Forex Trading Volatility Prediction using Neural Network Models

TL;DR

This paper presents a deep learning approach, specifically multiscale LSTM models, for predicting Forex volatility, demonstrating superior accuracy over traditional and other deep learning methods.

Contribution

It introduces a multiscale LSTM model for Forex volatility prediction, outperforming conventional autoregressive, GARCH, and other deep learning models.

Findings

Multiscale LSTM achieves state-of-the-art accuracy.

The model outperforms traditional GARCH and autoregressive models.

Multi-currency input improves prediction performance.

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.