Examining Full Collateral Coverage in Canada’s Large Value Transfer System
Bibliographic record
Abstract
The Large Value Transfer System (LVTS) is Canada’s main electronic interbank funds transfer system that financial institutions use daily to transmit thousands of payments worth several billions of dollars. The LVTS is different than real-time gross settlement (RTGS) systems because, while each payment is final and irrevocable, settlement occurs on a multilateral net basis at the end of the day. Furthermore, LVTS payments are secured by a collateral pool that mutualizes losses across participants in the event of a default. In this paper, we use the Bank of Finland Simulator to examine the implications of fully collateralizing LVTS payments, similar to an RTGS. An important caveat to consider, however, is that the simulations do not take into account the anticipated change in payment behaviour in response to a change in collateral requirements. In this regard, we include a queuing mechanism to at least reflect more efficient use of liquidity. The results indicate that collateral requirements vary by participant and some participants actually require less collateral in the simulation than what is required under the current LVTS design.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".