Study on exemptions for third-country central banks and other entities under the Market Abuse Regulation and the Markets in Financial Instruments Regulation
Bibliographic record
Abstract
In accordance with Article 1.9 of the Markets in Financial Instruments Regulation \n(MiFIR) and Article 6.5 of the Market Abuse Regulation (MAR), this study reviews \ncentral banks’ and Debt Management Offices’ (DMOs) mandates and operational \nprocedures for a selected group of non-EU countries. It describes the main legal \nframework for market abuse and for the transparency of operations and markets \napplicable to third-country (non-EU) central banks. The study also offers a \nsnapshot of the current transparency of central banks’ balance sheets and trading \nactivities with EU counterparts or in EU-listed financial instruments. For DMOs, \nthe study only covers the market abuse regime, as DMOs are outside the scope of \nthe MiFIR exemption. Market transparency and market abuse frameworks \napplicable in the EU are also discussed in this study, as a benchmark for the \nassessment of third-country regimes. The countries covered include Australia, \nBrazil, Canada, China, Hong Kong SAR, India, Japan, Mexico, Singapore, South \nKorea, Switzerland, Turkey and the United States (as well as the BIS under \nMiFIR). The report concludes that the extension of the exemptions under MiFIR \nand MAR is appropriate and necessary for all central banks and DMOs, with the \nexception of two institutions under the MiFIR regime and one institution under the \nMAR regime due to insufficient information and/or transaction data.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| 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".