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.
Inhaltsverzeichnis (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
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
The objective of this paper is to develop a consistent framework for quantifying uncertainties in the estimation of IRB risk parameters (Probability of Default (PD), Loss Given Default (LGD), and Credit Conversion Factor (CCF)) in accordance with EBA guidelines. The framework addresses deficiencies in data, systems, and methodologies, incorporating a Margin of Conservatism (MoC) to account for these uncertainties.
- Quantification of uncertainties in IRB risk parameter estimation.
- Development of a consistent framework for addressing data and methodological deficiencies.
- Application of a Margin of Conservatism (MoC) to account for identified uncertainties.
- Categorization of deficiencies and their impact on risk parameter estimations.
- Appropriate adjustment methodologies for correcting identified deficiencies.
Zusammenfassung der Kapitel (Chapter Summaries)
1 Category and Triggers of Identified Deficiencies: This chapter outlines the EBA Guidelines' requirements for identifying and categorizing deficiencies in risk parameter estimation. It details the three categories of deficiencies (A, B, and C) and provides a comprehensive list of potential sources of uncertainty falling under each category, including missing data, inaccurate information, methodological flaws, and changes in underwriting standards or market conditions. The chapter emphasizes the importance of a thorough review of data and processes to identify these deficiencies as a prerequisite for accurate risk assessment. This forms the foundation for the entire framework, setting the stage for the quantification of uncertainty and the application of a margin of conservatism.
2 Design Concepts: an post-publication update: This chapter provides an overview of the design concepts and key properties of the framework. It clarifies the definition of risk parameters as ratios and explains the challenges of assessing the impact of future market changes on these ratios due to differing levels of institutional control over the numerator and denominator. The chapter highlights the importance of expert judgment alongside numerical evidence and emphasizes that the framework necessitates the identification and counting of deficiencies by examining individual data records. This chapter serves as a crucial explanation of the core principles, addressing potential misunderstandings and highlighting the need for meticulous data analysis.
Part I. PD Estimation: This section focuses on the estimation of Probability of Default (PD). It introduces mathematical expressions for PD-related parameters and details methods for appropriate adjustments and the determination of best estimates. The section addresses how to handle uncertainties related to single and multiple categories of deficiencies, highlighting the significance of correctly addressing identified biases to gain a precise and reliable picture of the probability of defaults, essential for accurate risk assessments.
Part II. LGD Estimation: This section elaborates on the estimation of Loss Given Default (LGD). It provides a mathematical definition of LGD-related parameters and discusses the updates and applications of these definitions within the proposed framework. The section's detailed approach assures the thorough and accurate estimation of LGD, a crucial element in overall credit risk management.
Part III. CCF Estimation: This section deals with the estimation of Credit Conversion Factor (CCF). It presents the mathematical expression of CCF and details its application within the framework. The section ensures the accurate estimation of CCF which is another important aspect of a comprehensive approach to credit risk.
Part IV. Margin of Conservatism Framework: This section outlines the comprehensive application of the developed framework across the PD, LGD, and CCF estimations. It describes how the individual estimations are integrated and how the final margin of conservatism is determined. This section demonstrates the consolidation of findings into a coherent assessment that considers the collective impact of uncertainties in various estimations, producing a robust risk profile.
Schlüsselwörter (Keywords)
Advanced IRB, Long-run Default Rate, Long-run LGD, Central Default Tendency, Risk Weighted Assets (RWA), Margin of Conservatism (MoC), Probability of Default (PD), Loss Given Default (LGD), Credit Conversion Factor (CCF), Exposure at Default (EAD), EBA Guidelines, Data Deficiencies, Methodological Deficiencies, Appropriate Adjustment, Best Estimate, Uncertainty Quantification.
FAQ: A Comprehensive Framework for Quantifying Uncertainties in IRB Risk Parameter Estimation
What is the main objective of this paper?
The paper aims to create a consistent framework for measuring uncertainties when estimating IRB risk parameters (Probability of Default (PD), Loss Given Default (LGD), and Credit Conversion Factor (CCF)) according to EBA guidelines. This includes addressing data, system, and methodological shortcomings and using a Margin of Conservatism (MoC) to account for these uncertainties.
What are the key themes explored in this paper?
Key themes include quantifying uncertainties in IRB risk parameter estimation, developing a consistent framework for dealing with data and methodological deficiencies, applying a Margin of Conservatism (MoC), categorizing deficiencies and their impact on risk parameter estimations, and using appropriate adjustment methods to correct identified deficiencies.
What are the three categories of deficiencies identified in the paper?
The paper categorizes deficiencies into three types: A, B, and C. Each category encompasses various sources of uncertainty, such as missing data, inaccurate information, methodological flaws, and changes in underwriting standards or market conditions. The specific sources within each category are detailed in the paper.
How does the paper address the estimation of Probability of Default (PD)?
Part I of the paper focuses on PD estimation. It provides mathematical expressions for PD-related parameters and outlines methods for appropriate adjustments and best estimate determination. It addresses uncertainties related to single and multiple deficiency categories, emphasizing the importance of correcting identified biases for accurate PD assessment.
How does the paper address the estimation of Loss Given Default (LGD)?
Part II covers LGD estimation, providing mathematical definitions of LGD-related parameters and discussing their updates and applications within the framework. The detailed approach ensures thorough and accurate LGD estimation, a crucial aspect of credit risk management.
How does the paper address the estimation of Credit Conversion Factor (CCF)?
Part III deals with CCF estimation, presenting its mathematical expression and its application within the framework. This ensures the accurate estimation of CCF, another key component of comprehensive credit risk assessment.
How is the Margin of Conservatism (MoC) incorporated into the framework?
Part IV describes the comprehensive application of the framework to PD, LGD, and CCF estimations. It explains how individual estimations are integrated and how the final MoC is determined. This section consolidates the findings into a coherent assessment, considering the collective impact of uncertainties for a robust risk profile.
What are the key design concepts of the framework?
Chapter 2 details the framework's design concepts, including defining risk parameters as ratios and addressing the challenges of assessing the impact of future market changes on these ratios. It highlights the importance of expert judgment alongside numerical evidence and the need for meticulous data analysis at the individual data record level.
What keywords are associated with this framework?
Keywords include: Advanced IRB, Long-run Default Rate, Long-run LGD, Central Default Tendency, Risk Weighted Assets (RWA), Margin of Conservatism (MoC), Probability of Default (PD), Loss Given Default (LGD), Credit Conversion Factor (CCF), Exposure at Default (EAD), EBA Guidelines, Data Deficiencies, Methodological Deficiencies, Appropriate Adjustment, Best Estimate, Uncertainty Quantification.
What is the structure of the document?
The document includes a table of contents, objectives and key themes, chapter summaries, and keywords. Each chapter is summarized, providing an overview of its contents and contribution to the overall framework.
- Citar trabajo
- Yang Liu (Autor), 2018, Margin of Conservatism Framework for IRB PD, LGD and CCF, Múnich, GRIN Verlag, https://www.grin.com/document/491426
