Developing macroprudential policy for alternative investment funds. Towards a framework for macroprudential leverage limits in Europe: an application for the Netherlands
Bibliographic record
Abstract
This joint ECB-DNB Occasional Paper aims to inform the ongoing discussions about an EU-level framework for operationalising macroprudential leverage limits for alternative investment funds (AIFs). It builds on, and extends, the analysis of an ECB-DNB special feature article published in the ECB's Financial Stability Review in November 2016. First, this Occasional Paper presents new EU-level evidence suggesting that leveraged funds exhibit stronger sensitivity of investor outflows to bad past performance than unleveraged funds, which has the potential to exacerbate systemic risk. Second, it devises a framework for assessing financial stability risks from leverage in investment funds. This is applied to leveraged AIFs managed by asset managers in the Netherlands using Alternative Investment Fund Managers Directive (AIFMD) data for the two-year period from the first quarter of 2015 to the fourth quarter of 2016. Third, it discusses the potential effectiveness and efficiency of various designs for macroprudential leverage limits. To this end, it builds on the findings for the Dutch AIF sector and suggests design options for further exploration at EU level. Beyond assessing financial stability risks from leverage in the Dutch AIF sector, the case study aims to show how equivalent information on AIFs at the European level - which will be made available to the European Securities Markets Authority (ESMA) and the European Systemic Risk Board (ESRB) in the coming years - could be used when developing an EU-level framework for operationalising macroprudential leverage limits.
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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 | 0.047 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".