In this course, participants 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.
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1.Learn how to | Download |
- 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.
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2.Who should attend |
- Anyone who is involved in building credit risk models, or is responsible for monitoring the behavior and performance of credit risk models
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Course Outline |
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1.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 IIIRegulatory versus Economic capital
- Basel I, Basel II, and Basel III regulationsC
- Standard approach versus IRB approaches for credit risk
- PD versus LGD versus EAD
- Expected loss versus unexpected loss
- The Merton/Vasicek model
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2.Sampling selecting the sampletypes 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.)
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3.Developing PD Models |
- Basic concepts of classification
- Classification techniques: logistic regression, decision trees, linear programming, k-nearest neighbor, cumulative logistic regressionC
- Input selection methods, such as filters, forward/backward/stepwise regression, and p-values
- Setting the cut-off (marginal good-bad rates)
- Measuring scorecard performance
- Data: single sample, holdout sample Splitting up the, 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
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4.Developing LGD and EAD Models |
- 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
- 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)
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5.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
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6.Low Default Portfolios (LDPs) |
- Definition of LDP
- Sampling approaches (under sampling versus oversampling)
- Likelihood approaches
- Calibration for LDPs
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7.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
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8.Neural Networks (included only in classroom version) |
- Background
- The multilayer perceptron (MLP)
- Transfer functions
- Data preprocessing
- Weight learning
- Overfitting
- Architecture selection
- Opening the black box
- Using MLPs in credit risk modeling
- Self Organizing Maps (SOMs)
- Using SOMs in credit risk modeling
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9.Survival Analysis (included only in classroom version) |
- Survival analysis for credit scoring
- Basic concepts
- Censoring
- Time-varying covariates
- Survival distributions
- Kaplan-Meier analysis
- Parametric survival analysis
- Proportional hazards regression
- Discrete survival analysis
- Evaluating survival analysis models
- Competing risks
- Mixture cure modeling
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