Forex Trading Volatility Prediction using Neural Network Models

Shujian Liao, Jian Chen and Hao Ni

Papers from arXiv.org

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.

Date: 2021-12, Revised 2021-12
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa, nep-for and nep-rmg
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2112.01166

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