The EBA Guidelines on PD and LGD estimation is due to apply from 1 January 2021, in which the banks are expected to have a framework in place as part of the risk rating and reporting process to adjust and correct the uncertainties identified from deficiencies in data, system and methodology. The ECB Guide on the TRIM in the meantime state that the requirement of Margin of Conservatism (MoC) also applies for the CCF estimation. In this paper, we develop and present a consistent framework to quantify the identified uncertainties for the purpose of IRB risk parameter estimation.
Table of Contents
- 1. Category and Triggers of Identified Deficiencies
- 2. Design Concepts: a post-publication update
- Part I. PD Estimation
- 3. Math Expression of PD Related Parameters
- 4. Appropriate Adjustment and Best Estimate for Default Rates
- 4.1. Single Category
- 4.2. Multiple Categories
- 4.3. Generalized Form at Trigger Level Estimation
- 5. Category C Deficiencies: the General Estimation Error
- 5.1. Calibration Target and Output
- 5.2. Error in Rank Ordering Estimation
- 5.3. Error in Calibration
- Part II. LGD Estimation
- Part III. CCF Estimation
- Part IV. Margin of Conservatism Framework
- Appendix:
- A. Fully Commented Numerical Example for PD
- A.1. Double counting, inclusion, and exclusion of deficiency affected records
- A.2. Required input
- A.3. Calculated results
- A.3.1. Category A
- A.3.2. Category B
- A.3.3. MoC A and B adjusted PD
- A.4. Closing remarks
- B. Conceptual Example for LGD and CCF
- B.1. Required input
- B.2. Quantification steps:
- A. Fully Commented Numerical Example for PD
Objective & Thematic Focus
This paper aims to develop and present a consistent, transparent framework for quantifying identified uncertainties in the estimation of IRB (Internal Ratings-Based) risk parameters, specifically Probability of Default (PD), Loss Given Default (LGD), and Credit Conversion Factor (CCF), in accordance with EBA Guidelines.
- Framework for Margin of Conservatism (MoC)
- IRB Risk Parameter Estimation (PD, LGD, CCF)
- Quantification of Data, System, and Methodological Deficiencies
- Regulatory Compliance with EBA and ECB Guidelines
- Modular and Transparent Approach to Uncertainty Management
- Numerical and Conceptual Examples for Practical Application
Excerpt from the Book
2. Design Concepts: a post-publication update
July 12, 2019: In this chapter, we briefly outline some design concepts and key properties of the presented framework. The purpose of this chapter, as an post publication update, is to aid understanding of the framework and address some commonly misunderstood concepts. There is no change in the quantification methodology between this update and the previously published version.
Simple Counting and Observed Ratio
The risk parameters in scope are defined in the form of ratios of counting results. For example, number of defaults vs. total number of observations, currency units of value lost vs. units of value at risk, etc.
Often, such data are stored in different sources, or even groups of different sources each with a different level of aggregation. The counting of units consisting of the numerator and denominator, respectively, indicate the data deficiency in a transparent manner and so is the quantification of the joint impact on the risk parameter.
In general, impact analysis of future market, economy or strategic changes on the risk parameter, is challenging because different level of influence an institution has in the numerator and denominator. For example in case of loss rate, while the lending institution have more control over the level of total exposure, it is less likely to have such influence on the amount of loss. Therefore, expert judgmental opinion on individual elements of the fraction should be considered alongside numerical evidence for quantification of uncertainties.
The presented framework suggest and require identifying and counting of deficiencies by combing through individual data records. The follow up quantification process starts with each
Chapter Summaries
1. Category and Triggers of Identified Deficiencies: This chapter outlines the types of deficiencies related to risk parameter estimation, categorizing them as data/methodological issues (Category A) or changes in underwriting standards/risk appetite (Category B), based on EBA Guidelines.
2. Design Concepts: a post-publication update: This section provides an update on the framework's design concepts, clarifying the definition of risk parameters as ratios and emphasizing the identification and quantification of deficiencies at the individual data record level.
Part I. PD Estimation: Focuses on the Probability of Default (PD) estimation, detailing its mathematical expression, appropriate adjustments for default rates, and general estimation errors across different categories of deficiencies.
Part II. LGD Estimation: Addresses the Loss Given Default (LGD) estimation, including its mathematical definition and how the framework's notation and application apply to LGD-related parameters and deficiencies.
Part III. CCF Estimation: Explores the Credit Conversion Factor (CCF) estimation, presenting its mathematical formulation and detailing the application of the framework's adjustment and notation updates for CCF.
Part IV. Margin of Conservatism Framework: Develops the comprehensive Margin of Conservatism (MoC) framework, presenting generalized formulas for PD, LGD, and CCF estimation and detailing the final calculation of the MoC across all deficiency categories.
12. Conclusions: Summarizes the paper's contribution in providing a consistent, intuitive, and transparent framework for quantifying uncertainties in IRB PD, LGD, and CCF estimation, compliant with EBA Guidelines.
Appendix: Provides detailed numerical examples for PD, and conceptual examples for LGD and CCF, to demonstrate the framework's practical application and compliance with regulatory requirements.
Keywords
Advanced IRB, Margin of Conservatism (MoC), Probability of Default (PD), Loss Given Default (LGD), Credit Conversion Factor (CCF), Risk Parameter Estimation, EBA Guidelines, Regulatory Compliance, Uncertainty Quantification, Data Deficiencies, Model Calibration, Long-run Default Rate
Frequently Asked Questions
What is this paper fundamentally about?
This paper presents a consistent framework for quantifying uncertainties in the estimation of IRB risk parameters (PD, LGD, CCF) to ensure compliance with EBA Guidelines and address data, system, and methodological deficiencies.
What are the central thematic areas?
The central thematic areas include advanced IRB modelling, estimation of key risk parameters (PD, LGD, CCF), quantification of Margin of Conservatism (MoC), identification and categorization of deficiencies, and regulatory compliance in financial risk management.
What is the primary goal or research question?
The primary goal is to develop a consistent framework that quantifies identified uncertainties for IRB risk parameter estimation, addressing how banks should adjust for and correct uncertainties stemming from deficiencies in data, systems, and methodologies.
What scientific method is used?
The paper develops and presents a framework based on EBA and ECB guidelines, proposing a structured, mathematical approach to quantify uncertainties and their impact on risk parameters, complemented by numerical examples.
What is covered in the main body?
The main body covers the categorization and triggers of deficiencies, design concepts of the framework, detailed mathematical expressions and adjustments for PD, LGD, and CCF estimation, and the overall Margin of Conservatism framework, including final MoC calculation.
What key terms characterize the work?
Key terms characterizing the work include Advanced IRB, Margin of Conservatism (MoC), Probability of Default (PD), Loss Given Default (LGD), Credit Conversion Factor (CCF), Risk Parameter Estimation, and EBA Guidelines.
What are the three main categories of deficiencies identified in the framework?
The framework identifies three categories of deficiencies: Category A (data and methodological deficiencies), Category B (relevant changes to underwriting standards, risk appetite, collection, and recovery policies), and Category C (general estimation error).
How does the framework address "appropriate adjustment" versus "methodology of correction"?
The framework distinguishes "methodology of correction" (e.g., replacing missing data) from "appropriate adjustment," which is defined as the *impact* of that methodology on the risk parameter, aiming for a more accurate "best estimate."
What is the purpose of the post-publication update in Chapter 2?
The post-publication update in Chapter 2 aims to clarify some design concepts and key properties of the presented framework, aiding understanding and addressing commonly misunderstood concepts without changing the underlying quantification methodology.
How does the framework ensure avoidance of double counting in deficiency assessment?
The modular design of the quantification process, combined with the independence of trigger assessment and the explicit identification and counting of deficiencies at the individual data record level, helps to avoid double counting and ensures transparency.
- Arbeit zitieren
- Yang Liu (Autor:in), 2019, Margin of Conservatism Framework for IRB PD, LGD and CCF, München, GRIN Verlag, https://www.grin.com/document/923170
