Forex Trading Volatility Prediction using Neural Network Models
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Abstract (arXiv)
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.
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References · 6
- 2013Speech Recognition with Deep Recurrent Neural Networkscited by 666
- 2019Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019cited by 270
- 2014Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognitioncited by 111
- 2018Deep Learning for Forecasting Stock Returns in the Cross-Sectioncited by 6
- 2018Deep Learning for Predicting Asset Returnscited by 5
- 2018Long Short-Term Memory Networks for CSI300 Volatility Prediction with Baidu Search Volumecited by 4
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