## Credit Risk Modeling Using SAS
## This course has been replaced.Please see the schedule for the new Credit Risk Modeling Using SAS course.In this course, students learn how to develop credit risk models in the context of the recent Basel II and Basel III guidelines. The course provides a sound mix of both theoretical and technical insights, as well as practical implementation details. These are illustrated by several real-life case studies and exercises.
- 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.
Before attending this course, you should have business expertise in credit risk and a basic understanding of statistical classification methods. Previous SAS software and SAS Enterprise Miner experience is helpful but not necessary. 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 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 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 methods, such as filters, forward/backward/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
- migration matrices
- rating philosophy (Point-in-Time versus Through-the-Cycle)
- mobility metrics
- PD calibration
- scorecard alignment and implementation
Developing LGD and EAD Models for Basel II - modeling loss given default (LGD)
- defining LGD using market approach and work-out 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
- time weighted versus default weighted versus exposure 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
- nodeling 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
Low Default Portfolios (LDPs) - definition of low default porfolios
- undersampling versus oversampling
- likelihood approaches to LDPs
- rating mapping approaches to LDPs
Stress Testing for PD, LGD, and EAD Models - overview of stress testing regulation
- sensitivity analysis
- scenario analysis (historical versus hypothetical)
- examples from industry
- Pillar 1 versus Pillar 2 stress testing
- macro-economic stress testing
New Techniques to Develop PD, LGD, and EAD Models - review of traditional techniques for scorecard development
- neural networks: the neuron model, multilayer perceptrons (MLPs), training an MLP
- opening up the neural network black box
- two-stage models
- 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 Credit Risk Modeling - example applications (predicting time to default, time to early repayment, etc.)
- the censoring problem
- survival curves versus hazard curves
- Kaplan Meier analysis
- parametric survival analysis
- proportional hazards regression
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