COURSE OBJECTIVE
Recently, the number of models used in financial institutions has increased exponentially, particularly in the field of credit risk, such is the case of scoring models in admission, monitoring and recovery, machine learning models, IRB parameters, capital, correlations, stress testing and the recent IFRS 9 parameters, among many others.
This proliferation of models has benefits such as automation, efficiency and speed in decision making. However, they also have drawbacks, due to decisions made by the wrong models or used inappropriately.
Model risk, in the United States, is defined as the set of possible adverse consequences derived from decisions based on results and incorrect reports of models, or from their inappropriate use. The European regulator defines it as the risk related to the underestimation of own funds, for example, due to the use of the IRB.
The objectives of the course are the following:

Explain the definition and scope of the model risk, the best practices in terms of management, control, governance validation and quantification of the same. Know how COVID19 impacts credit risk models and the model risk itself.

Explain the pioneering model risk directive SR 117 in the US, the recent internal model review directive, TRIM, in the European Union, EU, and other important model risk and validation directives such as the estimation directive of PD and LGD and treatment of EBA default exposures.

Explain the use of artificial intelligence for model validation.

Techniques are shown to achieve the automation of the Construction and Calibration of Models through Artificial Intelligence.

Indicate the best practices for validation of credit risk models of financial institutions.

Show model risk quantification techniques in credit scoring models, PD, LGD and EAD parameters and regulatory and economic capital.

Explore credit scoring validation techniques, and others such as discriminant power, stability tests and backtesting.

Offer a very significant number of econometric and machine learning methodologies to develop credit scoring, PD, LGD and EAD models under the IRB and IFRS9 approaches.

Explain methodologies to develop models of economic capital and stress testing.

Present validation techniques for economic and regulatory capital models.

Show a significant number of validation techniques for econometric models and time series used in stress testing.

Modeling the stress testing of the PD, LGD and transition matrices of consumer and corporate portfolios.

Show innovative stress testing validation techniques.

Explain and detect model risk in stress testing.
WHO SHOULD ATTEND?
This program is aimed at managers, analysts and credit risk consultants. Particularly, to professionals of model risk, model validation and model auditing. For a better understanding of the topics, it is recommended that the participant have knowledge of statistics. The course contains exercises in SAS, R and Excel.
Schedules:

Europe: MonFri, CEST 1619 h

America: MonFri, CDT 1821 h

Asia: MonFri, IST 1821 h
Price: 6 900 €
Summer Price: 5 900 €
Level: Advanced
Duration: 30 h
Material:
Presentations PDF
Exercises in R, Python, SAS and Excel
Comprehensive Guide to Model Risk
in Credit Risk Management
AGENDA
Comprehensive Guide to Model Risk
in Credit Risk Management
INTRODUCTION RISK MODEL
Module 1: Risk Management Model and Quantification

Model Risk Definitions

model risk management

Model Definition

Sources of Model Risk

Dating

Estimate

Use


Inventory of risk models

control methodology

Process and technology management

governance

Model lifecycle management

Model risk quantification

Quantitative risk management cycle model

source identification

Model risk mitigation

Model documentation

Model validation

Profile of model risk teams in financial institutions

Structure and organization chart

Main team activities

How to make an inventory of models?

COVID19

Impact of COVID19 on credit risk

Impact of COVID19 on model risk

Main flaws in credit risk models

Generation of PostCOVID19 credit risk models


Case study 1: European bank model risk

Case study 2: model risk in credit risk models
MODEL RISK IMPLEMENTATION
Module 2: MODEL RISK PROCESS

Model validation process

Definition of the objective and methodology of the model

Review of model memories

validation plan

Validation conclusions

Model risk subrisks:

Model risk in the data

Model risk in the methodology

Model risk in implementation

Risk in model results


Model governance

Controls at each subrisk level

Analysis and validation of model documentation

Purpose of the model

Data

Design and methodology

Specification and estimate

Evidence

Implementation

monitoring

operational controls

reporting

Control panel

Solutions and technology necessary for model risk management

Case study 3: Model risk management for credit risk models, validation and documentation review
EU AND US MODEL RISK REGULATION
Module 3: Model Risk Management Directive SR 117 in USA

Introduction

Scope and purpose

Model Risk Management

Model development, implementation and use

Model validation

Concept strength assessment and development testing

Permanent monitoring, verification of processes and Benchmarking

Analysis of the results, including Backtesting

Governance, Policies and Controls

Politics and procedures

Roles and responsibilities

Internal audit

model inventory
Module 4: Guide for the Targeted Review of Internal Models (TRIM) EU

Scope and objectives of the guide

General principles of internal models Rollout and PPU

governance

Internal audit

internal validation

Model Usage

Management of model changes

Data quality

Third parties Participation

Credit risk

Scope of the credit risk guide

Data requirements


Probability of default (PD)

Structure of PD models

main drivers

Pool distribution

Rating Philosophy

Calculation of the default rate

Calculation of longterm mean PD


Loss Given Default (LGD)

Credit conversion factor (CCF)

Modelrelated conservatism margin

Review of estimates
MODEL RISK
Module 5: Validation of Models in practice

Lessons learned from the financial crisis on validation

Validation Framework

Validation Definition

Validation principles

Roles and responsibilities

Scope and frequency

Validation Process

Internal Governance

Validation of IRB Models

Qualitative Validation

model design

data quality

Use Test


Quantitative Validation

backtesting

Discriminating Power

Stability Tests


Technological infrastructure

Required documentation

Internal validation department and team

Audit department and team

artificial intelligence

Autonomous validation, reconstruction and recalibration

Model Validation using Machine Learning

Artificial intelligence for model risk

Artificial intelligence to validate models

Module 6: Model Risk Management

Governance in model risk

Role of the board and senior management

Role of the risk department

Responsible and creators of the model

Model risk committees

Internal audit

Policy definition


Organization

Three lines of defense

Internal and external communication


Model Life Cycle Phases

ID

Model Inventory

Classification

Levels or "Tiering" according to materiality, sophistication and impact

Planning and development

internal validation

Documentation

pattern approval

Implementation and use of the model

Model monitoring

International best practice risk management model


Model Risk Mitigation
Data quality test
Validation and audit
Benchmarks
Whatif sensitivity tests
Stress testing
backtesting

Model risk appetite

Model risk appetite statement

risk tolerance


Qualitative measurement of model risk

Creation of the Model Risk Scorecard

Definition of scale and ranges

International Scorecard Best Practices


Case study: Scorecard for model risk
MODEL RISK IN CREDIT RATING AND SCORING
Module 7: Model risk in rating and scoring

Dimension and use of materiality

Classification of scoring models by importance within the financial institution

Impact of the model on the entity

model dependency

Model limitations

Model governance

Documentation and review

Implementation

operational controls

Decision tree to assess rating and scoring models

Casuistry in expert Credit Rating models

Casuistry in statistical credit scoring models

Model risk in big data

Risk model by machine learning and black box
Construction of Credit Scoring and model risk analysis
Module 8: Advanced data validation

Data typology

transactional data

Unstructured data embedded in text documents

Social Media Data

data sources

Data review

Target definition

Time horizon of the target variable

Sampling

Random Sampling

Stratified Sampling

Rebalanced Sampling


Exploratory Analysis:

histograms

Q Q Plot

Moment analysis

boxplot


Treatment of Missing values

imputation

Delete

Keep


Advanced Outlier detection and treatment techniques

ZScore

Mahalanobis Distance


Data Standardization

Variable categorization

Equal Interval Binning

Equal Frequency Binning

ChiSquare Test


binary coding

WOE Coding

WOE Definition

Univariate Analysis with Target variable

Variable Selection

Treatment of Continuous Variables

Treatment of Categorical Variables

Fisher Score

gini

Information Value

Pearson Correlation

Cramer Von Misses

Optimization of continuous variables

Optimization of categorical variables


Decision Trees

Segmentation

Expert Decision

Statistics

Decision Trees

K Means Clustering

Finite Mixture Model

Univariate Gaussian Mixture

Bivariate Gaussian Mixture



Exercise 1: Exploratory Analysis in R

Exercise 2: Detection and treatment of Outliers in R

Exercise 3: Missing imputation techniques in R

Exercise 4: Stratified and Random Sampling

Exercise 5: Analysis of the Weight of Evidence in Excel

Exercise 6: Univariate analysis in percentiles in R

Exercise 7: Continuous variable optimal univariate analysis in R

Exercise 8: KS, Gini and IV validation of each variable in R and Excel

Exercise 9: Optimizing categorical variables in R

Exercise 10: Univariate Analysis with decision trees in R

Exercise 11: Segmentation using K means Clustering in R
Module 9: Multivariate models and Machine Learning

Multivariate Models

Logistic regression

Cox Regression

Model Risk


Machine Learning

decision trees

neural networks

SVM

Ensemble Learning

bagging

Boosting

Random Forest


Model Risk in Machine Learning

overfitting

Transparency

failed sampling

important variables


Exercise 12: Logistic Regression, stepwise method in R

Exercise 14: Piecewise Regression in Excel and R

Exercise 15: Decision trees in R

Exercise 16: Support Vector Machine in R

Exercise 17: Neural Networks in R

Exercise 18: Ensemble models in R

Exercise 19: Random Forest in R

Exercise 20: Bagging in R

Exercise 23: Comparison of models of discriminant power between models: Neural Networks, Logistic Regression, Panel Data Logistic Regression and Cox Regression

Exercise 24: Model Risk using Confidence Intervals of Logistic Regression Coefficients
Module 10: Model Risk in the Scorecard

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


Advanced Cut Point Techniques

Cutoff optimization using ROC curves


Model risk by cut point decision

Model risk due to lack of data

Model Risk for not updating or recalibrating

Exercise 21: Construction of Scorecard in Excel

Exercise 22: Optimum cutoff point estimation in Excel and model risk by cutoff point selection

Exercise 23: Confusion matrix to verify Type 1 and Type 2 Error in Excel with and without variables

Exercise 24: Model risk in credit scoring due to not recalibrating on time
Validation of Credit Scoring models
Module 11: Stability tests

Model stability index

Factor stability index

Xisquare test

KS test

Exercise 25: Stability tests of models and factors
Module 12: Validation of traditional and Machine Learning models

Out of Sample and Out of time validation

Checking pvalues in regressions

R squared, MSE, MAD

Waste diagnosis

Goodness of Fit Test

deviation

Bayesian Information Criterion (BIC)

Akaike Information Criterion


Multivariate Multicollinearity

cross validation

Error bootstrapping

Binary case confusion matrix

Multinomial case confusion matrix

Main discriminant power tests:

KS, ROC Curve, Gini Index, Cumulative Accuracy Profile, KullbackLeibler Distance, Pietra Index, 1Ph, Conditional Entropy, Information Value, Kendall Tau, Brier Score, Divergence


confidence intervals

Jackknifing with discriminant power test

Bootstrapping with discriminant power test

Kappa statistic

KFold Cross Validation

Traffic Light Analysis

Exercise 26: Cross validation in R

Exercise 27: Gini Estimation, Information Value, Brier Score, Lift Curve, CAP, ROC, Divergence in Excel

Exercise 28: Bootstrapping of R parameters

Exercise 29: Gini/ROC Bootstrapping in R

Exercise 30: Kappa estimation

Exercise 31: KFold Cross Validation in R

Exercise 32: Out of time traffic light validation (horizon 6 years) of Logistics and Machine Learning models
Automation of the Construction and Calibration of Models with Artificial Intelligence
Module 14: Build Automation and Calibration

What is modeling automation?

that is automated

Automation of machine learning processes

Optimizers and Evaluators

Modeling Automation Workflow Components

Summary

Indicted

Feature engineering

Model generation

Assessment


Hyperparameter optimization

Reconstruction or recalibration of credit scoring

Credit Scoring Modeling

Main milestones

Evaluation and optimization

Possible Issues


PD calibration modeling

Evaluation and optimization

backtesting

Discriminating Power

Stability Tests


Global evaluation of modeling automation

Implementation of modeling automation in banking

Technological requirements

available tools

Benefits and possible ROI estimation

Main Issues

Model Risk

Exercise 33: Automation of modeling and optimization and validation of credit scoring hyperparametry
Explainable Artificial Intelligence
Module 15: Explainable Artificial Intelligence XAI

Interpretability problem

model risk

Regulation of the General Data Protection Regulation GDPR

EBA discussion paper on machine learning for IRB models

1. The challenge of interpreting the results,

2. The challenge of ensuring that management functions adequately understand the models, and

3. The challenge of justifying the results to supervisors


Black Box Models vs. Transparent and Interpretable Algorithms

interpretability tools

Shap, Shapley Additive explanations

Global Explanations

Dependency Plot

Decision Plot

Local Explanations Waterfall Plot


Lime, agnostic explanations of the local interpretable model

Explainer Dashboard

Other advanced tools

Exercise 34: XAI interpretability of credit scoring
MODEL VALIDATION
PD, LGD and EAD validation IRB and IFRS 9
Module 16: Directive on the estimation of PD and LGD IRB and defaulted exposures issued by EBA

European Directive on estimation of PD and LGD, and exposures in default

Why is it advisable to consider it in Latin America?

Parameter variability reduction

Homogenization of the calculation of PD and LGD

Implementation dates in European banks

Data quality

Representativeness of the data for the development of the model and for the calibration of the risk parameters

Human judgment for parameter estimation

Treatment of deficiencies and margin of conservatism (Moc)

PD estimation

Model development

Data requirement

Risk drivers and rating criteria

Treatment of external ratings

Rating philosophy

Pool Treatment

PD Calibration

Data requirement

One Year Default Rate Calculation

Calculation and use of the average observed default rate

Longterm default rate

Calibration of the longterm default rate

LGD estimation

Methodologies for estimating PD

Data requirement

Recoveries from collaterals

Model development

Risk drivers

Collateral Eligibility

Inclusion of collaterals

LGD Calibration

Definition of economic loss and realized loss

Treatment of commissions, interest and other withdrawals after default

Discount rate

Direct and indirect costs

longterm LGD

Long Term LGD Calibration

Estimation of risk parameters for exposures in default

Estimation and calibration of the Expected Loss Best Estimate

Estimation and calibration of LGD indefault

Application of risk parameters

Review of estimates

Accompanying Documents

impact assessment

Identification of the problem

Policy objectives

Baseline scenario

Options considered

Costbenefit analysis
Module 17: PD templates for IRB and IFRS 9 approach

Introduction to Probability of Default

Default definition

Default Triggers

Effective and robust process to detect default

Technical defaults and technical default filters

Indispensable data model

Single Factor Analysis

Multifactor Analysis

Model Selection

Historical PS

Econometric and Machine Learning Models of PD

Risk factors that affect default

macroeconomic

idiosyncratic


PD Logistic Regression

PD COX Regression

PD Loglog Complementary

PD Logistic Regression Data Panel

Machine Learning to estimate PD

PD Calibration

Introduction to Calibration

Anchor Point Estimate

Mapping from Score to PD

Temporal structure of the PD

PS Marginal

PS Forward

Cumulative PD


Techniques for Mapping PD's to temporary structure

Vintages or vintages of PD

Bayesian PD

Expert Judgment

Prior and posterior distribution

Markov Chain Monte Carlo

probit model

Adjustment to the PD Economic Cycle

Introduction of Adjustment to the Economic Cycle

Directives on the economic cycle in the PD

PD Trough The Cycle (PD TTC) Models

Considerations of the Adjustment to the “Scalar Variable” approach cycle

PD on Low Default Portfolios

PD estimation without correlations

PD estimation with correlations

LDP Calibration Using CAP Curves

Bayesian PD estimation for LDP

Default correlation

Correlation of defaults and multiperiod

Neutral Bayesian and Conservative Bayesian

Transition and PD Matrices

Properties of transition matrices

Multiyear transition matrix

discrete time

continuous time

Generating Matrix

Exponential of a matrix


duration method

Cohort method

error management

IFRS 9 PD Modeling

IFRS 9 requirements

Probability Weighted

Forward Looking

Lifetime PD modeling

PD Forecasting Modeling

PD Point in Time Forecasting

Markov chains

Exercise 35: Calibration of PD with COX regression in R

Exercise 36: PD calibration with loglog complementary in R

Exercise 37: PD calibration with logistic model in R

Exercise 38: PD calibration logistic Bayesian regression

Exercise 39: Calibration of PD regression panel logistic data

Exercise 40: Calibration of PD Lasso Regression

Exercise 41: Calibration of PD Bayesian Probit regression in R

Exercise 42: Transition matrices in Excel and R

Exercise 43: Multinomial Regression to estimate PD Lifetime

Exercise 44: Multi stage Markov chains in R
Module 18: Backtesting PD IRB and IFRS9

Validation of the PD in IRB PIT and TTC

Validation of PD Lifetime and PD12m in IFRS 9

Backtesting PS

PD Calibration Validation

Hosmer Lameshow test

normal test

Binomial Test

Spiegelhalter test

Redelmeier Test

Traffic Light Approach

Traffic Light Analysis and PD Dashboard

PS Stability Test

Forecasting PD vs Real PD in time

Validation with Monte Carlo simulation

Exercise 45: Backtesting PD IRB and PD IFRS 9

Exercise 46: Forecasting Estimated PD and Actual PD in Excel

Exercise 47: Validation using Monte Carlo Simulation
Module 19: LGD Models for IRB and IFRS 9 Approach

BASEL III and EBA LGD

LGD estimation and calibration in practice

Default LGD estimation

LGD Econometric and Machine Learning Models

Advantages and disadvantages of LGD Predictive Models

Forward Looking models incorporating Macroeconomic variables

Parametric and nonparametric models and transformation regressions

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

neural networks

SVM

Beta regression

Inflated beta regression

Fractional Response Regression

LGD for IFRS 9

Comparison of regulatory LGD vs. IFRS 9

LGD adjustments

Selection of Interest Rates

Allocation of Costs

floors

Treatment of collateral over time

Marginal LGD

PIT LGD

Loss Lifetime Concept

exposure treatment

Exercise 48: Logistic and linear regression LGD in R

Exercise 49: Neural Networks and SVM LGD

Exercise 50: Generalized Additived Model LGD in R

Exercise 51: Beta Regression Model LGD in R

Exercise 52: Longterm LGD calibration

Exercise 53: Inflated Beta Regression in SAS

Exercise 54: Comparison of the performance of the models using Calibration and precision tests
Module 20: LGD Backtesting

LGD backtesting in IRB

LGD Backtesting in IFRS 9

Accuracy ratio

absolute accuracy indicator

Confidence Intervals

transition analysis

RR Analysis using Triangles

Advanced LGD Backtesting with a vintage approach

Backtesting for econometric models

ROC, Gini and KS curve

Exercise 55: Comparison of the performance of the models using Calibration and precision tests.
Module 21: EAD Models

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

CCF Econometric and Machine Learning Models

Linear regression

Logistic regression

Generalized Additive Model

neural networks

SVM

Beta regression

Inflated beta regression

Fractional Response Regression


EAD for IFRS 9

Comparison of regulatory EAD vs. IFRS 9

Adjustments in the EAD

Interest Accrual

CCF PIT Estimate

Modeling of available lifetime


Exercise 56: Estimation and adjustments for EAD IFRS 9 in excel and R

Exercise 57: Neural Networks and SVM CCF

Exercise 58: Generalized Additived Model CCF in R

Exercise 59: Beta Regression Model CCF in R
Module 22: EAD Backtesting

EAD validation

CCF validation

Backtesting EAD and CCF IRB

Backtesting of the EAD and CCF IFRS 9

r squared

Pearson coefficient

Spearman correlation

Validation using ROC, KS and Gini

Exercise 60: Comparison of the performance of EAD models
IFRS 9 Expected Credit Loss (ECL) Validation
Module 23: ECL Validation

Initial validation

Periodic validation

Monitoring

Main milestones of qualitative validation

Data quality

Default Definition

Relevance of the qualification process

Override Analysis

environmental dynamics

user test


Main milestones of quantitative validation

Samples used for validation purposes

Discriminating Power

population stability

Characteristic Stability

concentration analysis

Staging analysis

Parameter Calibration

ECL backtesting


Principle 5 – Validation of the ECL model in Basel III

Governance

Model inputs

model design

Model output/performance


Validation metrics

Bayesian/Akaike/Schwartz/Deviance information criteria

Receiver operating characteristic (ROC) curve or AUROC statistic

Lorenz curve, Gini coefficient, KolmogorovSmirnov test

Ttests, Ftests, Wald test, log likelihood test

RMSE, MAPE, MAD

Rsquared, Adjusted Rsquared

Outofsample testing

benchmarking

Population stability index (PSI)


Statistical problems

Sampling bias

Survivorship bias

Disproportionately high model weightsT

Autoregressive and lagged terms that do not capture macroeconomic effects

spurious correlation

Smoothing methods that alter data integrity

simple linear models in nonlinear relationships

Validation of Regulatory and Economic Capital for Credit Risk
Module 24: Economic Capital Models

Definition and Objective

Temporal horizon

Default Correlation

asset mapping

unexpected loss

Regulatory capital models

ASRF Economic Capital Models

Business Models

Multifactorial Models

Economic Capital for retail using Charge off

Dependency modeling using copulas

VaR estimation and Expected Shortfall

Economic capital management

Exercise 61: Default correlation matrix in SAS

Exercise 62: Correlation of default: consumer portfolios in R

Exercise 63: Correlation of assets with EMV and observable data in R

Exercise 64: Creditrisk + in R

Exercise 65: Onefactor model in Excel and R

Exercise 66: Multifactorial Model in Excel

Exercise 67: Tstudent in Excel in Excel

Exercise 68: Copulas in R
Module 25: Economic capital validation

Regulatory Capital Models

Economic Capital Models

Validation of Credit Portfolio Models

Model Design

Model Output

Processes, data and test of use


Loss aggregation validation

Validation of Basel models

Testing distributions using Berkowitz test

Credit Loss Distribution

Simulation of the critical chisquare value

Berkowitz test in subportfolios

power assessment

Scope and limits of the test

Model risk in economic capital due to uncertainty

Exercise 69: Implementation of the Berkowitz test in economic and regulatory capital models

Exercise 70: Simulation of losses and model risk in regulatory and economic capital
Validation of Stress Testing Credit Risk
Module 26: Forecasting Models

Data processing

NonStationary Series

DickeyFuller test

Cointegration Tests


Econometric Models

ARIMA models

VAR Autoregressive Vector Models

ARCH models

GARCH models

Linear regression


Machine Learning Models

Supported Vector Machine

neural network


Exercise 71: Nonstationary Series and Cointegration Tests in R and Python

Exercise 72: macroeconomic variables with VAR in R

Exercise 73: Garch modeling market variables R

Exercise 74: Machine Learning SPV and NN modeling in Python
Module 27: Validation of econometric models

Review of assumptions of econometric models

Review of the coefficients and standard errors of the models

Model reliability measures

Error management

not normal

heteroscedasticity

Outliers

autocorrelation

multicollinearity

Exercise 75: Measuring Logistic Regression Collinearity
Module 28: Stress Testing Consumer Credit Risk

Temporal horizon

Multiperiod approach

Impact on P&L, RWA and Capital

Macroeconomic Stress Scenarios in consumption

Stress Testing of PD, LGD and EAD

Stress Testing of the Transition Matrix

Chage Off Stress Testing

Losses from new impaired assets

Losses on old impaired assets

Exercise 76: Stress Testing PD in Excel and R multifactorial model

Exercise 77: Stress Testing PD Multiyear Approach

Exercise 78: Stress test of PD and Autoregressive Vectors

Exercise 79: Net Charge Off Stress Test

Exercise 80: LGD Stress Test
Module 29: Stress Testing Credit Risk Portfolios Corporate

Stress Test Methodology for corporate portfolios

Creditmetrics and Transition Matrices

Credit Index and PD

PD simulation and transition matrices

Exercise 81: Corporate portfolio stress test
Module 30: Stress Testing of ECL IFRS 9

Stress testing IFRS 9 and COVID19

Pandemic scenarios applied to the ECL calculation

Stress Testing of IFRS 9 parameters

EBA Stress Testing 2021

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 82: Stress Testing of the ECL using matrices and time series R and Excel
Module 31: Validation of Stress Testing

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 83: Validation tests of stress testing VAR vs MARS