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

Author

Listed:
  • Marek Hlavacek
  • Michael Konak
  • Josef Cada

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.

Suggested Citation

  • Marek Hlavacek & Michael Konak & Josef Cada, 2005. "The Application of Structured Feedforward Neural Networks to the Modelling of Daily Series of Currency in Circulation," Working Papers 2005/11, Czech National Bank, Research Department.
  • Handle: RePEc:cnb:wpaper:2005/11
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    File URL: http://www.cnb.cz/en/research/research_publications/cnb_wp/download/cnbwp_2005_11.pdf
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    References listed on IDEAS

    as
    1. Alberto Cabrero & Gonzalo Camba-Mendez & Astrid Hirsch & Fernando Nieto, 2009. "Modelling the daily banknotes in circulation in the context of the liquidity management of the European Central Bank," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(3), pages 194-217.
    2. Harvey, Andrew & Koopman, Siem Jan & Riani, Marco, 1997. "The Modeling and Seasonal Adjustment of Weekly Observations," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 354-368, July.
    3. Koreisha, Sergio G. & Pukkila, Tarmo, 1998. "A two-step approach for identifying seasonal autoregressive time series forecasting models," International Journal of Forecasting, Elsevier, vol. 14(4), pages 483-496, December.
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    Cited by:

    1. Jan Cimburek & Pavel Řežábek, 2013. "Currency in Circulation: Reaction in Crises," Český finanční a účetní časopis, University of Economics, Prague, vol. 2013(3).
    2. Haider, Adnan & Hanif, Muhammad Nadeem, 2007. "Inflation Forecasting in Pakistan using Artificial Neural Networks," MPRA Paper 14645, University Library of Munich, Germany.
    3. Maroje Lang & Davor Kunovac & Silvio Basač & Željka Štaudinger, 2008. "Modelling of Currency outside Banks in Croatia," Working Papers 17, The Croatian National Bank, Croatia.
    4. Faruk Balli & Elsayed Mousa Elsamadisy, 2012. "Modelling the currency in circulation for the State of Qatar," International Journal of Islamic and Middle Eastern Finance and Management, Emerald Group Publishing, vol. 5(4), pages 321-339, November.

    More about this item

    Keywords

    Neural network; seasonal time series; currency in circulation.;

    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

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