Losses from Simulated Defaults in Canada's Large Value Transfer System
Bibliographic record
Abstract
The Large Value Transfer System (LVTS) loss-sharing mechanism was designed to ensure that, in the event of a one-participant default, the collateral pledged by direct members of the system would be sufficient to cover the largest possible net debit position of a defaulting participant. However, the situation may not hold if the indirect effects of the defaults are taken into consideration, or if two participants default during the same payment cycle. The authors examine surviving participant total losses under both oneand two-participant default conditions, assuming the potential knock-on effects of the default. Their analysis includes the impact of a decline in value of LVTS collateral following an unexpected default. Simulations of participant defaults indicate that the impact on the LVTS is generally small; surviving participants do incur end-of-day collateral shortfalls, but only rarely and in small amounts. Under the two-participant default scenario, the likelihood of the Bank of Canada having to provide funds to ensure LVTS settlement is reasonably low, as is the average residual-coverage amount. The majority of LVTS participants pledge as collateral securities issued by other system members. However, the impact of an issuer of such collateral defaulting is generally not significant in the LVTS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".