Can Oil Price Predict Exchange Rate? Empirical Evidence from Deep Learning

Samir Safi, Salisu Aliyu, Ibrahim Kekere () and Olajide Idris Sanusi
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Samir Safi: Department of Analytics in the Digital Era, CBE, United Arab Emirates University, United Arab Emirates,
Salisu Aliyu: Department of Computer Science, Ahmadu Bello University, Zaria, Nigeria,
Olajide Idris Sanusi: Department of Innovation in Government and Society, CBE, United Arab Emirates University, United Arab Emirates.

International Journal of Energy Economics and Policy, 2022, vol. 12, issue 4, 482-493

Abstract: This paper critically analyses the predictability of exchange rates using oil prices. Extant literature that investigates the significance of oil prices in forecasting exchange rates remains largely inconclusive due to limitations arising from methodological issues. As such, this study uses deep learning approaches such as Multi-Layer Perceptron (MLP), Convolution Neural Network (CNN), and Long Short-Term Memory (LSTM) to predict exchange rates. In addition, the Empirical Mode Decomposition (EMD) of time series dataset was utilized to ascertain its effect on the quality of prediction. To examine the efficacy of using oil prices in forecasting exchange rates, bivariate models were also built. Of the three bivariate models developed, the EMD-CNN model has the best predictive performance. Results obtained show that oil price information has a strong influence on forecasting exchange rates.

Keywords: Exchange rate; Oil prices; Deep learning; Convolution Neural Network; Multilayer perceptron; Long short-term memory (search for similar items in EconPapers)
JEL-codes: C61 E17 Q43 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eco:journ2:2022-04-51

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