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.
|Series||CentER Discussion Paper Series|
- c32 - "Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models"
- c53 - "Forecasting and Prediction Methods; Simulation Methods "
- c61 - "Optimization Techniques; Programming Models; Dynamic Analysis"
- e42 - "Monetary Systems; Standards; Regimes; Government and the Monetary System; Payment Systems"
- e44 - Financial Markets and the Macroeconomy
- e47 - Money and Interest Rates: Forecasting and Simulation: Models and Applications
- large-value payment systems
- predictive modeling
- dynamic system
- TIME-SERIES ANALYSIS