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

Author

Listed:
  • 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,)

  • Kekere Sule Ibrahim

    (Department of Economics, Ahmadu Bello University, Zaria, Nigeria,)

  • Olajide Idris Sanusi

    (Department of Innovation in Government and Society, CBE, United Arab Emirates University, United Arab Emirates.)

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.

Suggested Citation

  • Samir Safi & Salisu Aliyu & Kekere Sule Ibrahim & Olajide Idris Sanusi, 2022. "Can Oil Price Predict Exchange Rate? Empirical Evidence from Deep Learning," International Journal of Energy Economics and Policy, Econjournals, vol. 12(4), pages 482-493, July.
    • Handle: RePEc:eco:journ2:2022-04-51
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    References listed on IDEAS

    as
    1. Akbar, Muhammad & Iqbal, Farhan & Noor, Farzana, 2019. "Bayesian analysis of dynamic linkages among gold price, stock prices, exchange rate and interest rate in Pakistan," Resources Policy, Elsevier, vol. 62(C), pages 154-164.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Exchange rate; Oil prices; Deep learning; Convolution Neural Network; Multilayer perceptron; Long short-term memory;
    All these keywords.

    JEL classification:

    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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