How Much Liquidity Would a Liquidity-saving Mechanism save If a Liquidity-saving Mechanism Could save Liquidity? A Simulation Approach for Canada’s Large-value Payment System

IF 0.4 Q4 BUSINESS, FINANCE
Shaun Byck, Ronald Heijmans
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引用次数: 4

Abstract

Canada’s Large Value Transfer System (LVTS) is in the process of being replaced by a real-time gross settlement (RTGS) system. A pure RTGS system typically requires participants to hold substantial amounts of intraday liquidity in order to settle their payment obligations. Implementing one or more liquidity-saving mechanisms (LSMs) can reduce the amount of liquidity that participants need to hold. This paper investigates how much liquidity requirements can be reduced with the implementation of different LSMs in the Financial Network Analytics simulation engine using LVTS transaction data from 2018. These LSMs include (1) bilateral offsetting, (2) FIFO–bypass, (3) multilateral offsetting and (4) a combination of all LSMs. We simulate two different scenarios at varying levels of the upper bound of liquidity. In the first scenario, all payments from Tranche 1, which are considered time critical, are settled in a pure RTGS payment stream, while less time-critical Tranche 2 payments are settled in a payment stream with LSMs. In the second scenario, we settle all payments (Tranches 1 and 2) in the LSM stream. Our results show that when there is ample liquidity available in the system, there is minimal benefit from LSMs, as payments are settled without much delay: the effectiveness of LSMs increases as the amount of intraday liquidity decreases. A combination of LSMs shows a reduction in liquidity requirements that is larger than any one individual LSM.

如果一个流动性储蓄机制能够拯救流动性,那么一个流动性储蓄机制能拯救多少流动性?加拿大大额支付系统的仿真方法
加拿大的大额转账系统(LVTS)正在被实时全额结算(RTGS)系统所取代。一个纯粹的RTGS系统通常要求参与者持有大量的日内流动性,以结算他们的支付义务。实施一个或多个流动性节约机制(LSMs)可以减少参与者需要持有的流动性数量。本文使用2018年的LVTS交易数据,研究了在金融网络分析模拟引擎中实现不同的lsm可以降低多少流动性需求。这些lsm包括(1)双边抵消,(2)fifo旁路,(3)多边抵消和(4)所有lsm的组合。我们在流动性上限的不同水平上模拟了两种不同的情景。在第一个场景中,所有来自Tranche 1的支付(被认为是时间关键的)都在纯RTGS支付流中结算,而时间不那么关键的Tranche 2支付则在带有lsm的支付流中结算。在第二个场景中,我们结算LSM流中的所有付款(第1和第2部分)。我们的研究结果表明,当系统中有充足的流动性时,lsm的收益最小,因为付款结算没有太多延迟:lsm的有效性随着日内流动性的减少而增加。LSM的组合显示流动性需求的减少比任何单个LSM都要大。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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