What To Do about Bilateral Credit Limits in the LVTS When a Closure Is Anticipated: Risk versus Liquidity Sharing among LVTS Participants
Bibliographic record
Abstract
The authors examine the effect of a trade-off between shared credit risk and liquidity efficiency, among participants in Tranche 2 of the Large Value Transfer System (LVTS T2), on their decisions to leave open, or close, their bilateral credit limits (BCLs) to a participant at risk of imminent closure. The authors' analysis considers a network of three banks, in a settlement system similar to the LVTS T2. Although it is widely believed that closure of one bank is imminent, the exact timing of the closure – during or after the settlement cycle – is uncertain. The other two banks face an "open or close" choice regarding their BCLs to the problem participant. Based on the expected net payoff of each choice, which includes the value of network externalities, the analysis shows that, when the expected credit loss is sufficiently low, an open-BCL pure-strategy Nash equilibrium can exist and can be Pareto efficient. This result dispels the generality of the frequent assertion that participants in the LVTS T2 will close their BCLs to a participant that is subject to imminent closure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".