The optimal desk coverage ratio and the Basel III FRTB internal models approach
Bibliographic record
Abstract
The implementation of the internal models approach under the Fundamental Review of the Trading Book (FRTB) is a contemporary topic among regulators and the global banking industry, considering the pending finalisation or implementation of localised standards in some major jurisdictions including the US, UK and European Union (EU). This paper proposes a simple intuitive approach to assess the joint impact and sensitivities of Basel III capital Output Floor and minimum desk coverage threshold on the internal models approach (IMA) application. In particular, the paper introduces the optimal desk coverage ratio as a metric to quantify the optimal proportion of the trading desks that a bank may cover under IMA to maximise capital savings given the constraints of Output Floor, minimum desk coverage threshold and other factors. The paper also presents Japan and Canada, where FRTB is in force, as live examples for analysis using actual bank-level regulatory disclosures from ten major banks. The paper illustrates the coverage cliff effect and concludes that the IMA application from a capital savings perspective is heavily driven not only by Output Floor and minimum desk coverage threshold but also by credit and market risk weightings and their respective capital saving ratios from internal models. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".