GCD is running regularly benchmarking studies and hypothetical portfolio excercises, in order to support its members with an independent view on how their credit risk estimates compare to peers.
Our IFRS 9 benchmarking is now fully integrated in our benchmarking work, more information is available with Hale Tatar.
The results from previous excercises are summarized in our IFRS 9 Benchmarking Report 2019 (August 2019).
Summary of the results:
- Variability of ECL estimates is noticeable for all asset classes
- The variability between banks’ estimates is observed for all segments defined by ECL drivers such as obligor type, geography, industry, rating and PD, facility type, guarantees and collateralization
- Bank-specific or reference macro-economic scenarios used for projections led to identical conclusions: in the current macro-economic environment, the variability between banks is mainly caused by banks’ different models and not by different macro-economic forecasts
- In order to “measure” the variability, we introduce a multiplier (=ECL 3rd quartile / ECL 1st quartile, calculated over all participating banks). We see that the multiplier is fairly stable over all asset classes and – on average – stands at least at a level of 4.
- Projections of stress test scenarios logically increase ECL levels and also notably increase the variability between banks
Why should your bank participate in our benchmarking work:
- Determine how your bank’s IFRS 9 estimates compare to that of peer banks.
- Neutral to your bank’s portfolio or macro-economic forecast
- Be able to track down the reason for the variability, e.g.
- Does the difference lie in the PD estimation, the LGD estimation or the exposure calculation?
- How much is the 12-month ECL or the lifetime ECL impacted by banks’ economic forecast?
- Is your bank’s stage allocation process more or less conservative than that of other banks?
- Do banks PD curves differ by country? How many banks apply LGD term structures?
- In which countries is the variability between banks the most?
- How does the expected life of revolving facilities differ between banks?
- “By banks, for banks”: We value your input in changing the study to your needs
- Aligned with GCD’s other datapools: Easily explain further differences making use of GCD’s
- Benchmarking platform: Benchmarking estimated PD, LGD and CCFs on name-by-name basis / by “risk cluster”
- Ratings and PD platform: Benchmarking observed default rates by “risk cluster”
- LGD/EAD platform: Benchmarking observed LGDs and CCFs
- Based on GCD’s datapooling infrastructure: Highly-secured data portal and datapooling regulations ensure maximum confidentiality
- Ultimately, strengthen your IFRS 9 model development and validation process
