A Comparison of Three Models to Predict Liquidity Flows between Banks Based on Daily Payments Transactions
| Autor: | Ron Triepels, Hennie Daniels |
|---|---|
| Přispěvatelé: | Center Ph. D. Students, Research Group: Information & Supply Chain Management, Department of Management |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Actuarial science
media_common.quotation_subject e47 - Money and Interest Rates: Forecasting and Simulation: Models and Applications Financial market Inflow Payment Moving-average model Task (project management) Market liquidity Programming Models Constraint (information theory) Standards Regimes Government and the Monetary System e44 - Financial Markets and the Macroeconomy Econometrics dynamic system time-series analysis Business Dynamic Quantile Regressions Time series predictive modeling media_common large-value payment systems |
| Zdroj: | 14th Payment and Settlement System Simulation Seminar Computing in Economics and Finance |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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