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Non-Linear Interactions and Exchange Rate Prediction: Empirical Evidence Using Support Vector Regression

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  • Peng Yaohao
  • Pedro Henrique Melo Albuquerque

Abstract

This paper analysed the prediction of the spot exchange rate of 10 currency pairs using support vector regression (SVR) based on a fundamentalist model composed of 13 explanatory variables. Different structures of non-linear dependence introduced by nine different Kernel functions were tested and the predictions were compared to the Random Walk benchmark. We checked the explanatory power gain of SVR models over the Random Walk by applying White’s Reality Check Test. The results showed that the majority of SVR models achieved better out-of-sample performance than the Random Walk, but in overall they failed to achieve statistical significance of predictive superiority. Furthermore, we observed that non-mainstream Kernel functions performed better than the ones commonly used in the machine-learning literature, a finding that can provide new insights regarding machine-learning methods applications and the predictability of exchange rates using non-linear interactions between the predictors.

Suggested Citation

  • Peng Yaohao & Pedro Henrique Melo Albuquerque, 2019. "Non-Linear Interactions and Exchange Rate Prediction: Empirical Evidence Using Support Vector Regression," Applied Mathematical Finance, Taylor & Francis Journals, vol. 26(1), pages 69-100, January.
    • Handle: RePEc:taf:apmtfi:v:26:y:2019:i:1:p:69-100
    DOI: 10.1080/1350486X.2019.1593866
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    Cited by:

    1. Flavio Barboza & Geraldo Nunes Silva & José Augusto Fiorucci, 2023. "A review of artificial intelligence quality in forecasting asset prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1708-1728, November.
    2. Nagula, Pavan Kumar & Alexakis, Christos, 2022. "A new hybrid machine learning model for predicting the bitcoin (BTC-USD) price," Journal of Behavioral and Experimental Finance, Elsevier, vol. 36(C).
    3. Mei-Li Shen & Cheng-Feng Lee & Hsiou-Hsiang Liu & Po-Yin Chang & Cheng-Hong Yang, 2021. "An Effective Hybrid Approach for Forecasting Currency Exchange Rates," Sustainability, MDPI, vol. 13(5), pages 1-29, March.
    4. Julio E. Sandubete & León Beleña & Juan Carlos García-Villalobos, 2023. "Testing the Efficient Market Hypothesis and the Model-Data Paradox of Chaos on Top Currencies from the Foreign Exchange Market (FOREX)," Mathematics, MDPI, vol. 11(2), pages 1-29, January.
    5. Pavan Kumar Nagula & Christos Alexakis, 2022. "A Novel Machine Learning Approach for Predicting the NIFTY50 Index in India," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 28(3), pages 155-170, November.

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