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.
the dynamic system without constraint.
| Original language | English |
|---|---|
| Place of Publication | Tilburg |
| Publisher | Information Management |
| Number of pages | 15 |
| Volume | 2016-037 |
| Publication status | Published - 7 Sep 2016 |
Publication series
| Name | CentER Discussion Paper |
|---|---|
| Volume | 2016-037 |
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Keywords
- large-value payment systems
- predictive modeling
- dynamic system
- time-series analysis
Cite this
Triepels, R., & Daniels, H. (2016). A Comparison of Three Models to Predict Liquidity Flows between Banks Based on Daily Payments Transactions. (CentER Discussion Paper; Vol. 2016-037). Tilburg: Information Management.