Credit Risk Modeling (E-learning)

📅 Self-Paced E-learning course
🌍 English

Overview

Business Knowledge Series course

Presented by Bart Baesens, Ph.D. Professor at the School of Management of the University of Southampton (UK); or Christophe Mues, Ph.D., Professor at the School of Management of the University of Southampton (UK); or Cristian Bravo, Ph.D, Assistant Professor, Business Analytics, University of Southampton (UK); or Wouter Verbeke, Ph.D., Assistant Professor, Business Informatics, University of Brussels (Belgium); or Stefan Lessmann, Ph.D., Professor, School of Business and Economics, Humboldt University (Germany)

The E-learning course covers both the basic as well some more advanced ways of modeling, validating and stress testing Probability of Default (PD), Loss Given Default (LGD ) and Exposure At Default (EAD) models. Throughout the course, we extensively refer to our industry and research experience. Various business examples and small case studies in both retail and corporate credit are also included for further clarification. The E-learning course consists of more than 20 hours of movies, each 5 minutes on average. Quizzes are included to facilitate the understanding of the material. Upon registration, you will get an access code which gives you unlimited access to all course material (movies, quizzes, scripts, ...) during 6 months. The course focusses on the concepts and modeling methodologies and not on the SAS software. To access the course material, you only need a laptop, iPad, iPhone with a web browser. No SAS software is needed.

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
use new and advanced techniques for improved credit risk modeling.

Course Outline

Introduction to Credit Scoring

application scoring, behavioral scoring, and dynamic scoring
credit bureaus
bankruptcy prediction models
expert models
credit ratings and rating agencies

Review of Basel I, Basel II, and Basel III

Regulatory versus Economic capital
Basel I, Basel II, and Basel III regulations
standard approach versus IRB approaches for credit risk
PD versus LGD versus EAD
expected loss versus unexpected loss
the Merton/Vasicek model

Sampling and Data Preprocessing

selecting the sample
types of variables
missing values (imputation schemes)
outlier detection and treatment (box plots, z-scores, truncation, etc.)
exploratory data analysis
categorization (chi-squared analysis, odds plots, etc.)
weight of evidence (WOE) coding and information value (IV)
segmentation
reject inference (hard cutoff augmentation, parceling, etc.)

Developing PD Models

basic concepts of classification
classification techniques: logistic regression, decision trees, linear programming, k-nearest neighbor, cumulative logistic regression
input selection methods such as filters, forward/backward/stepwise regression, and p-values
setting the cutoff (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
migration matrices
rating philosophy (Point-in-Time versus Through-the-Cycle)
mobility metrics
PD calibration
scorecard alignment and implementation

Developing LGD and EAD Models

modeling loss given default (LGD)
defining LGD using market approach and workout approach
choosing the workout period
dealing with incomplete workouts
setting the discount factor
calculating indirect costs
drivers of LGD
modeling LGD
modeling LGD using segmentation (expert based versus regression trees)
modeling LGD using linear regression
shaping the Beta distribution for LGD
modeling LGD using two-stage models
measuring performance of LGD models
defining LGD ratings
calibrating LGD
default weighted versus exposure weighted versus time weighted LGD
economic downturn LGD
modeling exposure at default (EAD): estimating credit conversion factors (CCF)
defining CCF
cohort/fixed time horizon/momentum approach for CCF
risk drivers for CCF
modeling CCF using segmentation and regression approaches
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
quantitative versus qualitative validation
backtesting for PD, LGD, and EAD
backtesting model stability (system stability index)
backtesting model discrimination (ROC, CAP, overrides, etc,)
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 and Kruskal's gamma for benchmarking
use testing
data quality
documentation
corporate governance and management oversight