支付网络中的流动性覆盖率:揭示传染路径

Richard Heuver, R. Berndsen
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引用次数: 2

摘要

《巴塞尔协议III》框架的流动性覆盖率(LCR)要求旨在使银行更能抵御流动性冲击,并表明银行在30天压力期内能够履行其支付义务的程度。尽管它是监管机构现有信息的重要补充,但它提供的是单个银行每月报告的状况信息。在本文中,我们每天生成一个类似lcr的统计数据,并使用TARGET2的历史支付数据模拟每个系统重要性银行的流动性失败。这篇论文的目的是揭示传染的途径。触发因素是一家LCR不断恶化的银行,其连锁效应被建模为对其他银行LCR的影响。然后,我们推导出传染的级联反应,它通常由多条路径组成,试图回答金融网络进一步恶化到何种程度的问题。在这样做的过程中,我们提供了传染的路径,给网络中存在的潜在系统性风险的感觉。我们发现,大部分损失是由少数几家大银行造成的。此外,我们发现,无论破坏的规模或地点如何,一些银行都非常容易受到冲击。我们的模型显示,与其他银行的流动性增加相比,承压银行的流动性不足是一个更重要的驱动因素。基于14天周期的传染网络的一个版本揭示了一个月的模式,这与其他研究橱窗装饰的文献一致。本文中使用的数据可供监管机构、中央银行和清算机构使用,因此可以预测其支付网络中流动性覆盖失败的传染。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Liquidity Coverage Ratio in a Payments Network: Uncovering Contagion Paths
The Liquidity Coverage Ratio (LCR) requirement of the Basel III framework is aimed at making banks more resilient against liquidity shocks and indicates the extent to which a bank is able to meet its payment obligations over a 30-day stress period. Notwithstanding the fact that it forms an important addition to the available information for regulators, it presents information on the status of a single bank on a monthly reporting basis. In this paper we generate an LCR-like statistic on a daily basis and simulate liquidity failure of each of the systemically important banks, using historical payments data from TARGET2. The aim of the paper is to uncover paths of contagion. The trigger is a bank with a deteriorating LCR and the knock-on effect is modelled as the impact on the LCR of other banks. We generate then the cascade of contagion, which in general consists of multiple paths, trying to answer the question to what extent the financial network further deteriorates. In doing so we provide paths of contagion which give a sense of potential systemic risk present in the network. We find that the majority of damage is caused by a small group of large banks. Furthermore we find groups of banks that are very vulnerable to shocks, regardless of the size or location of the disruption. Our model reveals that the shortfall of liquidity at the stressed bank is a more important driver than the addition of liquidity at the other banks. A version of the contagion network based on a 14-day period reveals a monthly pattern, which is in line with other literature in which window dressing is addressed. The data used in this paper are available to supervisors, central banks and resolution authorities, therefore making it possible to anticipate contagion of failing liquidity coverage within their payment network on a daily basis.
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