Liquidity Stress Detection in the European Banking Sector

Richard Heuver, Ron Triepels

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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.
Original languageEnglish
Title of host publication Proceedings of the 11th International Conference on Agents and Artificial Intelligence
Place of PublicationPrague, Czech Republic
PublisherScitepress - Science And Technology Publications
Pages266-274
Volume2
ISBN (Print)978-989-758-350-6
DOIs
Publication statusPublished - 2019
Externally publishedYes
EventProceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2 - Prague, Czech Republic
Duration: 19 Feb 201921 Feb 2019

Conference

ConferenceProceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2
Abbreviated titleICAART
CountryCzech Republic
CityPrague
Period19/02/1921/02/19

Keywords

  • liquidity stress
  • risk monitoring
  • financial market infrastructures
  • large-value payment systems

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