Abstract
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
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A Comparison of Three Models to Predict Liquidity Flows between Banks Based on Daily Payments Transactions. / Triepels, Ron; Daniels, Hennie.
Tilburg : Information Management, 2016. (CentER Discussion Paper; Vol. 2016-037).Research output: Working paper › Discussion paper › Other research output
TY - UNPB
T1 - A Comparison of Three Models to Predict Liquidity Flows between Banks Based on Daily Payments Transactions
AU - Triepels, Ron
AU - Daniels, Hennie
PY - 2016/9/7
Y1 - 2016/9/7
N2 - 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 finallythe dynamic system without constraint.
AB - 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 finallythe dynamic system without constraint.
KW - large-value payment systems
KW - predictive modeling
KW - dynamic system
KW - time-series analysis
M3 - Discussion paper
VL - 2016-037
T3 - CentER Discussion Paper
BT - A Comparison of Three Models to Predict Liquidity Flows between Banks Based on Daily Payments Transactions
PB - Information Management
CY - Tilburg
ER -