DEEP CREDIT RISK
  • WELCOME
  • CONTENTS
  • START
  • FEATURED
  • DATA & CODE
  • TRAINING
  • PAPERS
  • CONTACT
  • WELCOME
  • CONTENTS
  • START
  • FEATURED
  • DATA & CODE
  • TRAINING
  • PAPERS
  • CONTACT
Search

Zoom Training Details:

Day 1:
Duration
Topic
80 min
LGD engineering
  • Introduction into Python
  • Outlier detection
  • Standardisation & categorisation
  • The role of LGD and EAD in credit loss modeling
  • Computing observed LGD
  • LGD discount rates
  • Resolution bias
  • Discount rates
  • COVID-19 challenges
20 min
Break
80 min
Loss Given Default (LGD) Concepts
  • Statistical and machine learning thinking
  • Workflow in machine learning
  • Loss functions
  • Train-test validation split
  • Standardisation and scaling
  • Bias-variance trade-off
  • Cross-validation
  • Hyper-parameters and tuning
  • Supervised and unsupervised learning
  • Classification and regression vs. dimensionality reduction
  • Marginal modeling (linear, fractional logit & beta regressions)
  • PD-LGD models
Day 2:
Duration
Topic
80 min
Payoff and Exposure Concepts
  • Payoff and expected lifetime modeling
  • Conversion measures at default and undistressed period
  • Credit lines & flexible repayment schedules
  • Backtesting LGD and EAD
20 min
Break
80 min
Machine Learning Validation
  • Validation framework
  • Backtesting LGDs: discrimination, calibration, stability
  • Sccuracy, ROC curves, AUC & accuracy ratio
  • Brier scores
  • R^2
  • Calibration curve and reliability diagram
  • Portfolio dependence
Day 3:
Duration
Topic
80 min
Current expected credit losses (CECL) for US GAAP and IFRS 9
  • Loan loss provisioning and Basel capital
  • 12-month expected loss vs. lifetime expected loss
  • Rating class formation and roll rate analysis
  • Macroeconomic forecasts
  • Multi-period PD forecasts based on the macroeconomy and lifecycle
  • Prediction of lifetime expected losses
  • Significant increase in credit risk (SICR)
20 min
Break
80 min
Supervised Machine Learning for LGDs
  • Regression
  • K Nearest Neighbours
  • Decision Trees
  • Bagging and Boosting
Day 4:
Duration
Topic
80 min
Unexpected Credit Losses
  • Basel Calibrations
  • Asset Correlation modelling
  • Asymptotic Single Risk Factor Model
  • Simulations
  • Value-at-Risk and Expected Shortfall
20 min
Break
80 min
Supervised Machine Learning for LGDs
  • Neural networks
  • Deep learning
  • Support Vector Regressions

Back to Zoom Training
Copyright © 2021  |  Privacy Policy | Terms & Conditions
  • WELCOME
  • CONTENTS
  • START
  • FEATURED
  • DATA & CODE
  • TRAINING
  • PAPERS
  • CONTACT