Forex Trading Volatility Prediction using Neural Network Models

12/02/2021
by   Shujian Liao, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/22/2022

Volatility forecasting using Deep Learning and sentiment analysis

Several studies have shown that deep learning models can provide more ac...
research
03/01/2021

Extreme Volatility Prediction in Stock Market: When GameStop meets Long Short-Term Memory Networks

The beginning of 2021 saw a surge in volatility for certain stocks such ...
research
06/07/2019

A long short-term memory stochastic volatility model

Stochastic Volatility (SV) models are widely used in the financial secto...
research
05/29/2018

Long Short-Term Memory Networks for CSI300 Volatility Prediction with Baidu Search Volume

Intense volatility in financial markets affect humans worldwide. Therefo...
research
06/20/2023

Comparing Deep Learning Models for the Task of Volatility Prediction Using Multivariate Data

This study aims to compare multiple deep learning-based forecasters for ...
research
09/26/2021

Multi-Transformer: A New Neural Network-Based Architecture for Forecasting S P Volatility

Events such as the Financial Crisis of 2007-2008 or the COVID-19 pandemi...
research
11/14/2017

SkipFlow: Incorporating Neural Coherence Features for End-to-End Automatic Text Scoring

Deep learning has demonstrated tremendous potential for Automatic Text S...

Please sign up or login with your details

Forgot password? Click here to reset