Forex Trading Volatility Prediction using Neural Network Models

• URL: http://arxiv.org/abs/2112.01166v2
• Date: Fri, 3 Dec 2021 15:19:49 GMT
• Title: Forex Trading Volatility Prediction using Neural Network Models
• Authors: Shujian Liao, Jian Chen and Hao Ni
• Abstract summary: We show 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.
• Score: 6.09960572440709
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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