Optimal Dynamic Clearing for Interbank Payments
Bibliographic record
Abstract
We investigate the optimal clearing policy for a financial payment system composed of a number of member banks and a central bank in a dynamic setting, when new payment obligations or debts between member banks are generated over time. The central bank clears the debts among members in the system in order to minimize the costs, including the setup cost of each clearing, the variable cost of clearing the net debts, and the liquidity cost of uncleared debts. We formulate the problem using dynamic programming via state space reduction that provides a tractable framework to analyze and compute the optimal policy. We characterize the structure of the optimal policy and show that it is optimal for the central bank either to clear all the debts in the system or not to clear at all in each period under mild conditions. This structure leads to efficient computation of the optimal clearing policy. We further characterize the optimal clearing frequency based upon the deterministic approximation for the debt process. We conduct a comprehensive case study based on the data acquired from our industry partner, Payments Canada, to demonstrate the performance of the policy and its feasibility in industrial-size problems. This paper was accepted by Chung Piaw Teo, optimization. Funding: S. Chen’s research was supported by the National Natural Science Foundation of China [Grants 72101273, 72188101, 72101258, and 71991463], the Humanities and Social Science General Foundation of the Ministry of Education of China [Grant 21YJC630008], and the Hunan Provincial Natural Science Foundation of China [Grant 2022JJ40643]. Support for O. Baron’s research on this paper was provided by a grant from the Natural Science and Engineering Research Council of Canada (NSERC). N. Chen’s research was supported by the NSERC [Discovery Grant RGPIN-2020-04038]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00380 .
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".