Exchange Rate Forecasting: Nonlinear GARCH-NN Modeling Approach

Fahima Charef ()
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Fahima Charef: FSEGT, University of Tunis Elmanar

Annals of Data Science, 2024, vol. 11, issue 3, No 9, 947-957

Abstract: Abstract This paper targets the description of the fusion of modeling techniques, such as the GARCH model and the Artificial Neural Network (ANN), for the sake of predicting financial series and precisely the series of the exchange rate in Tunisia, namely the USD/TND, the EUR/TND and the YEN/TND, for a daily frequency extending from 2015 through 2019. To our knowledge, this is the only paper that focuses on the descriptions of the fusion of modeling techniques (GARCH-NN) or hybridization method that applied on Tunisian currency (TND). The empirical results show that the hybrid model (GARCH-NN) outperforms and it is more efficient than the two used models. In fact, this method combines the advantages of two approaches in order to obtain a result more satisfactory for the expectations of the policy-makers in the exchange market in order to take their decisions. The results showed that the model proposed gives better results, so, can be an alternative of standard linear autoregressive model. This result has been joined by many empirical studies that confirm the quality and performance of this methodology, which researchers advise to be retained in all areas.

Keywords: Forecasting; Exchange rate; GARCH model; Artificial neuronal networks; Hybrid model (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s40745-022-00458-w

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