CuttingEdge Stress Testing: The Power of AI and Quantum Computing
COURSE OBJECTIVE
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 COVID19 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 COVID19 pandemic as well as new recovery scenarios after the pandemic. Stress testing regulations and exercises are explained: EUWide 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 Covid19.
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 nonlinear 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 subclass 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 highdimensional 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 COVID19 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 COVID19, 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 costbenefit of the directives in financial institutions is analyzed.

Teach cuttingedge 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 stateoftheart methodologies including machine learning models.

Explain methodologies to model the chargeoff, net chargeoff, 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 EUWide 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.
WHO SHOULD ATTEND?
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 €
Schedules:

Europe: MonFri, CEST 1619 h

America: MonFri, CDT 1821 h

Asia: MonFri, IST 1821 h
Level: Advanced
Duration: 36 h
Material:

Presentations PDF

Exercises in Excel, R, Python and Jupyterlab

The recorded video of the 40hour course is delivered.
AGENDA
CuttingEdge Stress Testing: The Power of AI
and Quantum Computing
STRESS TESTING
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
MACHINE LEARNING
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 KMeans 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
DEEP LEARNING
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

Oneway and twoway 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 dropout 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
QUANTUM COMPUTING
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 quantumclassical algorithms

Quantum annealing, simulation and optimization of algorithms

Quantum machine learning algorithms

Exercise 10: Quantum operations
PD IRB
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

Cutoff

Parceling

Fuzzy Augmentation

Machine Learning


Advanced Cut Point Techniques

Cutoff 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

LongTerm 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 Loglog 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
PD IFRS 9
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

MultiState 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
QUANTUM LIFETIME PD
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
LGD IRB and IFRS 9
Module 19: LGD in Retail Portfolios and IRB Companies

Impact of COVID19 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 Exposureweighted average LGD

LGD for performing and nonperforming 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 nonparametric 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 COVID19

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 COVID19


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
EAD IFRS 9
Module 22: Advanced EAD and CCF modeling

Impact of COVID19 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

Nonnormality 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: nonnormality test

Exercise 41: Nonstationary 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
SCENARIO ANALYSIS
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)


Nonequilibrium models

Sensitivity Analysis


Integrated assessment model (IAM)

Computable general equilibrium (CGE)

Overlapping generation

inputoutput

Agentbased

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
AI SCENARIO ANALYSIS
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

AR, MA, WEAPON, ARIMA, SARIMA

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 Covid19

Exercise 49: Forecasting GARCH volatility in Python

Exercise 50: Forecasting GARCH Multivariate volatility in R

Exercise 51: Chargeoff model with VAR and VEC
Module 28: Applied machine learning to Stress Testing

Stress Testing: a multistep 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


Blackbox models

Interpretation approaches

Shapley value in stress testing models

Calibration of stress testing models

AgentBased 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
STRESS TESTING MODELS
Module 29: Stress Testing Net ChargeOff Models

Stress Testing Net ChargeOff

Temporal horizon

Multiperiod 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 chargeoff 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

Multiperiod 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

Covid19 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 Covid19 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
STRESS TESTING ECL IFRS 9
Module 33: ECL IFRS 9 Stress Testing

Stress testing IFRS 9 and COVID19

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 nonproductive 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
QUANTUM STRESS TESTING
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 COVID19

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
STRESS TESTING VALIDATION
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