Course Outlines

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LGD and EAD prediction using SAS

This two day course offers a summary of today’s most commonly used average and cohort-based LGD calculation methodologies, together with detailed instructions on how to move toward more flexible statistical prediction models, including regression trees, beta regression, two-stage scenario prediction models, generalized additive neural networks and LGD scorecards.

The course also includes the most important definitions for setting up EAD modelling for facilities with explicit limits.

The course includes extensive and fully-scripted exercises that let the student create modelling process flows step by step and discover the rich possibilities and benefits of SAS Enterprise Miner.

All custom SAS code used in the process is documented, including dedicated LGD validation and bootstrapping code. Exercises are done on real mortgage LGD data.

Dr Hendrik Wagner

For 9 years Dr Wagner was Product Manager Data Mining Solutions at the SAS Institute covering Europe, Middle East and Africa.

He introduced scorecard development functionality into SAS' flagship data mining solution Enterprise Miner and made it the market leading solution for inhouse scorecard development. He also led the creation of an end-to-end model development, deployment and monitoring solution and defined specific functionality for building internal rating systems for Basel 2 -PD and LGD modeling, pooling and backtesting.

After writing SAS' first Risk Weighted Assets calculation code, he helped launch SAS' market leading Credit Risk Management solution.

He became a consultant in 2006 providing credit risk and internal audit departments with advisory and implementation services, such as readiness assessment, model development and rating system auditing.

Clients include inter alia GHB bank, Thailand, (Housing Loan Application Scorecard), Samlink, Finland, (Behavioral PD Model),Maybank Malaysia( Corporate PD Model Validation), National Australia Group UK, (Retail PD, LGD and EAD Model Validation for Basel2 IRB Approval), Deutsche Telekom Germany (PD model validation and development, early warning system, profit scoring) and BHW Bausparkasse ( PD and LGD model validation of a home loans portfolio).

Dr Wagner holds a doctorate in Computer Sciences.

Duration: 2 days

The course covers the following topics in detail:

Part1: LGD Basics

• Why LGD
• Individual Workout LGD Calculation
• LGD Averaging
• LGD Cohorts
• Structural LGD Models

Part2: Account Level Models

• Development Sample Definition
• Reference Dates
• Input Variables
• Data Pre-Processing and Exploration
• Data Partitioning
• Treatment of Missing Values and Outliers
• Multi-Plot
• Segmentation
• Table of Averages
• Regression Trees
• Linear Regression
• Normal-Theory Linear Regression
• Beta Regression
• Input Standardization and Normalization
• Two-stage Modelling
• Non-Linear Regression
• Grouped Variable Regression
• Generalized Additive Neural Networks
• LGD Scorecards
• Validation
• Residual Analysis
• Correlation and Error Measures
• Stability Analysis
• Pseudo-Power Measures
• Bootstrapping
• LGD Pooling
• LEQ and CCF Modelling (EAD)