结算速度与金融稳定

Federal Reserve Board, Washington, D.C.ISSN 1936-2854 (Print) Settlement Speed and Financial Stability Agostino Capponi, Jin-Wook Chang Please cite this paper as:Capponi, Agostino, and Jin-Wook Chang (2025). “Settlement Speed and Financial Stabil-ity,” Finance and Economics Discussion Series 2025-101. Washington: Board of Governors NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminarymaterials circulated to stimulate discussion and critical comment.The analysis and conclusions set forthare those of the authors and do not indicate concurrence by other members of the research staff or the Settlement Speed and Financial Stability Agostino Capponi†Jin-Wook ChangNovember 10, 2025 Abstract This paper investigates how settlement speed affects financial stability in paymentnetworks, taking into account netting benefits, liquidity costs, and counterparty risks.Our analysis reveals that faster settlements have ambiguous effects on systemic risk andsocial welfare. The optimal settlement speed is determined by the network structureand the trade-off between netting efficiency and liquidity costs on one hand, and the Keywords:settlement, payment systems, financial network, financial stability, systemic JEL Classification Numbers:D49, D53, G01, G21, G33 1.Introduction The Securities and Exchange Commission (SEC) has recently implemented the conversionof the U.S. securities market to a T+1 (one business day after the trade date) settlementcycle, starting May 28, 2024.Following the SEC’s implementation of the new settlement speed, there has not been a noticeable change in settlement fails other than trading halts,which were due to technical malfunctions, in the New York Stock Exchange on June 4, 2024. However, the question of “whether shorter settlement lags would increase the likelihood Despite extensive study, research on the systemic risk implications of altering the settle-ment cycle remains scarce. In particular, payment systems can amplify initial shocks because of their critical role in the financial system and their complex network structure, which can We develop a network model to study the role of settlement in payment systems. Ourmodel incorporates four core features of payment systems:(i) networked payment flows, liquidity shocks, including counterparty defaults (not simple operational failures). Our key contribution to the financial networks literature is incorporating an understudieddimension:time. This enables us to analyze ex ante social welfare by accounting for boththe probability of shocks and the severity of resulting contagion. In contrast, most existing Each agent in the network holds a cash buffer and has senior debt obligations. Agentsare interconnected through a payment network.The key parameter of our model isτ, which represents the settlement time for all transactions in the network. Asτdecreases, the settlement speed increases, withτ= 0 implying instant settlement. The settlement timeτ We incorporate counterparty risk through a random liquidity shock that can hit some ofthe agents in the network before settlement occurs. The probability of this shock arrivingbefore settlement is increasing inτ. When the shock arrives, it reduces the available cash of likelihood of default contagion, but increases liquidity costs and leaves a larger share ofliabilities unnetted.The ex-post welfare implications when a shock arrives is clear, as weshow in Lemma 1, because a smallerτreduces available cash and amplifies contagion via Our analysis sheds light on the complex relationship between settlement speed, network First, we establish the existence of a discontinuous contagion pattern.Specifically,changes in settlement time can cause social welfare to increase or decrease sharply, de-pending on the level of sparsity and interconnectedness in the network (see Figures 2 and 4).In a complete network, if settlement is sufficiently slow, only a single agent defaults when Second, we introduce the concept of default threshold points, defined as settlement timesat which the number of defaulting agents in the network changes discontinuously.Thesethreshold points are shown to be critical in determining the ex-ante welfare implications Third, we characterize the differential impact of settlement speed on different network structures. We prove that faster settlement time can improve ex ante social welfare of thering network but worsen ex ante social welfare of the complete network. This heterogeneity in Fourth, we show that the node depth centrality is a key measure of an agent’s systemicimportance.This measure captures how the initial impact of a default by one agent is We show that liquidity conditions play a crucial role in determining the optimal set-tlement speed for financial stability.As liquidity conditions deteriorate, financial stabilityconcerns could favor slower settlement systems that prioritize netting efficiency and liquid- Altogether, o