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Should Deep Learning Models be in High Demand, or Should They Simply be a Very Hot Topic? A Comprehensive Study for Exchange Rate Forecasting

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Abstract

Exchange rate movements can significantly impact not only foreign trade, capital flows, and asset portfolio management, but also real economic activity. Therefore, the forecast of exchange rates has always been of great interest among academics, economic agents, and institutions. However, exchange rate series are essentially dynamic and nonlinear in nature and thus, forecasting exchange rates is a difficult task. On the other hand, deep learning models in solving time series forecasting tasks have been proposed in the last half-decade. But the number of formal comparative study in terms of exchange rate forecasting with deep learning models is quite limited. For this purpose, this study applies ten different models (Random Walk, Autoregressive Moving Average, Threshold Autoregression, Autoregressive Fractionally Integrated Moving Average, Support Vector Regression, Multilayer Perceptron, Recurrent Neural Network, Long Short Term Memory, Gated Recurrent Unit and Autoregressive Moving Average-Long Short Term Memory Hybrid Models) and two forecasting modes (recursive and rolling window) to predict three major exchange rate returnsnamely, the Canadian dollar, Australian dollar and British pound against the US Dollar in monthly terms. To evaluate the forecasting performances of the models, we used Model Confidence Set procedure as an advanced test. According to our results, the proposed hybrid model produced the best out-of-sample forecast performance in all samples, without exception.

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Notes

  1. Critics of structural models and DSGE models highlight the fact that they are not appropriate for forecasting exchange rates; rather, these models chiefly look to explain the factors underlying the equilibrium setting of all endogenous variables within an interaction.

  2. Indeed, there is an esteemed literature about modelling exchange rates by focusing nonlinearities in volatility, but this debate is out of scope of this paper.

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  1. The Graduate School of Natural and Applied Sciences, Dokuz Eylul University, Izmir, Turkey

    Firat Melih Yilmaz

  2. Department of Econometrics, Uludag University, Bursa, Turkey

    Ozer Arabaci

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Yilmaz, F.M., Arabaci, O. Should Deep Learning Models be in High Demand, or Should They Simply be a Very Hot Topic? A Comprehensive Study for Exchange Rate Forecasting. Comput Econ 57, 217–245 (2021). https://doi.org/10.1007/s10614-020-10047-9

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