Basel III FRTB: data pooling innovation to lower capital charges
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Bibliographic record
Abstract
BACKGROUND: Anticipated to overhaul the structure of market risk teams, IT teams, and trading desks within banks by 2023, Basel III's Fundamental Review of the Trading Book requirements will also increase capital charges banks will incur globally. The case study focuses on describing what is needed with regards to the risk factor eligibility test (RFET) as well as for implementing a data pool to lower capital charges. By establishing a consortium of banks per region to implement a data pooling solution, participants can prove a wider breadth of modellable risk factors per asset class and use the Internal Models Approach (IMA) of valuing risk to lower capital charge requirements significantly. CASE DESCRIPTION: First, a description on the historical context surrounding the Fundamental Review of the Trading Book rules and the business requirements needed to comply with the risk factor eligibility test is made. Then an examination is conducted on the innovative data pooling initiative implemented by CanDeal, TickSmith Corp., and the 6 largest Canadian banks to lower capital charge requirements under the Fundamental Review of the Trading Book. DISCUSSION AND EVALUATION: A description is made on what types of data, expertise, and technology is needed to calculate for risk factor modellability. It is up to each firm to decide if the benefits to using the Internal Models Approach to lower capital charges outweighs implementation and running costs of the underlying data platform. Implementing a data pool for each region comes with challenges that include anti-competition law that may block the initiative, varied benefits to each competitive participant, and data security concerns. CONCLUSION: It is evident that the data pool innovation provides benefits to lowering capital charges as the Canadian banks have seen an increase of modellability by several factors using the sample bond asset class. While each firm must still determine internally if the benefits outweighs the technological costs they will incur, it is clear that regulators are pushing for increased data retention and scrutiny.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it