IFRS9 Expected Credit Loss Model Development and Validation

سرفصل های دوره

Learn how to model and validate expected credit losses for IFRS9 using R Programming

1 - Introduction

1 -Introduction

2 - Theoretical Framework

1 -IFRS 9 vs Basel III

2 -Loan Loss Distribution

3 -Objectives of IFRS9

4 -Improvement in Accounting Standards IAS 39 to IFRS9

5 -IFRS9 Staging Concept

6 -Significant Increase in Credit Risk (SICR) Assessment

7 -IFRS9 ECL Classification

8 -Expected Credit Loss (ECL) Components

3 - Probability of Default (PD)

1 -Default Definition

2 -Exploring the Dataset

2 -codes.pdf

2 -oneyearpd.xlsx

3 -Data Preparation

4 -Default Flag Creation

5 -Creating Training and Test Dataframes

6 -Information Value Assessment

7 -Binning and Weight of Evidence (WoE)

8 -WoE Variables

9 -Correlation Check

10 -Pairs Correlation Analysis

11 -Fitting Logistic Regression Model (One Year PD)

12 -Summary Statistics -Logistic Regression output

13 -Calibration

14 -Quantitative interpretation of Score Formula

15 -Model Validation Discriminatory Power

16 -One-Year PD vs Lifetime PD

17 -Censoring Mechanics- Multi Period Analysis (Lifetime PD)

18 -Lifetime PD.xlsx

18 -Lifetime PD ML (Random Forest) Modeling

4 - Loss Given Default (LGD)

1 -LGD Theoretical Understanding

2 -LGD data elements

2 -data lgd year.xlsx

3 -LGD Database Creation

4 -Probability of Cure (PoC) Significance in LGD modeling

5 -Exploring the dataframe and performing exploratory data analysis

6 -Data Partitioning and Information Value Analysis

7 -LGD Regression Methods Tobit Regression

8 -LGD Regression Methods Beta Regression

5 - Exposure at Default (EAD)

1 -EAD Theoretical Understanding

2 -EADF Modeling

2 -ead.xlsx

3 -CCF Modeling

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تولید کننده: Udemy