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Forex Trading Volatility Prediction using Neural Network Models

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  • Shujian Liao
  • Jian Chen
  • Hao Ni

Abstract

In this paper, we investigate the problem of predicting the future volatility of Forex currency pairs using the deep learning techniques. We show step-by-step how to construct the deep-learning network by the guidance of the empirical patterns of the intra-day volatility. The numerical results show that the multiscale Long Short-Term Memory (LSTM) model with the input of multi-currency pairs consistently achieves the state-of-the-art accuracy compared with both the conventional baselines, i.e. autoregressive and GARCH model, and the other deep learning models.

Suggested Citation

  • Shujian Liao & Jian Chen & Hao Ni, 2021. "Forex Trading Volatility Prediction using Neural Network Models," Papers 2112.01166, arXiv.org, revised Dec 2021.
    • Handle: RePEc:arx:papers:2112.01166
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    1. M. Rusydi & Sardar M. N. Islam, 2007. "Market Models and Applications," Palgrave Macmillan Books, in: Quantitative Exchange Rate Economics in Developing Countries, chapter 4, pages 45-62, Palgrave Macmillan.
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    3. Olson, Luke M. & Qi, Min & Zhang, Xiaofei & Zhao, Xinlei, 2021. "Machine learning loss given default for corporate debt," Journal of Empirical Finance, Elsevier, vol. 64(C), pages 144-159.
    4. Yu-Long Zhou & Ren-Jie Han & Qian Xu & Wei-Ke Zhang, 2018. "Long Short-Term Memory Networks for CSI300 Volatility Prediction with Baidu Search Volume," Papers 1805.11954, arXiv.org.
    5. Masaya Abe & Hideki Nakayama, 2018. "Deep Learning for Forecasting Stock Returns in the Cross-Section," Papers 1801.01777, arXiv.org, revised Jun 2018.
    6. Ruoxuan Xiong & Eric P. Nichols & Yuan Shen, 2015. "Deep Learning Stock Volatility with Google Domestic Trends," Papers 1512.04916, arXiv.org, revised Feb 2016.
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