# Forex Trading Volatility Prediction using Neural Network Models

arXiv: https://arxiv.org/abs/2112.01166 · HTML: https://mlsanity.com/paper/2112.01166/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 2 Dec 2021.

## Builds on (most cited first, 6 of 6)

- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019](https://mlsanity.com/paper/1911.13288/) (2019)
- [Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition](https://mlsanity.com/paper/1402.1128/) (2014)
- [Deep Learning for Forecasting Stock Returns in the Cross-Section](https://mlsanity.com/paper/1801.01777/) (2018)
- [Deep Learning for Predicting Asset Returns](https://mlsanity.com/paper/1804.09314/) (2018)
- [Long Short-Term Memory Networks for CSI300 Volatility Prediction with Baidu Search Volume](https://mlsanity.com/paper/1805.11954/) (2018)

## 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.