Question 1

Operational Risk and Cyber Risk in Python and R 30 h

Accepted Answer

Module 1: Minimum Capital Requirements for Operational Risk – Basel IV
 • Exercise 1: Measurement of SMA capital, BI simulation, Loss Multiplier and financial projection in Excel.
Module 2: Operational Risk Requirements under Pillar 2
(No exercises listed)
Module 3: Risk Control Self Assessment (RCSA)
(No exercises listed)
Module 4: Internal Loss Database
(No exercises listed)
Module 5: External Loss Databases
(No exercises listed)
Module 6: Key Risk Indicators
(No exercises listed)
ECONOMIC CAPITAL FOR OPERATIONAL RISK – PILLAR 2
Module 7: Estimation of Frequency and Severity Distributions
 • Exercise 2: Severity distribution fitting (Lognormal, Weibull, Exponential, Inverse Gaussian, Gamma, Burr, Alpha-Stable) in R & Python.
 • Exercise 3: Frequency distribution fitting: Poisson & Negative Binomial in R & Python.
 • Exercise 4: Fitting GB2 distribution in R.
 • Exercise 5: Fitting g-and-h distribution in Excel.
 • Exercise 6: Maximum likelihood estimation of lognormal mixtures in R.
 • Exercise 7: Mean Excess, Q-Q, Hill plots in Python.
 • Exercise 8: MLE for Generalised Pareto Distribution (GDP) in R.
 • Exercise 9: EVT random number generation in R.
 • Exercise 10: Lognormal body + EVT tail fitting in R.
 • Exercise 11: Regulatory review of severity distributions.
 • Exercise 12: Review of robust estimators.
Module 8: Bayesian Inference & MCMC
 • Exercise 13: Bayesian estimation of Gamma–Poisson parameters in R.
 • Exercise 14: Bayesian estimation of Gamma–Pareto parameters in R.
 • Exercise 15: Bayesian estimation of Normal–Lognormal parameters in R.
 • Exercise 16: MCMC posterior simulation in Python.
Module 9: Goodness-of-Fit Tests
 • Exercise 17: Comparative density plot of distributions in R.
 • Exercise 18: K-S, Anderson–Darling, Cramer–von Mises tests in R.
 • Exercise 19: Confidence intervals for Lognormal parameters.
 • Exercise 20: Random number generation of parametric distributions.
 • Exercise 21: Performance statistics (LR, AIC, BIC).
Module 10: Modeling Truncated Data
 • Exercise 22: MLE estimation & GDP simulation in R.
Module 11: External Data & Scenario Treatment
 • Exercise 23: Poisson regression for frequency rescaling in R.
Module 12: Scenario Analysis Modeling
 • Exercise 24: Fitting distributions to scenario analysis in Excel.
Module 13: BEICFs Modeling
 • Exercise 25: BEICF modeling in Excel.
Module 14: Loss Aggregation Techniques
 • Exercise 26: VaR and ES estimation in Python.
 • Exercise 27: Latin Hypercube variance reduction in R.
 • Exercise 28: Panjer recursion & FFT loss aggregation in R.
 • Exercise 29: Monte Carlo simulation in Python & Cython.
Module 15: Dependence Modeling
 • Exercise 30: Archimedean & t-Student copulas in R.
 • Exercise 31: Dependence between frequencies using Gaussian copula.
 • Exercise 32: Dependence between unit-level losses using copulas.
Module 16: LDA and Scenario-Based Approach
(No exercises listed)
Module 17: Insurance Analysis and Modeling
 • Exercise 33: Probability models for insurance recovery rates in Excel.
 • Exercise 34: Simulation of expected/unexpected loss with random recoveries in R.
Module 18: Global Economic Capital Exercise
 • Exercise 25: Economic capital estimation for 5 business units (multiple distributions).
 • Exercise 35: Best distribution selection using goodness-of-fit tests.
 • Exercise 36: Economic capital comparison using VaR 99.9% vs ES.
 • Exercise 37: Net/Gross capital with insurance via Monte Carlo (R, Python, Cython).
 • Exercise 38: Economic capital using Gaussian & t-copula aggregation (Python, R).
 • Exercise 39: Capital comparison: Panjer vs FFT vs Monte Carlo.
 • Exercise 40: Subexponential distribution modelling in R.
 • Exercise 41: Interpretation of results: capital vs expected loss.
 • Exercise 42: Model risk from new operational risk capital rules (AMA vs SMA).
 • Exercise 43: Backtesting methods for severity & frequency.
 • Exercise 44: Economic capital vs SMA comparison.
Module 19: Model Risk – Parameter Uncertainty
 • Exercise 45: Model risk of Lognormal & Burr distributions (parameter uncertainty).
 • Exercise 46: VaR & ES model risk under parameter uncertainty (Lognormal).
 • Exercise 47: Bayesian model risk of severity parameters.
Module 20: Model Risk – Data Deficiency
 • Exercise 48: Data quality analysis.
 • Exercise 49: Multivariate imputation model for missing data.
 • Exercise 50: ML for outlier detection.
 • Exercise 51: Optimal univariate analysis.
 • Exercise 52: Confidence intervals for operational risk distributions.
 • Exercise 53: Quantification of model risk due to data deficiency.
STRESS TESTING
Module 21: Stress Testing & Forecasting of Operational Risk
 • Exercise 54: Linear regression with macro variables.
 • Exercise 55: Poisson & Negative Binomial regression for frequency.
 • Exercise 56: Quantile regression for operational loss severity.
 • Exercise 57: VAR & ARIMAX forecasting in Python.
 • Exercise 58: Monte Carlo simulation with macro variables in R.
 • Exercise 59: Performance comparison with calibration/precision tests.
Module 22: Risk Appetite & Economic Capital
 • Exercise 60: Global Risk Appetite exercise for operational risk (limits, KRIs, AMA/SMA, stress testing).
CAPITAL FOR CYBER RISK
Module 23: Economic Capital for Cyber Risk
 • Exercise 61: Expected Loss & VaR of Cyber Risk in Python.

Question 2

Model Risk: Quantification, Management, and Machine Learning in Python and R 30h

Accepted Answer

Module 1: Introduction to Model Risk
(No exercises)
Module 2: Model Risk Management
(No exercises)
Module 3: Model Risk Management Guidelines – SR 11-7 (US)
(No exercises)
Module 4: Guide for the Targeted Review of Internal Models (TRIM – EU)
(No exercises)
Module 5: Quantitative Measurement of Model Risk
(No exercises)
Module 6: Models to Quantify Model Risk
 • Exercise 1: Estimation of a model risk scorecard, definition of scale and ranges.
 • Exercise 2: Estimation of model risk using frequency and severity distributions (Poisson, Negative Binomial; Lognormal, Burr, Gamma, Weibull, Inverse Gaussian, GDP EVT, 4-parameter g-and-h, lognormal mixtures, lognormal–EVT, Poisson–Gamma Bayesian, lognormal–GDP partition, expert-based scenarios).
 • Exercise 3: Selection of the best distribution using goodness-of-fit tests (Cramér–von Mises, Anderson–Darling, Kolmogorov–Smirnov).
 • Exercise 4: Comparison of model risk using Panjer recursion, Fast Fourier Transform and Monte Carlo simulation.

Module 7: Advanced Data Validation
 • Exercise 5: Exploratory data analysis in R.
 • Exercise 6: Detection and treatment of outliers in R.
 • Exercise 7: Missing value imputation techniques in R.
 • Exercise 8: Stratified and random sampling.
 • Exercise 9: Weight of Evidence (WOE) analysis in Excel.
 • Exercise 10: Univariate percentile analysis in R.
 • Exercise 11: Optimal univariate analysis for continuous variables in R.
 • Exercise 12: KS, Gini and Information Value validation for each variable in R and Excel.
 • Exercise 14: Optimization of categorical variables in R.
 • Exercise 15: Univariate analysis with decision trees in R.
 • Exercise 16: Segmentation using K-means clustering in R.

Module 8: Multivariate Models and Machine Learning
 • Exercise 17: Logistic regression with stepwise selection in R.
 • Exercise 18: Piecewise regression in Excel and R.
 • Exercise 19: Decision trees in R.
 • Exercise 20: Support Vector Machine in R.
 • Exercise 21: Neural networks in R.
 • Exercise 22: Ensemble models in R.
 • Exercise 23: Random Forest in R.
 • Exercise 24: Bagging in R.
 • Exercise 26: Comparison of discriminant power between neural networks and logistic regression models.
 • Exercise 27: Model risk using confidence intervals of logistic regression coefficients.

Module 9: Model Risk in the Scorecard
 • Exercise 28: Construction of a scorecard in Excel.
 • Exercise 29: Optimal cut-off point estimation in Excel and model risk from cut-off selection.
 • Exercise 30: Confusion matrix to verify Type I and Type II error in Excel with and without variables.

Module 10: Stability Tests
 • Exercise 31: Stability tests for models and factors.

Module 11: Discriminant Power of Traditional and Machine Learning Models
 • Exercise 32: Estimation of Gini, Information Value, Brier Score, Lift curve, CAP, ROC and divergence in SAS and Excel.
 • Exercise 33: Jackknifing.
 • Exercise 34: Bootstrapping.
 • Exercise 35: Kappa statistic estimation.
 • Exercise 36: K-fold cross-validation in R.
 • Exercise 37: Traffic-light (semaphore) validation out-of-time (6-year horizon) for logistic and ML models.

Module 12: Model Risk in Credit Scoring
 • Exercise 38: Application of a credit scoring tool over a 2-year horizon without updating data.
 • Exercise 39: Measurement of model risk via Type I/II errors and losses from accepting bads and opportunity cost from rejecting goods.

Module 14: Explainable Artificial Intelligence (XAI)
 • Exercise 40: XAI-based interpretability of credit scoring.

Module 15: Automation of Modelling
 • Exercise 41: Automation of credit scoring modelling, hyperparameter optimization and validation.

Module 16: PD Calibration and Backtesting
 • Exercise 42: PD calibration using central tendency adjustment.
 • Exercise 43: PD calibration using quasi-moment matching.
 • Exercise 44: PD calibration using regression approaches.
 • Exercise 45: PD calibration in machine learning models.
 • Exercise 46: PD backtesting in Excel.
 • Exercise 47: Forecasting PD and realized PD in Excel.
 • Exercise 48: Validation using Monte Carlo simulation.

Module 17: Alternative PD Backtesting
 • Exercise 49: PD validation using a latent-variable model.

Module 18: LGD Calibration and Backtesting
 • Exercise 50: Model performance comparison using calibration and accuracy tests.

Module 19: EAD Backtesting
 • Exercise 51: Performance comparison of EAD models.

Module 20: Lifetime PD Estimation and Backtesting (IFRS 9)
 • Exercise 52: Machine learning to estimate Lifetime PD.
 • Exercise 53: Multinomial regression to estimate Lifetime PD.
 • Exercise 54: Cox regression to estimate Lifetime PD.
 • Exercise 55: Multi-stage Markov chains in R.
 • Exercise 56: Stability and accuracy testing of Lifetime PD.
 • Exercise 57: Lifetime PD calibration for machine learning models.

Module 21: Estimation and Validation of Expected Credit Losses (ECL) – IFRS 9
 • Global Exercise 58: Lifetime ECL estimation (Lifetime PD, LGD, EAD, prepayment probability and scenarios).
 • Global Exercise 59: Validation through ECL simulation and economic capital.

Module 22: IFRS 9 ECL Validation

(No exercises)

Module 23: Economic Capital Models
 • Exercise 60: Default correlation in retail/consumer portfolios.
 • Exercise 61: Asset correlation with EMV (e.g., Exogenous Maturity Vintage).
 • Exercise 62: Sectoral CreditRisk+ implementation.
 • Exercise 63: Single-factor model.
 • Exercise 64: Multi-factor model.
 • Exercise 65: Copulas in R.

Module 24: Validation and Model Risk in Economic Capital
 • Exercise 66: Capital validation using the Berkowitz test.
 • Exercise 67: Model risk due to uncertainty in CreditRisk+.
 • Exercise 68: Model risk due to data deficiencies in CreditRisk+.

Module 25: Model Risk – Parameter Uncertainty in Credit Risk
 • Exercise 69: Model risk measurement in an ASRF credit risk model (parameter uncertainty in credit scoring, PD and LGD calibration).
 • Exercise 70: Model risk of VaR and ES under parametric lognormal distribution.
 • Exercise 71: Model risk from PD and asset-correlation parameter uncertainty using a Bayesian approach.
 • Exercise 72: Propagation of uncertainty in credit risk models.

Module 26: Statistical Validation of Models
 • Exercise 73: Comprehensive analysis of regression statistical tests.

Module 27: Estimation and Validation of Financial & Macroeconomic Time Series Models
 • Exercise 74: Non-stationary and cointegrated series analysis.
 • Exercise 75: GARCH modelling of market variables.
 • Exercise 76: Machine learning modelling (SVM and neural networks).
 • Exercise 77: Backtesting of machine learning time series models.

Module 28: Determination of Macroeconomic Scenarios in IFRS 9
 • Exercise 78: Advanced macroeconomic scenario modelling with BVaR and DSGE.

Module 29: Stress Testing Net Charge-Off
 • Exercise 79: VAR stress testing model.
 • Exercise 80: VEC stress testing model.
 • Exercise 81: MARS stress testing model.

Module 30: Stress Testing Validation
 • Exercise 82: Validation tests comparing VAR vs MARS stress testing models.

Module 31: Model Risk in IFRS 9 Stress Testing
 • Exercise 83: Model risk in stress testing using the Credit Portfolio View approach.
 • Exercise 84: Model risk in the Multiyear approach.
 • Exercise 85: Model risk in LGD stress testing.
 • Exercise 86: Model risk in transition matrix stress testing.

Module 32: Margin of Conservatism (MoC) – IRB
 • Exercise 87: Estimation of MoC for PD, LGD and EAD under two different methodologies.

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📘 SECTION 1 — Duration & Workload

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