EBA欧洲银行-EBA-Report-results-from-the-2016-high-default-portfolio-exercise-March-2017_69页_5mb

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

Core Content

This report presents the results of the 2016 supervisory benchmarking exercise on high default portfolios (HDPs) for residential mortgages, SME retail, SME corporate, and corporate-other portfolios. The analysis is based on data reported as of 31 December 2015 by 114 institutions across 17 EU countries, representing the full population of banks using internal credit risk models.

Main Objectives

• Assess the variability in risk-weighted assets (RWA) and identify its drivers.
• Compare different regulatory approaches (FIRB and AIRB) and their impact on risk parameters.
• Evaluate the performance of internal models using the CET1 ratio.
• Provide insights into the quality of internal models and the role of competent authorities (CAs) in monitoring and supervising them.

Key Findings

Top-Down Approach

• The global charge (GC) variability increased compared to the 2013 HDP report, with an average GC of 75% (up from 67%).
• GC variability ranged from 8% to 293%, with 82% of this variability explained by key drivers such as the proportion of defaulted exposures, non-EU exposures, and portfolio mix.
• The remaining 18% of variability is attributed to underlying credit risk, bank-specific practices, and supervisory practices.

Cross-Sectional Approach

• The interquartile range (IQR) of RWs showed significant variability, especially for SME corporate and corporate-other portfolios.
• The country of the counterparty and the reporting bank significantly influenced RW variability, with EU countries experiencing stressed macroeconomic conditions showing higher RWs.
• Differences were observed between FIRB and AIRB approaches: LGDs under AIRB were generally lower, while PDs under FIRB were lower than under AIRB.

Outturn (Backtesting) Approach

• Estimated PDs and LGDs were generally higher than observed default and loss rates, indicating a conservative approach by banks.
• Some banks had observed values exceeding estimated values, requiring further analysis.
• The country of the reporting bank and counterparties was confirmed as a key driver of RW variability, influenced by both risk profiles and supervisory practices.

Competent Authorities' Assessments

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

Impact on CET1 Ratio

• Replacing estimated PDs with observed default rates and LGDs with observed loss rates would result in a slight decrease in the CET1 ratio (17 bps on average).
• This impact should be interpreted with caution due to data quality issues and the fact that higher RWAs were not designed to estimate potential impacts.
• The study does not suggest a significant shortfall in capital requirements based on observed data.

Methodology and Data

• Data Collection: Data was collected from 114 institutions, with 99 included after data cleansing. It was based on the ITS and RTS technical standards, and included details on exposure values, IRB parameters, and portfolio composition.
• Data Quality: Constraints were noted due to the larger sample size and new standards. Interpretation of findings should be cautious, especially regarding the impact on CET1.
• Approaches Used:
  • Top-down: Analyzed GC variability by controlling for key drivers.
  • Cross-sectional: Examined distribution analysis of RWs and risk parameters.
  • Outturn (Backtesting): Compared observed values with estimated values for risk parameters.

Additional Notes

• Interviews with 10 banks provided qualitative insights into internal models and their calculation methods.
• The report highlights the importance of supervisory oversight and the need for ongoing monitoring and corrective actions.
• The findings are intended to support CAs in their assessments and to guide future supervisory activities.

Conclusion

The 2016 HDP exercise provides valuable insights into the variability of RWA and the effectiveness of internal models in different portfolios. While the overall GC increased, the majority of variability is explained by controllable factors. The report underscores the importance of using multiple approaches to assess model quality and the role of supervisory actions in addressing potential underestimations.