  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.
