Liquidity Stress Detection in the European Banking Sector

15 Pages Posted: 28 Jun 2019 Last revised: 2 Jul 2019

See all articles by Richard Heuver

Richard Heuver

De Nederlandsche Bank

Ron Triepels

Tilburg University - Center for Economic Research (CentER)

Date Written: June 25, 2019

Abstract

Liquidity stress constitutes an ongoing threat to financial stability in the banking sector. A bank that manages its liquidity inadequately might find itself unable to meet its payment obligations. These liquidity issues, in turn, can negatively impact the liquidity position of many other banks due to contagion effects. For this reason, central banks carefully monitor the payment activities of banks in financial market infrastructures and try to detect early-warning signs of liquidity stress. In this paper, we investigate whether this monitoring task can be performed by supervised machine learning. We construct probabilistic classifiers that estimate the probability that a bank faces liquidity stress. The classifiers are trained on a dataset consisting of various payment features of European banks and which spans several known stress events. Our experimental results show that the classifiers detect the periods in which the banks faced liquidity stress reasonably well.

Keywords: Risk Monitoring, Liquidity Stress, Neural Networks, Financial Market Infrastructures, Large-Value Payment Systems

JEL Classification: G32, G33, C45, E42

Suggested Citation

Heuver, Richard and Triepels, Ron, Liquidity Stress Detection in the European Banking Sector (June 25, 2019). De Nederlandsche Bank Working Paper No. 642, June 2019. Available at SSRN: https://ssrn.com/abstract=3411469 or http://dx.doi.org/10.2139/ssrn.3411469

Richard Heuver (Contact Author)

De Nederlandsche Bank ( email )

PO Box 98
1000 AB Amsterdam
Amsterdam, 1000 AB
Netherlands

Ron Triepels

Tilburg University - Center for Economic Research (CentER) ( email )

P.O. Box 90153
Tilburg, 5000 LE
Netherlands

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