, and benchmark credit risk models,
• stress test credit risk models,
• develop credit risk models for low default portfolios.

2 days ( 9 a.m. to 5 p.m.)

Review of Basel I and Basel II

• application scoring, behavioral scoring, and profit scoring
• bankruptcy prediction models
• credit ratings
• the Basel I and Basel II regulation
• standard approach versus IRB approaches for credit risk
• PD versus LGD versus EAD
• expected loss versus unexpected loss

Sampling and Data Preprocessing

• selecting the sample
• types of variables
• missing values
• outlier detection and treatment
• exploratory data analysis
• categorization
• weight of evidence coding and information value
• segmentation
• reject inference (hard cut-off augmentation, parceling, etc.)

Developing PD Models for Basel II

• basic concepts of classification
• classification techniques: logistic regression, decision trees, linear programming, k-nearest neighbor, cumulative logistic regression
• input selection, such as filters, stepwise regression, and p-values
• setting the cut-off (strategy curve, marginal good-bad rates)
• measuring scorecard performance
• splitting up the data: single sample, holdout sample, cross-validation
• performance metrics, such as ROC curve, CAP curve, and KS-statistic
• defining ratings
• scorecard alignment and implementation

Developing LGD ans EAD Models for Basel II

• modeling loss given default (LGD)
• defining LGD, such as market approach and work-out approach
• time weighted versus default weigthed versus exposure weigthed LGD
• choosing the discount factor and the workout period
• dealing with incomplete workouts
• economis downturn LGD
• modeling LGD using segmentation
• modeling LGD using regression
• shaping the Beta distribution for LGD
• modeling LGD using two stage models
• modeling exposure at default (EAD): estimating credit conversion factors (CCF)
• cohort/fixed time horizon/momentum approach fo CCF
• risk drivers for CCF
• CAP curves for LGD ans CCF
• correlations between PD, LGD, and EAD
• calculating expected loss (EL)

Validation, Backtesting, and Stress Testing

• validating PD, LGD, and EAD models
• quantitative versus qualitative validation
• backtesting for PD, LGD, and EAD
• backtesting model stability (system stability index)
• backtesting model discrimination
• backtesting model calibration using the binomial, Vasicek, and chi-squared tests
• traffic light indicator approach
• backtesting action plans
• through-the-cycle (TTC) versus point-in-time (PIT) validation
• benchmarking
• internal versus external benchmarking
• Kendall's tau ans Kruskal's gamma for benchmarking
• use testing
• data quality
• documentation
• corporate governance and management oversight
• stress testing for PD, LGD, and EAD models
• static versus dynamic stress testing
• correlated trend analysis
• scorecard management
• low default portfolios (LDPs): implementation and validation
• likelihoud approaches to LDPs
• rating mapping approaches to LDPs