A computational model of bilateral credit limits in payment systems and other financial market infrastructures
Bibliographic record
Abstract
This paper provides the first steps towards a theoretical and structural modelling framework through which optimal decision making in financial market infrastructures such as payments clearing and settlement systems can be assessed from a market microstructure perspective. In particular, the paper focuses on the application of agent-based computational economics and stochastic games in modelling the bilateral credit limit establishing behaviour of Participants in loss sharing arrangements within financial market infrastructures such as the Canadian Large Value Payments System (LVTS). With specific focus on the LVTS, the paper presents a structural model where the payments system represents a market in which bilateral credit limits are the pricing mechanisms for intraday liquidity provisioning and the credit risk arising from the loss sharing arrangement. The data-driven stochastic game framework further illustrates how payments data, in conjunction with other financial market and credit data, can be used to assess emergent macroscopic outcomes in clearing and settlement systems from the underpinning interactions of autonomous decision making agents. The paper speaks to potential policy issues such as the effectiveness of policy levers such as the System-Wide Percentage, regulatory concerns around procyclicality and free-riding arising from the market microstructure behaviours, and design of the System.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".