The Application of Structured Feedforward Neural Networks to the Modelling of Daily Series of Currency in Circulation
AbstractOne of the most significant factors influencing the liquidity of the financial market is the amount of currency in circulation. Although the central bank is responsible for the distribution of the currency it cannot assess the demand for the currency, as that demand is influenced by the non-banking sector. Therefore, the amount of currency in circulation has to be forecasted. This paper introduces a feedforward structured neural network model and discusses its applicability to the forecasting of currency in circulation. The forecasting performance of the new neural network model is compared with an ARIMA model. The results indicate that the performance of the neural network model is better and that both models might be applied at least as supportive tools for liquidity forecasting.
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Bibliographic InfoPaper provided by Czech National Bank, Research Department in its series Working Papers with number 2005/11.
Date of creation: Dec 2005
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Neural network; seasonal time series; currency in circulation.;
Find related papers by JEL classification:
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-06-10 (All new papers)
- NEP-CBA-2006-06-10 (Central Banking)
- NEP-CMP-2006-06-10 (Computational Economics)
- NEP-FMK-2006-06-10 (Financial Markets)
- NEP-FOR-2006-06-10 (Forecasting)
- NEP-ICT-2006-06-10 (Information & Communication Technologies)
- NEP-MON-2006-06-10 (Monetary Economics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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