Credit Risk Modeling for Basel II Using SAS

Duration: 4.0 days Course fee: $3,200 EPTO units: 6.5 CEUs: 2.4

Presented by Bart Baesens, Ph.D. or Christophe Mues, Ph.D., Assistant Professors at the School of Management of the University of Southampton (UK)

In this course, students 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.

Learn how to

develop PD, LGD, and EAD models for Basel II
validate and stress-test Basel II models
ensure your organization is in compliance.

Who should attend

Anyone involved in building scoring/predictive credit risk systems, or responsible for validating and monitoring their behavior and performance

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Prerequisites

Before attending this course, you should

have business expertise in credit scoring and an understanding of statistical classification methods. Previous SAS software and SAS Enterprise Miner experience is helpful but not necessary.
contact instructor Bart Baesens directly if you have questions: Bart.Baesens@econ.kuleuven.be.

Course Contents

Review of Basel I and Basel II

the Basel I and Basel II regulation
standard approach versus IRB approach for credit risk
PD versus LGD versus EAD
the Merton/Vasicek model for calculating the regulatory capital
application scoring, behavioral scoring, and profit scoring
bankruptcy prediction models

Sampling and Data Preprocessing

selecting the sample
segmentation
reject inference
exploratory data analysis
outlier detection and treatment
missing values
weight of evidence coding and information value

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
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 implementation

Developing LGD and EAD Models for Basel II

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

Validation, Backtesting, and Stress Testing

validating PD, LGD, and EAD models
quality control
quantitative versus qualitative validation
use testing
through-the-cycle (TTC) versus point-in-time (PIT) validation
backtesting for PD, LGD, and EAD
backtesting statistics, such as binomial, Vasicek, and chi-squared
traffic light indicator approach
backtesting action plans
stress testing for PD, LGD, and EAD models
static versus dynamic stress testing
correlated trend analysis
monitoring PD, LGD, and EAD models
segmenting PD, LGD, and EAD models
benchmarking
internal versus external benchmarking
Kendall's tau and Kruskal's gamma for benchmarking
scorecard management
low default portfolios (LDPs): implementation and validation
value-at-risk (VaR) models
the Merton/Vasicek model for calculating the regulatory capital

New Techniques to Develop PD, LGD, and EAD Models for Basel II

review of traditional techniques for scorecard development
neural networks: the neuron model, multilayer perceptrons (MLPs), training an MLP
support vector machines: the SVM classification model and building scorecards using SVMs (short)
case study: using logistic regression and support vector machines to develop a country rating system

Survival Analysis for Profit Scoring

survival analysis for developing customer lifetime models
the censoring problem
survival curves versus hazard curves
Kaplan Meier analysis
parametric survival analysis
proportional hazards regression
using survival analysis for LGD modeling and profit scoring

Software

This course addresses SAS Enterprise Miner, SAS for Enterprise Risk Management.

Course Materials

Students receive a hardcopy of the course notes and, in some courses, can choose to take home a copy of the course data.

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Are there additional topics you'd like for this course to address? Would you like for this course to be offered at another training facility? Let us know by adding to our Interest List.

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