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A Comparison between Neural Networks and GARCH Models in Exchange Rate Forecasting

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  • Fahima Charef
  • Fethi Ayachi

Abstract

Modeling and forecasting of dynamics nominal exchange rate has long been a focus of financial and economic research. Artificial Intelligence (IA) modeling has recently attracted much attention as a new technique in economic and financial forecasting. This paper proposes an alternative approach based on artificial neural network (ANN) to predict the daily exchange rates. Our empirical study is based on a series of daily data in Tunisia. In order to evaluate this approach, we compare it with a generalized autoregressive conditional heteroskedasticity (GARCH) model in terms of their performance. Results indicate that the proposed nonlinear autoregressive (NAR) model is an accurate and a quick prediction method. This finding helps businesses and policymakers to plan more appropriately.

Suggested Citation

  • Fahima Charef & Fethi Ayachi, 2016. "A Comparison between Neural Networks and GARCH Models in Exchange Rate Forecasting," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 6(1), pages 94-99, January.
    • Handle: RePEc:hur:ijaraf:v:6:y:2016:i:1:p:94-99
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    References listed on IDEAS

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    1. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    2. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Cited by:

    1. Muhammad Nadim Hanif & Khurrum S. Mughal & Javed Iqbal, 2018. "A Thick ANN Model for Forecasting Inflation," SBP Working Paper Series 99, State Bank of Pakistan, Research Department.
    2. Pyo, Sujin & Lee, Jaewook, 2018. "Exploiting the low-risk anomaly using machine learning to enhance the Black–Litterman framework: Evidence from South Korea," Pacific-Basin Finance Journal, Elsevier, vol. 51(C), pages 1-12.

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    More about this item

    Keywords

    Nominal exchange rate; Neural Networks; GARCH model; Forecasting; Tunisia;
    All these keywords.

    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
    • F31 - International Economics - - International Finance - - - Foreign Exchange

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