The Application of Structured Feedforward Neural Networks to the Modelling of Daily Series of Currency in Circulation

Marek Hlavacek, Michael Koňák and Josef Cada

Working Papers from Czech National Bank, Research and Statistics Department

Abstract: One 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.

Keywords: Neural network; seasonal time series; currency in circulation. (search for similar items in EconPapers)
JEL-codes: C45 C53 (search for similar items in EconPapers)
Date: 2005-12
New Economics Papers: this item is included in nep-cba, nep-cmp, nep-fmk, nep-for, nep-ict and nep-mon
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:cnb:wpaper:2005/11

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