2016年-EBA欧洲银行管理局_QIS_report_on_default_definition_October_2016_73页_3mb

Summary of the Data Collection Exercise on the Proposed Regulatory Changes for a Common EU Approach to the Definition of Default

Core Content

This document summarizes the findings of a qualitative and quantitative impact study (QIS) conducted by the European Banking Authority (EBA) to assess the potential impact of proposed regulatory changes on the definition of default. The study is based on data collected from 72 institutions, with 8 participating only in the qualitative analysis and 64 in both qualitative and quantitative analysis. The findings aim to inform the final calibration of the regulatory products, specifically the Consultation Paper on the Guidelines (CP-GL) and the Consultation Paper on the Regulatory Technical Standards (RTS) (CP-RTS).

The main focus of the QIS is to understand the current practices of institutions in defining default and to evaluate the impact of harmonizing these definitions. The results highlight significant variability in how institutions currently apply the definition of default, particularly in relation to materiality thresholds, the use of probation periods, and the interpretation of distressed restructuring and sale of credit obligations as indicators of unlikelihood to pay.

Main Points and Key Information

1. Qualitative Analysis of Current Practices

• Default Definition Differences:

  • Over half of the institutions apply a unique default definition across the group.
  • 39% apply different definitions for different types of exposure (retail vs. non-retail).
  • 19% apply different definitions in different legal entities.
  • 11% apply different definitions based on geographical location.

• Number of Default Definitions:

  • 54% of institutions use one definition.
  • 24% use two definitions.
  • 10% use between three and six definitions.
  • 8% use more than six definitions.
  • 4% do not specify the number of definitions in use.

• Reasons for Differences:

  • 52% of differences stem from materiality thresholds.
  • 15% from the number or counting of DPD (Days Past Due).
  • 3% from additional indications of unlikelihood to pay.
  • 3% from criteria for returning to non-defaulted status.
  • The 'other' category includes multiple factors such as exposure type, legal entity, jurisdiction, and treatment of specific credit risk adjustments (SCRA).

2. Application of Default Definition for Retail Exposures

• Choice of Application Level:

  • Institutions can apply the default definition at the facility level or the obligor level.
  • 45% of institutions apply the definition at the obligor level.
  • 30% apply it at the facility level.
  • 24% apply it based on different jurisdictions, legal entities, or exposure types.

• Pulling Effect:

  • A rule where a significant part of exposures to an obligor being in default leads to the recognition of default on the remaining exposures.
  • 75% of institutions that apply the definition at the facility level do not use the pulling effect.
  • 15% use it partially.
  • 12% use it fully (5% with a 0% threshold, 7% with a 20% threshold).

• Contagion Rules:

  • Contagion rules apply when a default of one obligor affects others.
  • 30% of institutions do not have a contagion rule (either no contagion or case-by-case assessment).
  • 18% follow rules in line with the proposed ones in the CP-GL.
  • 8% base rules on the nature or type of joint obligation.
  • 4% apply rules from parent to subsidiary.
  • 37% have other specific rules.

3. Definitions of Technical Default

• Technical Default is not formally defined in Regulation (EU) No 575/2013, but is used to describe situations that do not meet the criteria for default.
• Usage:
  • 43% of institutions do not have a formal definition of technical default.
  • 18% of the sample do not use technical default definitions at all.

4. Materiality Thresholds

• Non-Retail Exposures:

  • Materiality thresholds vary significantly.
  • 54% of institutions use a fixed materiality threshold.
  • 24% use a relative materiality threshold.
  • 22% use a combination of both.

• Retail Exposures:

  • The application of materiality thresholds is more varied.
  • 54% of institutions apply materiality thresholds to retail exposures.
  • 24% use a fixed threshold, 22% use a relative threshold.

5. Indications of Unlikelihood to Pay

• Indicators:

  • Institutions use various indicators, including SCRA, impaired exposures, and sale of credit obligations.
  • 37% of institutions use alternative indicators other than those prescribed in Article 178(3) of Regulation (EU) No 575/2013.

• Automatic Trigger:

  • 46% of institutions do not automatically trigger default with these alternative indicators.
  • 54% use them as part of a case-by-case assessment.

6. Probation Period and Return to Non-Defaulted Status

• Probation Period:

  • Half of the institutions apply probation periods at least partially.
  • The length and starting point of probation periods vary significantly.

• Return to Non-Defaulted Status:

  • Criteria for returning to non-defaulted status vary.
  • 30% of institutions apply probation periods based on DPD.
  • 20% apply them based on distressed restructuring.
  • 50% apply them based on other criteria.

7. Impact of Policy Options

• Quantitative Impact:

  • The introduction of a harmonized default definition is expected to lead to a modest increase in capital charges, particularly for IRB institutions.
  • The average CET1 ratio is expected to decrease by around 20 basis points for IRB institutions.
  • For SA institutions, the impact is limited.

• Variability Drivers:

  • The main sources of variability in default definitions are:
    1. Materiality thresholds.
    2. Use of probation periods.
    3. Different interpretations of distressed restructuring.
    4. Differences in DPD counting and payment allocation.

8. Limitations and Considerations

• Simplifying Assumptions:

  • The study used simplifying assumptions to reduce the burden on institutions.
  • These assumptions may affect the accuracy of the quantitative results.

• Data Quality:

  • Quality checks revealed issues with sample representativeness and data accuracy.
  • Some subjectivity remains in the selection of representative samples and the estimation of impacts.

• Interpretation:

  • The quantitative results should be interpreted with care due to methodological simplifications and the nature of the data collected.

Conclusion

The EBA’s QIS highlights the need for harmonizing the definition of default across EU institutions to reduce variability in risk-weighted assets (RWA) and capital requirements. The findings show that differences in default definitions are primarily due to materiality thresholds, probation periods, and interpretations of unlikelihood to pay indicators. While the study provides valuable insights, the results are subject to simplifying assumptions and data quality issues, and should be used as indicative rather than definitive.