Module 1: Stress Testing in Basel III Module 2: Stress Testing Methodology Module 3: Unsupervised Models Exercise 1: Segmentation of the data with K-Means in R Module 4: Support Vector Machine (SVM) Exercise 2: Modeling PD using Support Vector Machine Module 5: Ensemble Learning Exercise 3: Regression Random Forest in R and Python Exercise 4: Classification Gradient Boosting Trees Module 6: Deep Learning Feed Forward Neural Networks Exercise 5: Credit Scoring using Deep Learning Feed Forward Module 7: Deep Learning Convolutional Neural Networks (CNN) Exercise 6: Credit Scoring using Deep Learning CNN Module 8: Deep Learning Recurrent Neural Networks (RNN) Exercise 7: Forecasting macroeconomic time series using Deep Learning LSTM Module 9: Calibrating Machine Learning and Deep Learning Exercise 8: Optimization of Credit Scoring with XGBoosting, Random Forest and SVM Exercise 9: Optimized Credit Scoring with Deep Learning and Model Interpretation Module 10: Quantum Computing and Algorithms Exercise 10: Quantum Operations Module 11: Credit Scoring for PD Estimation Exercise 11: Building Scorecard in Excel, R and Python Module 12: Probability of Default (PD) Exercise 12: PD Calibration Models Exercise 14: PD Calibration in Machine Learning Models Exercise 15: Modeling the Margin of Caution (PD) Module 14: Econometric Models of PD Exercise 16: Calculating PD with Cox Regression in R Exercise 17: PD Calibration with Logistic Model in Python Module 15: Bayesian Models for PD Stress Testing Exercise 18: Logistic Model Bayesian PD in Python Module 16: IFRS 9 PD Forecasting Exercise 19: Forecasting PD using VARMAX in R Module 17: Lifetime PD Models Exercise 20: PD Lifetime using Vintage EMV Decomposition Model Exercise 21: PD Lifetime using Multinomial Regression in R Exercise 23: PD Lifetime using Markov Model Exercise 24: PD Lifetime using SVM in Python Exercise 25: PD Lifetime using Neural Network in Python Module 18: Lifetime PD (Quantum Lifetime PD) Exercise 26: Lifetime PD IFRS 9 Estimation using Quantum Enhancements Module 19: LGD in Retail Portfolios and IRB Companies Exercise 27: LGD Formulas and LGD Downturn Models Module 20: Econometric and Machine Learning Models of the LGD Exercise 28: LGD Econometric Models Exercise 29: Machine Learning and Deep Learning Models of LGD Module 21: LGD for IFRS 9 Exercise 30: Estimation and Adjustments for LGD IFRS 9 using Tobit Regression in R Exercise 31: Censored Regression Model for LGD in R Exercise 32: LGD Estimation IFRS 9 with SVM and LGD Estimation IFRS 9 with Neural Networks Module 22: Advanced EAD and CCF Modeling Exercise 33: CCF Logistic Regression Model in Python Exercise 34: Neural Networks and SVM CCF in R Exercise 35: Comparison of the Performance of EAD Models Module 23: EAD Lifetime for Lines of Credit Exercise 37: Econometric Model of Credit Line Use in R Exercise 38: EAD Lifetime Model for Individual Line of Credit Exercise 39: EAD Lifetime Vintage Model for Pool of Credit Lines in R and Excel Module 24: Validation of Econometric Models Exercise 40: Non-Normality Test Exercise 41: Detection of Non-Stationary Series and Cointegration Exercise 42: Measuring Multivariate Collinearity in Logistic and Linear Regression Models Exercise 43: Heteroskedasticity Test Exercise 44: Serial Autocorrelation Test Module 25: Macroeconomic Inflation and Geopolitical Scenarios Exercise 45: Advanced Model of BVaR and DSGE Macroeconomic Scenarios Exercise 46: Inflationary Risk Scenarios Exercise 47: Geopolitical Risk Scenarios Module 26: Modernization of Macroeconomic Dynamics using Deep Learning Exercise 48: Bellman Equation Macroeconomic Model using Neural Networks Module 27: Econometric and Deep Learning Models for Macroeconomic Projections Exercise 49: Forecasting GARCH Volatility in Python Exercise 50: Forecasting Multivariate GARCH Volatility in R Exercise 51: Charge-Off Model with VAR and VEC Module 28: Applied Machine Learning to Stress Testing Exercise 52: Stress Testing Model using LASSO Regression Exercise 53: Stress Testing Model using MARS Exercise 54: Stress Testing Model using Deep Learning Feed Forward Module 29: Stress Testing Net Charge-Off Models Exercise 55: MARS Stress Testing Model Exercise 56: Long Short Term Memory (LSTM) Stress Testing Model Module 30: Validation of Stress Testing I Exercise 57: Validation and Backtesting of Econometric and Machine Learning Stress Testing Models (VAR, VEC and MARS) Module 31: Stress Testing PD and LGD Exercise 58: Stress Testing PD in Excel and SAS – Multifactor Credit Portfolio View Model Exercise 59: Stress Testing PD in SAS – Multiyear Approach Exercise 60: Stress Test of PD and Autoregressive Vectors Exercise 61: Stress Test PD – COVID-19 and Climate Change Exercise 62: Stress Test of the LGD Econometric Model in Excel Exercise 63: Joint Stress Test of PD & LGD Module 32: Stress Testing in Corporate Portfolios Exercise 64: Stress Testing PD and Corporate Portfolio Transition Matrices using Transition Matrix and ASRF Model in SAS, R and Excel Module 33: ECL IFRS 9 Stress Testing Exercise 65: ECL Stress Testing using Matrices and Time Series in R and Excel Module 34: Quantum Stress Testing Exercise 66: DGSE Model using Deep Learning Exercise 67: Quantum Monte Carlo Simulation vs Classical Monte Carlo Simulation Module 35: Global Stress Testing Global Exercise 68: Advanced Stress Testing and Capital Planning (Risk Appetite, Business Plan, Balance Sheet & P&L Projections, Capital Planning, Firm-Wide Stress Testing) Module 36: Validation of Stress Testing II Exercise 69: Validation Tests of Stress Testing – VAR vs MARS

Module 1: Impact of COVID-19 on Credit Risk Module 2: Introduction to Climate Change and Financial Risk Management Module 3: Stress Testing in Basel Module 4: 2021 EU-Wide Stress Test Module 5: CCAR Dodd-Frank Act Stress Test (USA) Module 6: ARIMA Models and Vector Autoregressions Exercise 1: Tests for non-stationary series and cointegration in R and SAS Exercise 2: Modeling macroeconomic variables with vector autoregressive models in R and SAS Module 7: Forecasting Models Exercise 3: Tests for non-stationary series and cointegration in R and SAS Exercise 4: Modeling macroeconomic variables with vector autoregressive models in R and SAS Exercise 5: Forecasting GARCH volatility and copulas Exercise 6: Machine learning forecasting with SVM and neural networks in SPSS Module 8: Validation of Econometric Models Exercise 7: Detection of non-stationary series and cointegration Exercise 8: Measurement of multivariate collinearity in linear and logistic regression models Module 9: Determination of Macroeconomic Scenarios in Post-COVID-19 Stress Testing and Climate Scenarios Exercise 9: Macroeconomic GDP scenarios in SAS and R Exercise 10: Network analysis in stress testing Exercise 11: Bayesian networks in stress testing Module 10: Models for Measuring and Forecasting Default Exercise 12: Using roll rates and transition matrices to estimate ALLL in Excel Exercise 14: Multivariate time series of default in SAS Exercise 15: ARIMA time series of net charge-off in R Exercise 16: Structural models in SAS Exercise 17: Markov processes in SAS Exercise 18: Survival models in SAS Exercise 19: Continuous-time transition matrices in SAS Exercise 20: Poisson regression model of defaults in SAS and Excel Module 11: Introduction to Probability of Default (PD) Module 12: Econometric Models of PD Exercise 21: Cox regression in R and SAS Exercise 22: Complementary log-log regression in SAS Module 13: PD Calibration Exercise 23: PD calibration by age of operation in SAS Exercise 24: PD calibration with Cox regression in SAS Exercise 25: PD calibration with complementary log-log model in SAS Exercise 26: PD calibration with logistic model in SAS Exercise 27: PD calibration by vintage in SAS Exercise 28: Point-in-time PD estimation in Excel Exercise 29: Review of country ceiling and minimum PD Module 14: PD Economic Cycle Adjustment Exercise 30: Cycle adjustment for corporations in Excel using Solver Exercise 31: Estimation of through-the-cycle PD using cointegration Exercise 32: Logistic regression TTC PD in SAS Exercise 33: Survival model for TTC PD in SAS Module 15: PD Master Scale Exercise 34: Master scale calibration using CAP curve in Excel Exercise 35: Calibration curves in Matlab Module 16: PD in Low Default Portfolios Exercise 36: Integral-based estimation in SAS of PD for correlated LDPs Exercise 37: CAP curve calibration of master scale in Excel Exercise 38: Bayesian PD in R Module 17: Advanced PD Calibration Exercise 40: PD calibration using quasi-moment matching Exercise 41: PD calibration using most prudent estimation Exercise 42: PD backtesting Module 18: LGD in Retail and Corporate Portfolios Exercise 43: Estimation and analysis of LGD and exposure-weighted average LGD Module 19: Econometric Models of LGD Exercise 44: LGD linear regression in SAS Exercise 44: LGD logistic regression in SAS Exercise 46: Two-stage LGD model: logistic and linear regression in SAS Exercise 47: LGD neural networks Exercise 48: LGD generalized additive model in R Exercise 49: LGD beta regression model in R and SAS Exercise 50: LGD censored regression model in R Exercise 51: Inflated beta regression in SAS Exercise 52: Comparison of model performance using calibration and accuracy tests Module 20: LGD in Low Default Portfolios Exercise 53: Calibration and optimization of implicit LGD in VBA Exercise 54: LGD estimation using linear and Black–Scholes approaches in Excel Module 21: Advanced EAD Modeling for IRB Exercise 55: OLS, beta and inflated beta regression models for CCF in SAS Exercise 56: OLS regression, beta regression, neural networks and logistic regression for CCF in R Exercise 57: Comparison of EAD model performance Module 22: Transition Matrices and PD Term Structure Exercise 58: Analysis and error of transition matrix using cohort and duration approaches in Python Exercise 59: Calibration of the PD term structure Module 23: IFRS 9 PD Forecasting Exercise 60: PD forecasting using VARMAX in R Exercise 61: PD forecasting using ASRF in R and Excel Module 24: Lifetime PD Models Exercise 62: Lifetime PD using vintage EMV decomposition model Exercise 63: Lifetime PD using multinomial regression in R Exercise 64: Lifetime PD using Markov model Exercise 65: Lifetime PD using semiparametric Markov model Exercise 66: Lifetime PD using matrix ASRF model Exercise 67: Lifetime PD using extrapolation approach Exercise 68: Lifetime PD using SVM in Python Exercise 69: Lifetime PD using neural network in Python Module 25: LGD for IFRS 9 Exercise 70: Estimation and adjustments for IFRS 9 LGD using Tobit regression in R Exercise 71: LGD censored regression model in R Exercise 72: IFRS 9 LGD estimation using LASSO regression in Python Exercise 73: IFRS 9 LGD estimation using SVM Exercise 74: IFRS 9 LGD estimation using neural networks Module 26: Contractual Options Exercise 75: IFRS 9 prepayment model for mortgages in R and Excel Module 27: Lifetime EAD for Credit Lines Exercise 76: Econometric model of credit line usage in R Exercise 77: Lifetime EAD model for an individual credit line Exercise 78: Vintage model of lifetime EAD for a pool of credit lines in R and Excel Module 28: Credit Risk Appetite and Stress Testing Exercise 79: Credit risk appetite and stress testing Module 29: Stress Testing Consumer Credit Risk Exercise 80: PD stress testing in Excel and SAS using multifactor Credit Portfolio View model Exercise 81: PD stress testing in SAS using multiyear approach Exercise 82: PD stress test with vector autoregressive models Exercise 83: Net charge-off stress test Exercise 84: EAD stress testing in R Exercise 85: LGD stress test adjusted for climate risk Exercise 86: Charge-off stress test with econometric models Exercise 87: Stress test of delinquency bucket transition matrices Exercise 88: Joint PD and LGD stress test Exercise 89: LGD stress test adjusted for climate risk Module 30: Stress Testing Credit Risk for Corporate Portfolios Exercise 90: Corporate portfolio stress test using transition matrix Module 31: Stress Testing on Balance Sheet and Income Statement Global Exercise 91: Global stress testing in SAS, R, Excel with VBA, SPSS, Gephi and Qlik View Module 32: Stress Testing of IFRS 9 ECL Exercise 92: ECL stress testing using matrices and time series in R and Excel