Bibliographic record
Abstract
The authors build a theoretical model that generates demand for collateral by Large Value Transfer System (LVTS) participants under the assumption that they minimize the cost of holding and managing collateral for LVTS purposes. The model predicts that the optimal amount of collateral held by each LVTS participant depends on the opportunity cost of collateral, the transactions costs of acquiring assets used as collateral and transferring them in and out of the LVTS, and the distribution of an LVTS participant's payment flows in the LVTS. The authors conclude that the aggregate amount of collateral pledged to the LVTS is quite close to that predicted by the model, when benchmark values are used for opportunity and transactions costs that are based on anecdotal evidence, despite the fact that these costs are likely to vary among participants. If one LVTS participant that appears to face a lower opportunity cost of collateral is excluded from the analysis, the model predicts an aggregate level of collateral that is within 5 per cent of the amount actually held by LVTS participants, on average, between February 1999 and May 2003. The authors also apply panel-data regressions to the level of collateral held in the LVTS. They find that the results are broadly supportive of the theoretical model. Sensitivity analysis of this model indicates that, when the opportunity cost of collateral increases, the amount of collateral that participants hold could be greatly reduced.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".