In this paper, we follow the EBA documents regarding the guidelines that apply from 1January 2021 and propose a framework to quantify, document and monitor the impact of uncertainties relevant to the IRB PD, LGD and CCF estimation. Following the categorization of deficiency types, we derived a general form methodology of appropriate adjustment, best estimate and final MoC, that is intuitive, flexible and transparent to the institution.
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 states, that the requirement of Margin of Conservatism (MoC) also applies for the CCF estimation.
Table of Contents
1 Category and Triggers of Identified Deficiencies
2 Design Concepts: an 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
6 Mathematical Definition of LGD Related Parameters
7 Notation Update and Application for LGD Estimation
Part III. CCF Estimation
8 Math Expression of CCF
9 Notation Update and Application for CCF Estimation
Part IV. Margin of Conservatism Framework
10 Genral Form Results for PD, LGD and CCF Estimation
11 Final Margin of Conservatism
12 Conclusions
Objectives and Topics
This paper aims to develop and present a consistent, transparent framework for quantifying uncertainties in IRB risk parameter estimation (PD, LGD, CCF) in compliance with EBA guidelines, providing a clear method for institutions to identify, categorize, and document deficiencies to determine the required Margin of Conservatism (MoC).
- Categorization of data, methodological, and general estimation deficiencies.
- Mathematical derivation of "Appropriate Adjustment" and "Best Estimate" for risk parameters.
- Quantification of General Estimation Error (Category C) via rank ordering and calibration errors.
- Framework integration with existing bank monitoring and review processes.
Excerpt from the Book
Appropriate Adjustment
The Appropriate Adjustment is an important term for the quantification of the MoC. Paragraph 38 of the Guideline states that:
38. In order to overcome biases in risk parameter estimates stemming from the identified deficiencies referred to in paragraphs 36 and 37, institutions should apply adequate methodologies to correct the identified deficiencies to the extent possible. The impact of these methodologies on the risk parameter (‘appropriate adjustment’), which should result in a more accurate estimate of the risk parameter (‘best estimate’), represents either an increase or a decrease in the value of the risk parameter. Institutions should ensure and provide evidence that the application of an appropriate adjustment results in a best estimate.
Highlighted that, an adequate methodology is required to correct the identified deficiency; post-correction, the impact of this methodology on the risk parameter, is defined as ‘appropriate adjustment’. This is an important concept because it is observed that even professionals in the industry could have misunderstood the methodology of correction as the appropriate adjustment itself.
Summary of Chapters
1 Category and Triggers of Identified Deficiencies: Details the regulatory requirements for identifying and classifying deficiencies into Category A and Category B sources of uncertainty.
2 Design Concepts: an post-publication update: Outlines the fundamental design principles of the framework, focusing on the distinction between deficiency correction and risk parameter adjustment.
3 Math Expression of PD Related Parameters: Defines the mathematical calculation of one-year and long-run default rates according to EBA guidelines.
4 Appropriate Adjustment and Best Estimate for Default Rates: Introduces the methodology for adjusting default rates based on Category A and B triggers, providing generalized formulas for single and multiple categories.
5 Category C Deficiencies: the General Estimation Error: Discusses the quantification of general estimation errors, specifically addressing rank ordering discordance and calibration dispersion.
6 Mathematical Definition of LGD Related Parameters: Provides the calculation basis for realized LGD and long-run average LGD.
7 Notation Update and Application for LGD Estimation: Adapts the general MoC framework notation and formulas to specifically address LGD parameter estimation.
8 Math Expression of CCF: Outlines the regulatory context and mathematical foundations for realized CCF and long-run CCF estimation.
9 Notation Update and Application for CCF Estimation: Applies the developed MoC quantification methodology to Credit Conversion Factor estimation.
10 Genral Form Results for PD, LGD and CCF Estimation: Summarizes the generalized mathematical formulas for appropriate adjustment, best estimation, and general error quantification across all risk parameters.
11 Final Margin of Conservatism: Derives the final MoC values by combining adjustments for Categories A, B, and C.
12 Conclusions: Reviews the framework's effectiveness as a consistent, intuitive, and compliant tool for institutions to monitor and quantify uncertainty.
Keywords
Advanced IRB, Long-run Default Rate, Long-run LGD, Central Default Tendency, Risk Weighted Assets, Margin of Conservatism, Probability of Default, Loss Given Default, Credit Conversion Factor, Exposure at Default, Appropriate Adjustment, Category A Deficiency, Category B Deficiency, General Estimation Error, Calibration
Frequently Asked Questions
What is the primary objective of this publication?
The publication aims to establish a consistent, transparent, and modular framework for banks to quantify and document the Margin of Conservatism (MoC) for IRB risk parameters (PD, LGD, CCF) in adherence to EBA guidelines.
What are the three categories of deficiencies addressed by the framework?
The framework addresses Category A (data and methodological deficiencies), Category B (changes to underwriting standards, risk appetite, etc.), and Category C (general estimation error).
What is the core research question or problem solved by this framework?
The work addresses the difficulty of accurately quantifying uncertainties and biases in risk parameter estimates, providing a clear method to link identified deficiencies to a mathematical "Appropriate Adjustment" and final MoC.
What scientific or mathematical methods are primarily employed?
The framework utilizes ratio-based counting, weighted averages, and dispersion measures inspired by statistical principles such as Kendall’s tau correlation coefficient for rank ordering error.
How is the "Appropriate Adjustment" defined in this context?
It is defined as the quantifiable impact of a correction methodology on a risk parameter, distinct from the correction methodology itself.
Which keywords best characterize this work?
Key terms include IRB, Margin of Conservatism (MoC), Appropriate Adjustment, PD, LGD, CCF, and EBA Guidelines.
How does the framework distinguish between deficiency correction and the MoC?
The framework clarifies that MoC should not replace the need for a remediation plan to address the root causes of errors, serving as a buffer rather than a fix for model failures.
What is the significance of the "Category C" error in this framework?
Category C represents the general estimation error, which the framework decomposes into rank ordering error and calibration dispersion, ensuring that these independent uncertainties are measured consistently.
Is the framework dependent on specific statistical models?
No, the proposed framework is designed to work well without relying on specific model or distributional assumptions, allowing for flexibility and independence from re-estimating models during MoC updates.
- Arbeit zitieren
- Yang Liu (Autor:in), 2018, Margin of Conservatism Framework for IRB PD, LGD and CCF, München, GRIN Verlag, https://www.grin.com/document/491426
