How much liquidity does a payment system need, and what do netting and gridlock resolution save?

What it found

A simulated RTGS day (Zipf-concentrated banks, lognormal payment values, a share of payments followed by a return payment) with the paper's upper bound, lower bound and bilateral netting, a FIFO queue, and the Bech–Soramäki (2001) gridlock resolution algorithm, checked by brute force to return the largest feasible set of queue prefixes. Inputs from the paper: participants and payments per day (DK 146 and 925, FI 13 and 1,428), top-bank concentration and the Table 1 bounds. Independent payments need four times the published lower bound; with reciprocity fitted (ρ = 0.8 DK, 0.9 FI) the Danish-shaped system needs 34.7 % of gross value to settle on arrival and 10.1 % by close (published 37.2 and 10.7), the Finnish-shaped one 23.3 and 4.2 (27.4 and 4.3). Bilateral netting leaves 15.6 % (DK) and 7.2 % (FI) to fund. Gridlock resolution cuts mean settlement delay by 24–46 % in the Danish-shaped system (paper: up to 50 %) and 34–62 % in the Finnish-shaped one, where the paper found about 10 %: reported as an unexplained discrepancy. Assumed or fitted, not sourced: payment-value spread (swept), reciprocity (fitted to the lower bound, so the bound match is calibration), uniform timing, and my reading of the concentration shares. Simulated payments only; not financial advice.

A figure from its own output

Paper, code and results

• README and method
• Experiments and results
• Code
• Tests

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Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.