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