Forex Trading Volatility Prediction using Neural Network Models
This addresses volatility prediction for Forex traders, but it is incremental as it applies existing deep learning methods to this domain.
The paper tackled predicting Forex currency pair volatility using deep learning, achieving state-of-the-art accuracy with a multiscale LSTM model that outperformed conventional baselines and other deep learning models.
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.