Liquidity coverage ratio in a payment network: Uncovering contagion paths

The Basel III framework’s liquidity coverage ratio (LCR) requirement aims to make banks more resilient against liquidity shocks. LCR 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 information available to 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 and simulate liquidity failure for each of the systemically important banks, using historical data from the TARGET2 payment system. Our aim is to uncover paths of contagion. The trigger is a bank with a deteriorating LCR, and we model the knock-on effect as the impact on other banks’ LCR. We then generate the cascade of contagion, which in general consists of multiple paths, to determine the extent to which the financial network further deteriorates. In doing so, we provide paths of contagion that give a sense of the systemic risk present in the network. We find that the majority of damage is caused by a small group of large banks. Furthermore, we found groups of banks that are very vulnerable to shocks, regardless of the size or location of the disruption. Our model reveals that a liquidity shortfall at a stressed bank is a more important driver than the addition of liquidity at other banks. A version of our 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, making it possible to anticipate the contagion of liquidity coverage failures within their payment network on a daily basis.