EBA欧洲银行-EBA-REPORT-RESULTS-FROM-THE-2016-HIGH-DEFAULT-PORTFOLIO-28HDP2920EXERCISE_69页_5mb

EBA Report: 2016 High Default Portfolios (HDP) Exercise Summary

Core Content

This report presents the findings of the 2016 supervisory benchmarking exercise on high default portfolios (HDPs) for residential mortgages, SME retail, SME corporate, and corporate-other portfolios. The data was collected from 114 institutions across 17 EU countries, with a reference date of 31 December 2015. The study aims to assess the variability in risk-weighted assets (RWAs) and identify the drivers behind such differences, using both quantitative and qualitative methods.

Main Objectives

• To evaluate the variability in RWA and GC (Global Charge) across HDPs.
• To examine the impact of different factors (e.g., default status, country of exposure, portfolio mix) on RWA and GC.
• To compare the results of internal ratings-based (IRB) approaches (FIRB and AIRB) and assess their accuracy against EU benchmarks.
• To identify potential underestimations in internal models and their implications for capital adequacy.

Key Findings

1. Top-Down Approach

• The top-down approach quantifies the proportion of GC variability that can be explained by key drivers such as the proportion of defaulted exposures, non-EU exposures, and portfolio mix.
• GC increased compared to previous reports (from 67% in 2013 to 75% on average in 2016).
• GC variability ranged from 8% to 293%, indicating significant dispersion.
• The average RW per institution varied from 7% to 129%, with a simple average of 33%.
• Approximately 82% of GC variability can be explained by the drivers mentioned above, slightly higher than the 78% from the 2013 report.
• The remaining 18% is attributed to underlying credit risk, modeling assumptions, and supervisory practices.

2. Cross-Sectional (Distribution Analysis) Approach

• This approach provides an in-depth look at risk parameters and portfolio characteristics.
• Significant variability in RW interquartile ranges was observed, particularly in SME corporate and corporate-other portfolios.
• The country of the counterparty plays a key role in RW variability, with banks in countries experiencing stressed macroeconomic conditions showing higher RWs.
• Differences in PDs and LGDs were noted between FIRB and AIRB approaches, with AIRB generally showing lower LGDs and CCFs, possibly due to PD estimation practices.

3. Outturns (Backtesting) Approach

• The outturns approach compares observed default rates and loss rates with estimated PDs and LGDs.
• Estimated PDs and LGDs were found to be generally higher than observed values, suggesting an overall conservative approach by banks.
• Some banks showed observed values above estimated ones, requiring further analysis.
• The country of the reporting bank and counterparty was identified as a major driver of RW variability, influenced by both risk profiles and supervisory practices.

4. Competent Authorities (CAs) Assessments

• Most CAs found RW deviations from EU benchmarks to be justified and not significant.
• Residential mortgages were highlighted as a key portfolio for monitoring due to their importance and potential impact on RWAs.
• Corporate-other and SME corporate portfolios showed the highest numbers of potential underestimations in RWs, with no clear justifications.
• Banks' internal validations did not identify most of these underestimations, but CAs noted that supervisory actions were being taken to address them.

5. Impact on CET1 Ratio

• Replacing estimated PDs with observed default rates and LGDs with observed loss rates would only slightly decrease the average CET1 ratio by 17 bps.
• This impact should be interpreted cautiously due to data quality constraints and the fact that the higher RWAs were not designed to estimate potential impacts.
• The study does not suggest a significant capital shortfall based on observed defaults.

Methodology and Data

• Data Sources: Data was collected based on the EBA's technical standards (ITS) and COREP data, with a focus on HDPs.
• Data Scope: The data included only HDPs (excluding LDPs like government and large corporate portfolios), and was reported at the highest level of consolidation.
• Sample Size: 114 institutions participated, with 99 remaining after data cleansing.
• Data Quality: The study noted data quality constraints due to the larger sample size and new technical standards, suggesting the need for caution in interpreting results.

Qualitative Insights

• Interviews with 10 participating banks provided qualitative data on modeling methodologies, data sources, and risk parameter definitions.
• These insights helped to better understand the approaches used by banks and the factors behind observed differences in RWA and GC.

Limitations and Considerations

• The study acknowledges the limitations of the different approaches, including the inability to compare the same counterparties across institutions.
• The cross-sectional and outturns approaches are used in conjunction to provide a more comprehensive understanding of variability.
• The results should be interpreted with care due to data quality issues and the potential influence of different modeling practices.

Conclusion

The 2016 HDP exercise highlights the importance of supervisory benchmarking in understanding and managing RWA variability. It confirms that a significant portion of variability can be attributed to measurable factors like default status and portfolio mix, but also notes the need for further analysis and supervisory action in cases of potential underestimations. The study serves as a valuable input for CAs in their assessments and monitoring of internal models.