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Cutting-Edge Stress Testing: The Power of AI and Quantum Computing






Intensive and modern credit risk stress testing course using advanced econometric models, artificial intelligence and quantum computing. The stress tests will test the resilience of banks against an adverse macroeconomic scenario due to assumptions of stagflation, recession of the economy, severe shocks in vulnerable sectors affected by the COVID-19 pandemic, energy crisis triggered by the invasion Russian Ukraine and geopolitical tensions in China.

The new directives on Basel regarding Stress Testing are explained. Stress testing models are exposed during the COVID-19 pandemic as well as new recovery scenarios after the pandemic. Stress testing regulations and exercises are explained: EU-Wide Stress Testing for the year 2023 and Comprehensive Capital Analysis and Review in the United States.

The course explains the use of artificial intelligence in stress tests with the aim of improving the accuracy of the projections, the interpretability of the results, the ability to capture the adaptive behavior of companies and households in the face of structural ruptures in the environment. economy and the halting of supply chains as has occurred during Covid-19.

The accuracy of projections in stress testing can be difficult to achieve due to limited knowledge about the macroeconomic impacts on the profitability, liquidity and soundness of financial companies. Therefore, the course explains to the participant the use of artificial intelligence as a viable option to improve the accuracy of the projections due to the models' ability to capture non-linear effects between the scenario variables and the risk factors that drive the solvency of a financial entity.


The advantages of artificial intelligence models over stress testing models based on traditional econometric models are reviewed.


Dynamic Stochastic General Equilibrium (DSGE) models are a sub-class of applied general equilibrium economic models, widely used for creating Stress Testing scenarios. However, when neural networks are applied in the DSGE, they offer the following advantages: ability to solve high-dimensional problems and high approximation power outside the steady state. But deep learning has limitations and Monte Carlo simulation is essential, so it is possible to use quantum Monte Carlo simulation to improve speed over traditional simulation.

These are the particular objectives of the course.


  • Expose the impact of COVID-19 on banking, inflation, economic recession, energy crises and geopolitical tensions in financial institutions through stress testing practices and scenario analysis.

  • Measure and manage credit risk stress testing in corporate and retail portfolios using econometric models and improvements through artificial intelligence and quantum computing.

  • Explain the impact of COVID-19, recession and inflation on the credit quality of assets and particularly on the estimation of the Expected Credit Losses ECL of IFRS 9.

  • It discusses how to incorporate climate change financial risks into existing financial risk management practice, how to use scenario analysis to inform strategy setting, and risk assessment and identification.

  • Present methodologies to create climate change scenarios and their conversion into macroeconomic scenarios to develop stress testing models.

  • Explain the principles of Basel Stress Testing. The impact and cost-benefit of the directives in financial institutions is analyzed.

  • Teach cutting-edge methodologies to calibrate the PD IRB in retail, corporate, bank and sovereign portfolios.

  • Offer a very important number of PD, LGD and EAD Stress Testing methodologies.

  • Present LGD stress test models for Low Default Portfolio and mortgage portfolios.

  • Address validation methodologies of Stress Testing models.

  • Show how to build scenario analysis of stress testing econometric models.

  • Explain DSGE models and improvements using artificial intelligence and quantum computing.

  • Model the Lifetime PD, LGD and EAD of Expected Credit Losses using state-of-the-art methodologies including machine learning models.

  • Explain methodologies to model the charge-off, net charge-off, recoveries, balances for the estimation of the ECL Loss Rate Approach of IFRS 9.

  • Show ECL IFRS 9 Stress Testing methodologies, SICR and transition matrices

  • Analyze the stress tests in EU-Wide Stress Testing 2023 and the Comprehensive Capital Analysis and Review 2023.

  • Review the effectiveness of Stress Testing in a financial institution with practical examples on limits, capital ratios, KPIs and triggers.

  • A global exercise of Stress Testing, capital management, financial projections of the balance sheet and income statement is exposed, measuring, not only, the impact of stressful scenarios on capital and RWAs, but also the impact on profitability metrics such as KRIs, RAPMs, RARWAs, KPIs, etc.




This program is aimed at directors, managers, consultants, regulators, auditors and credit risk analysts, as well as those professionals who are implementing Stress Testing models. Professionals who work in banks, savings banks and all those companies that are exposed to credit risk. It is important to have knowledge of Statistics and Probability as well as Excel.





Price: 6 900 €





  • Europe: Mon-Fri, CEST 16-19 h


  • America: Mon-Fri, CDT 18-21 h

  • Asia: Mon-Fri, IST 18-21 h






Level: Advanced


Duration: 36 h




  • Presentations PDF

  • Exercises in Excel, R, Python and Jupyterlab 

  • The recorded video of the 40-hour course is delivered.


 Cutting-Edge Stress Testing: The Power of AI

and Quantum Computing



Anchor 10


Module 1: Stress Testing in Basel III

  • Principles of stress testing in Basel

    • 1. Stress testing frameworks should have clearly articulated and formally adopted objectives

    • 2. Stress Testing Frameworks Must Include an Effective Governance Structure

    • 3. Stress tests should be used as a risk management tool and to inform trading decisions.

    • 4. Stress testing frameworks should capture material and relevant risks and apply stresses that are severe enough

    • 5. Resources and organizational structures must be adequate to meet the objectives of the stress testing framework

    • 6. Stress tests must be supported by accurate and sufficiently granular data and robust IT systems

    • 7. Models and methodologies to assess scenario impacts and sensitivities should be fit for purpose

    • 8. Stress testing models, results and frameworks should be subject to periodic challenge and review

    • 9. Stress test practices and results should be communicated within and between jurisdictions

  • Internal and external stress testing

  • Management of stress testing tools

  • Effective governance structure

  • Risk Management Tool

  • Material and relevant risks and stresses that are severe

  • Resources and organizational structures

  • Accurate and sufficiently granular data and robust IT systems

  • The models and methodologies

  • periodic reviews

  • Communication

  • Recovery and resolution plans

Module 2: Stress Testing Methodology

  • Definition and scope of Stress Testing

  • Stress Testing Methodologies

  • Adverse macroeconomic scenarios

  • Treatment in trading portfolios

  • Treatment of sovereign risk

  • Credit risk

  • Provisions per IFRS 9

  • Operational risk

  • Legal Risk

  • Risk Conduct

  • Model Risk

  • Application of macroeconomic scenarios

  • Actives and pasives

  • Capital

  • P&L

  • Stressed PD/Stressed LGD

  • RWA

  • Analysis of Stress Testing Results in the EU for the year 2023


Unsupervised Learning

Module 3: Unsupervised models

  • Hierarchical Clusters

  • K Means

  • standard algorithm

  • Euclidean distance

  • Principal Component Analysis (PCA)

  • Advanced PCA Visualization

  • Eigenvectors and Eigenvalues

  • Exercise 1: Segmentation of the data with K-Means R

Supervised Learning

Module 4: Support Vector Machine SVM

  • SVM with dummy variables

  • SVM

  • optimal hyperplane

  • Support Vectors

  • add costs

  • Advantages and disadvantages

  • SVM visualization

  • Tuning SVM

  • kernel trick

  • Exercise 2:  Modeling PD using Support Vector Machine 

Module 5: Ensemble Learning

  • Set models

  • Classification and Regression Models

  • Bagging

  • Bagging trees

  • Random Forest

  • Boosting

  • adaboost

  • Gradient Boosting Trees

  • Advantages and disadvantages

  • Exercise 3: Regression Random Forest, R and Python

  • Exercise 4: Classification Gradient Boosting Trees




Module 6: Deep Learning Feed Forward Neural Networks

  • Single Layer Perceptron

  • Multiple Layer Perceptron

  • Neural network architectures

  • Activation function

    • sigmoidal

    • Rectified linear unit (Relu)

    • The U

    • Selu

    • hyperbolic hypertangent

    • Softmax

    • other

  • Back propagation

    • Directional derivatives

    • gradients

    • Jacobians

    • Chain rule

    • Optimization and local and global minima

  • Exercise 5: Credit Scoring using Deep Learning Feed Forward

Module 7: Deep Learning Convolutional Neural Networks CNN

  • CNN for pictures

  • Design and architectures

  • convolution operation

  • descending gradient

  • filters

  • strider

  • padding

  • Subsampling

  • pooling

  • fully connected

  • Credit Scoring using CNN

  • Recent CNN studies applied to credit risk and scoring

  • Exercise 6: Credit scoring using deep learning CNN

Module 8: Deep Learning Recurrent Neural Networks RNN

  • Natural Language Processing

  • Natural Language Processing (NLP) text classification

  • Long Term Short Term Memory (LSTM)

  • hopfield

  • Bidirectional associative memory

  • descending gradient

  • Global optimization methods

  • RNN and LSTM for credit scoring

  • One-way and two-way models

  • Deep Bidirectional Transformers for Language Understanding​

  • Exercise 7: Forecasting macroeconomic time series using Deep Learning LSTM

Module 9: Calibrating Machine Learning and Deep Learning

  • Hyperparameterization

  • Grid search

  • Random search

  • Bayesian Optimization

  • Train test split ratio

  • Learning rate in optimization algorithms (e.g. gradient descent)

  • Selection of optimization algorithm (e.g., gradient descent, stochastic gradient descent, or Adam optimizer)

  • Activation function selection in a (nn) layer neural network (e.g. Sigmoid, ReLU, Tanh)

  • Selection of loss, cost and custom function

  • Number of hidden layers in an NN

  • Number of activation units in each layer

  • The drop-out rate in nn (dropout probability)

  • Number of iterations (epochs) in training a nn

  • Number of clusters in a clustering task

  • Kernel or filter size in convolutional layers

  • pooling size

  • batch size

  • Interpretation of the Shap model

  • Exercise 8: Optimization Credit Scoring Xboosting, Random forest and SVM

  • Exercise 9: Credit Scoring Optimized Deep Learning and Model Interpretation


Module 10: Quantum Computing and Algorithms 

  • Future of quantum computing in banking

  • Is it necessary to know quantum mechanics?

  • QIS Hardware and Apps

  • quantum operations

  • Qubit representation

  • Measurement

  • Overlap

  • matrix multiplication

  • Qubit operations

  • Multiple Quantum Circuits

  • Entanglement

  • Deutsch Algorithm

  • Quantum Fourier transform and search algorithms

  • Hybrid quantum-classical algorithms

  • Quantum annealing, simulation and optimization of algorithms

  • Quantum machine learning algorithms

  • Exercise 10: Quantum operations



​Module 11: Credit Scoring for PD estimation


  • Scoring assignment

  • Scorecard Classification

    • Scorecard WOE

    • Binary Scorecard

    • Continuous Scorecard

  • Scorecard Rescaling

    • Factor and Offset Analysis

    • Scorecard WOE

    • Binary Scorecard

  • Reject Inference Techniques

    • Cut-off

    • Parceling

    • Fuzzy Augmentation

    • Machine Learning

  • Advanced Cut Point Techniques

    • Cut-off optimization using ROC curves

  • Exercise 11: Building Scorecard in Excel, R and Python

Module 12: Probability of Default PD

  • PD estimation

    • econometric models

    • Machine Learning Models

    • Data requirement

    • Risk drivers and credit scoring criteria

    • Rating philosophy

    • Pool Treatment

  • PD Calibration

    • Default Definition

    • Long run average for PD

    • Technical defaults and technical default filters

    • Data requirement

    • One Year Default Rate Calculation

    • Long-Term Default Rate Calculation

  • PD Model Risk

    • Conservatism Margin

  • PD Calibration Techniques

    • Anchor Point Estimate

    • Mapping from Score to PD

    • ​Adjustment to the PD Economic Cycle

    • Rating Philosophy

      • PD Trough The Cycle (PD TTC) models

      • PD Point in Time PD (PD PIT ) models

  • PD Calibration of Models Using Machine and Deep Learning

  • Margin of Caution

  • 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

  • PD estimation

  • Treatment of Panel data

  • Econometric models to estimate PD

    • PD Logistic Regression

    • PD Probit Regression

    • PD COX regression of survival

    • PD Log-log Complementary

    • PD Regression Data Panel

    • PD Bayesian Logistic Regression

    • PS Regression Lasso

  • PD Calibration

  • Calibration of econometric models

  • Anchor Point Estimate

  • PD calibration by vintages or vintages

  • vintage analysis

    • PS Marginal

    • PS Forward

    • Cumulative 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

  • Bayesian and deterministic approach

  • Confidence intervals

  • Expert judgment

  • Prior distributions

  • Bayes' theorem

  • Posterior distributions

  • Bayesian PD Estimation

  • Markov Chain–Monte Carlo MCMC approach

  • Credibility intervals

  • Bayesian PD in practice

  • Calibration with Bayesian approach

  • Convergence test

  • Exercise 18: Logistic Model Bayesian PD in Python



Module 16: IFRS 9 PD Forecasting

  • IFRS 9 requirements

- Probability Weighted Outcome

- Forward Looking

  • Lifetime PD modeling

  • PD Forecasting Modeling

  • PD Point in Time Forecasting

  • PS TTC Forecasting

  • Markov models

  • PIT PD Forecasting Models

    • ARIMA

    • VAR

    • VARMAX

    • ASRF

  • Exercise 19: Forecasting PD using VARMAX in R

Module 17: Lifetime PD Models

  • PD Lifetime consumer portfolio

  • PD Lifetime mortgage portfolio

  • PD Lifetime Wallet Credit Card

  • PD Lifetime portfolio SMEs

  • Vintage model

    • Exogenous Maturity Vintage EMV Model

    • decomposition analysis

    • Advantages and disadvantages

  • Basel ASRF model

    • Matrix ASRF model

    • Leveraging IRB in IFRS 9

    • Advantages and disadvantages

  • Regression Models

    • Logistic Multinomial Regression

    • Ordinal Probit Regression

  • Survival Models

    • Kaplan–Meier

    • Cox Regression

    • Advantages and disadvantages

  • Markov models

    • ​​Multi-State Markov Model

    • Cox Semiparametric Model

    • Advantages and disadvantages

  • Machine Learning Model​

    • SVM: Kernel Function Definition

    • Neural Network: definition of hyperparameters and activation function

  • PD Lifetime Extrapolation 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

  • PD lifetime modeling

  • Exogenous Maturity Vintage

  • Age Period Cohort

  • Classic Monte Carlo simulation

  • Quantum Monte Carlo Simulation

  • Quadratic acceleration over the classical Monte Carlo simulation

  • PD lifetime modeling

  • Monte Carlo Markov Chain MCMC

  • Quantum enhancement in MCMC

  • Exercise 26: Lifetime PD IFRS 9 estimation using quantum enhancements



​Module 19: LGD in Retail Portfolios and IRB Companies

  • Impact of COVID-19 on LGD

    • definition of default

    • moratoriums

    • Renovations and restructuring

    • Default Cycle

    • Real Default Cycles

  • Expected Loss and Unexpected Loss in the LGD

  • LGD in Default

  • Default Weighted Average LGD or Exposure-weighted average LGD

  • LGD for performing and non-performing exposures

  • Treatment of collaterals in the IRB

  • Workout Focus

    • Techniques to determine the discount rate

    • Treatment of recoveries, expenses and recovery costs

    • Default Cycles

    • recovery expenses

  • Downturn LGD in consumer portfolios

  • Downturn LGD in Mortgages

  • LGD in consumption

  • LGD in Mortgages

  • LGD in companies

  • LGD for portfolios with replacement

  • Exercise 27: LGD formulas and LGD Downtutn models

Module 20: Econometric and Machine Learning Models of the LGD

  • Advantages and disadvantages of LGD Predictive Models

  • Forward Looking models incorporating Macroeconomic variables

  • Parametric and non-parametric models and transformation regressions

  • Typology of LGD Multivariate Models

    • Linear regression and Beta transformation

    • Linear Regression and Logit Transformation

    • Linear regression and Box Cox transformation

    • Logistic and Linear Regression

    • Logistic and nonlinear regression

    • Censored Regression

    • Generalized Additive Model

    • Beta regression

    • Inflated beta regression

  • Support Vector Regression

  • Support Vector Classification

  • Random Forest Regression

  • XGBoosting Regression

  • neural networks

  • deep learning

  • Exercise 28: LGD econometric models

  • Exercise 29: Machine Learning and Deep Learning Models of LGD

Module 21: LGD for IFRS 9

  • Comparison of IRB LGD vs. IFRS 9

  • Impact on COVID-19

  • IFRS 9 requirements

    • Probability Weighted

    • Forward Looking

  • IRB LGD adjustments

    • Selection of Interest Rates

    • Allocation of Costs

    • floors

    • Treatment of collateral over time

    • Duration of COVID-19

  • LGD PIT modeling

  • Collateral Modeling

  • LGD IFRS 9 for portfolio companies

  • LGD IFRS 9 for mortgage portfolio

  • LGD IFRS 9 for corporate portfolios

    • Credit cycle

    • Tobit Regression

  • IFRS 9 LGD using LASSO Regression​

  • Machine Learning Models

    • Support Vector Machine

    • Neural Networks

  • Exercise 30: Estimation and adjustments for LGD IFRS 9 using Tobit regression in R

  • Exercise 31: Censored Regression Model LGD in R

  • Exercise 32: LGD Estimation IFRS 9 SVM and LGD Estimation IFRS 9 NN


Module 22: Advanced EAD and CCF modeling

  • Impact of COVID-19 on credit lines

  • Guidelines for estimating CCF

  • Guidelines for Estimating CCF Downturn

  • Temporal horizon

  • Transformations to model the CCF

  • Approaches to Estimating CCF

    • Fixed Horizon approach

    • Cohort Approach

    • Variable focus time horizon

  • Econometric Models

    • Beta regression

    • Inflated beta regression

    • Fractional Response Regression

    • Mixed Effect Model

  • Machine Learning Models

    • Neural networks

    • SVM

  • Intensity model to measure the withdrawal of credit lines

  • 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

  • Impact of the pandemic on the use of credit lines

  • Lifetime measurement in credit cards

  • Lifetime EAD

  • IFRS 9 requirements

  • Probability Weighted

  • Forward Looking

  • Adjustments in the EAD

  • Interest Accrual

  • CCF PIT Estimate

  • CCF Lifetime Estimate

  • EAD lifetime modeling

  • Model of the use of credit lines with macroeconomic variables

  • Credit card abandonment adjustment

  • EAD Lifetime model for pool of lines of credit

    • vintage model

    • Chain Ladder Approach

  • 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

  • Review of assumptions of econometric models

  • stationary series

  • Cointegrity tests

  • Review of the coefficients and standard errors of the models

  • Model reliability measures

  • Error management

  • Non-normality test

  • Measurement and treatment of Heteroskedasticity

  • Detection and treatment of Outliers

  • Serial Autocorrelation

  • Multicollinearity

    • Using Correlation to detect bivariate collinearity

    • Detection of multivariate collinearity in linear regression

    • Detection of multivariate collinearity in logistic regression

  • Seasonality treatment

  • Exercise 40: non-normality test

  • Exercise 41: Non-stationary series detection and cointegration

  • Exercise 42: Measuring the multivariate collinearity of the logistic and linear regression model

  • Exercise 43: heteroskedasticity test

  • Exercise 44: serial autocorrelation test


Module 25: Macroeconomic Inflation,

 and Geopolitical Scenarios

  • Macroeconomic inflation scenarios

  • Geopolitical scenarios

  • scenario analysis

  • Design of adverse scenarios

  • Financial and economic shocks

  • Important macroeconomic variables

  • Structural macroeconomic models

  • Bayesian VaR

  • balance models

    • Dynamic Stochastic General Equilibrium (DSGE)

  • Non-equilibrium models

    • Sensitivity Analysis

  • ​Integrated assessment model (IAM)

  • Computable general equilibrium (CGE)

  • Overlapping generation

  • input-output

  • Agent-based

  • Scenario analysis

  • Expert judgment in stage design

  • Scenario severity score

  • scenario validation

  • 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


  • Macroeconomic models

  • ​Neoclassical growth model

  • Partial differential equations

  • DSGE Stochastic Dynamic General Equilibrium Models

  • Deep learning architectures

  • Reinforcement Learning

  • Advanced Scenario Analysis

  • Exercise 48: Bellman equation macroeconomic model using neural networks

Module 27: Econometric and Deep Learning Models for Macroeconomic Projections


  • Econometric models

  • Temporal series


    • temporary windows

    • hyper parameters

  • Multivariate Models

    • VAR Autoregressive Vector Models

    • ARCH models

    • GARCH models

    • GARCH Models Multivariate Copulas

    • VEC Error Correction Vector Model

    • VARMAX model

  • Hyper parameters​ on VARMAX and VAR

  • Machine Learning Models

    • Supported Vector Machine

    • neural network

      • Forecasting market time series yields

      • NN and SVM algorithms for performance forecasting

      • Forecasting volatility NN vs. Garch

    • Development and validation base

  • Deep learning

    • Recurrent Neural Networks RNN

    • Elman's Neural Network

    • Jordan Neural Network

    • Basic structure of RNN

    • Long short term memory LSTM

    • temporary windows

    • Development and validation sample

    • Regression

    • Sequence modeling

  • Time series analysis with Facebook Prophet​

  • Prediction of the spread of Covid-19

  • Exercise 49: Forecasting GARCH volatility in Python

  • Exercise 50: Forecasting GARCH Multivariate volatility in R

  • Exercise 51: Charge-off model with VAR and VEC

Module 28: Applied machine learning to Stress Testing

  • Stress Testing: a multi-step process

  • Challenges of model selection in stress tests

  • Machine Learning: the balance between interpretability and flexibility

  • Linear Models: Subset Selection and Contraction Methods

  • Lasso Regression Applications

  • A stress test app: PD projection

  • Multivariate adaptive regression splines MARS

    • MARS vs. VAR

  • Black-box models

  • Interpretation approaches

  • Shapley value in stress testing models

  • Calibration of stress testing models

  • Agent-Based Models

  • Stress Testing using deep learning

  • Feed forward neural networks

  • 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

  • Stress Testing Net Charge-Off

    • Temporal horizon

    • Multi-period approach

    • Data required

    • Failed balance or penalty

    • Selection of Macroeconomic scenarios

    • Charge Off

    • Net Charge Off

    • Losses on new impaired assets

    • Losses on old impaired assets

    • Net charge-off forecasting

  • Multivariate time series

    • Vector Autoregressive (VAR)

    • Vector Error Correction (VEC) Models

  • Machine Learning Models

    • Multivariate adaptive regression spline (MARS)

  • Deep Learning Model​

    • Long Short Term Memory

  • Exercise 55: MARS stress testing model

  • Exercise 56: Long Short Term Memory stress testing model

Module 30: Validation of Stress Testing I

  • Stress testing validation

  • Validation of econometric models

    • Performance metrics

    • Out of sample

    • Generalized Cross Validation – GCV

    • Squared Correlation - SC

    • Root Mean Squared Error – RMSE

    • Cumulative Percentage Error – CPE

    • Aikaike Information Criterion - AIC

    • backtesting

    • Temporal horizon

    • Magnitude of the error

    • Performance metrics

  • Validation of Machine Learning models

    • loss reduction

    • hyperparameters

    • Overfitting

    • Blackbox

    • Variable Interpretation

  • Exercise 57: Validation and backtesting of econometric Stress Testing models and Machine learning VAR, VEC and MARS​

Module 31: Stress Testing PD and LGD

  • Temporal horizon

  • Multi-period approach

  • Data required

  • Impact on P&L, RWA and Capital

  • Macroeconomic Stress Scenarios in consumption

    • Expert

    • Statistical

    • regulatory

  • PD Stress Testing:

    • Credit Portfolio View

    • Multiyear Approach

    • Reverse Stress Testing

    • Rescaling

    • Cox Regression

    • Covid-19 Stress Testing

    • Stress Testing for climate change

  • Stress Testing of the Transition Matrix

    • Approach Credit Portfolio View

    • Credit cycle index

    • Multifactor Extension

  • LGD Stress Testing:

    • LGD Downturn: Mixed Distribution Approach

    • PD/LGD Multiyear Approach modeling

    • LGD stress test for mortgage portfolios

  • Stress Testing of transition matrices

  • ​Exercise 58: Stress Testing PD in Excel and SAS multifactorial model Credit Portfolio Views

  • 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 the PD&LGD

Module 32: Stress Testing in corporate portfolios

  • Temporal horizon

  • Data required

  • Main Macroeconomic variables

  • Impact on P&L, RWA and Capital

  • ASRF model

  • Creditmetrics model

  • Using Transition Matrices

  • Use of the credit cycle index

  • Default forecasting

  • Stress Test Methodology for corporate portfolios

  • Impact on RWA and Capital

  • 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

  • Stress testing IFRS 9 and COVID-19

  • Pandemic scenarios applied to the ECL calculation

  • Stress Testing of IFRS 9 parameters

  • EBA Stress Testing 2023

  • Treatment of the moratorium

  • Possible regulatory scenarios

  • Impact on P&L

  • PIT starting parameters

  • PIT projected parameters

  • Calculation of non-productive assets and impairments

  • Changes in the stock of provisions

  • Changes in the stock of provisions for exposures phase S1

  • Changes in the stock of provisions for exposures phase S2

  • Changes in the stock of provisions of exposures phase S3

  • Sovereign Exposure Impairment Losses

  • Impact on capital

  • Internal Stress Testing Model for ECL IFRS 9

  • Exercise 65: ECL Stress Testing Using Matrices and Time Series R and Excel



Module 34: Quantum Stress Testing

  • Quantum economics

  • Classic Monte Carlo simulation

  • Quantum Monte Carlo

  • Coding Monte Carlo problem

  • Breadth Estimation

  • Acceleration applying the amplitude estimation algorithm

  • DGSE model using neural networks

  • Quantum Monte Carlo Simulation vs Normal Monte Carlo Simulation

  • Exercise 66: DGSE model using deep learning

  • Exercise 67: Quantum Monte Carlo Simulation vs. Classical Monte Carlo Simulation

​Module 35: Global Stress Testing

  • Stress Testing during COVID-19

  • Stress Testing with geopolitical tensions

  • Stress Testing EBA Europe 2023

  • Stress testing and joint capital planning

  • Definition of scenarios

  • Balance sheet and income statement projection

  • Static Balance Sheet vs Dynamic Balance Sheet

  • Projection of capital requirements

  • Solvency Analysis

  • Action plans

  • Global Exercise 68: Advanced Stress Testing and Capital Planning:

  • Risk Appetite

  • Risk Appetite Statement

  • Business plan

    • Forecasting of the Income Statement in 3 years

    • Forecasting of the Balance Sheet in 3 years

  • Capital planning

  • Application of Scenarios and External Shocks

  • Network analysis of main variables

  • Stress Testing Probabilistic Graphical Model

  • Stress Testing of IFSR 9 endowments

  • Stress Testing and counterparty venture capital

  • Stress Testing and capital for interest rate risk

  • Stress Testing and operational risk capital

  • Stress Testing and market venture capital

  • Stress Testing liquidity risk

  • Stress Testing conduct and reputational risk

  • Firm Wide Stress Testing

  • Impact Review on:

    • CET1 capital, regulatory capital and RWAs

    • ​Balance Sheet

    • P&L Income Statement

    • EVE and NII

    • Sectoral and individual concentration risk

    • Excess limits

    • LCR and NSFR liquidity ratios

    • Liquidity buffer

    • Leverage Rate Calculation

    • Business KPIs and critical values

    • KRIs and main critical values

    • RAPM estimation

    • Profitability Metrics

  • Control panel

  • solvency analysis

  • Action plans



Module 36: Validation of Stress Testing II

  • Validation of Stress Testing

  • Validation of the Best Case and adverse scenarios

  • Stationarity of variables

  • The signs of economically intuitive coefficients

  • Statistical significance of coefficients

  • confidence levels

  • residual diagnoses

  • Model performance metrics

    • goodness of fit

    • Risk classification

    • cumulative error measures

  • Industry Accepted Thresholds

  • Intuitive sort order

  • Generalized Cross Validation

  • Squared Correlation

  • Root Mean Squared Error

  • Cumulative Percentage Error

  • Akaike Information Criterion

  • Exercise 69: Validation tests of stress testing VAR vs MARS

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