A Comparison of Three Models to Predict Liquidity Flows between Banks Based on Daily Payments Transactions

Ron Triepels, Hennie Daniels

Research output: Working paper › Discussion paper › Other research output

Abstract

The analysis of payment data has become an important task for operators and overseers of financial market infrastructures. Payment data provide an accurate description of how banks manage their liquidity over time. In this paper we compare three models to predict future liquidity flows from payment data: 1) a moving average model, 2) a linear dynamic system that links the inflow of banks with their outflow, and 3) a similar dynamic system but with a constraint that guarantees the conservation of liquidity. The error graphs of one-step-ahead predictions on real-world payment data reveal that the moving average model performs best, followed by the dynamic system with constraint, and finally
the dynamic system without constraint.
Original languageEnglish
Place of PublicationTilburg
PublisherInformation Management
Number of pages15
Volume2016-037
Publication statusPublished - 7 Sep 2016

Publication series

NameCentER Discussion Paper
Volume2016-037

Keywords

  • large-value payment systems
  • predictive modeling
  • dynamic system
  • time-series analysis

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