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.
Keywords: Liquidity Coverage, Basel III, payment systems, graph theory, simulation modeling
JEL Codes: E58, G21, E42, C63