Cargando…

Volatility forecasts of stock index futures in China and the US–A hybrid LSTM approach

This paper is concerned with the unsolved issue of how to accurately predict the financial market volatility. We propose a novel volatility prediction method for stock index futures prediction based on LSTM, PCA, stock indices and relevant futures. Inspired by the recent advancement of deep learning...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Xue, Hu, Yan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9333249/
https://www.ncbi.nlm.nih.gov/pubmed/35901029
http://dx.doi.org/10.1371/journal.pone.0271595
_version_ 1784758831607185408
author Chen, Xue
Hu, Yan
author_facet Chen, Xue
Hu, Yan
author_sort Chen, Xue
collection PubMed
description This paper is concerned with the unsolved issue of how to accurately predict the financial market volatility. We propose a novel volatility prediction method for stock index futures prediction based on LSTM, PCA, stock indices and relevant futures. Inspired by the recent advancement of deep learning methodology, six models that combine a variety of artificial intelligence techniques are compared, including ANN, ANN(PCA), ANN(AE), LSTM, LSTM(PCA), and LSTM(AE). That is, in the design and comparison of the proposed AI models, we consider the combination of two dimensionality reduction methods (PCA and AE) and two typical neural networks (ANN and LSTM) in processing time series data. Besides, to further assess the prediction performance of the proposed models, two widely-applied statistical models (i.e. AR and EGARCH) on volatility prediction are used as benchmarks. In the empirical study, we collect financial trading data in both China and the US, and compare the performances of different models in predicting 5 days and 10 days ahead volatilities of stock index futures. In all, our analysis supports the use of LSTM(PCA) model to tackle those irregular and complex datasets.
format Online
Article
Text
id pubmed-9333249
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-93332492022-07-29 Volatility forecasts of stock index futures in China and the US–A hybrid LSTM approach Chen, Xue Hu, Yan PLoS One Research Article This paper is concerned with the unsolved issue of how to accurately predict the financial market volatility. We propose a novel volatility prediction method for stock index futures prediction based on LSTM, PCA, stock indices and relevant futures. Inspired by the recent advancement of deep learning methodology, six models that combine a variety of artificial intelligence techniques are compared, including ANN, ANN(PCA), ANN(AE), LSTM, LSTM(PCA), and LSTM(AE). That is, in the design and comparison of the proposed AI models, we consider the combination of two dimensionality reduction methods (PCA and AE) and two typical neural networks (ANN and LSTM) in processing time series data. Besides, to further assess the prediction performance of the proposed models, two widely-applied statistical models (i.e. AR and EGARCH) on volatility prediction are used as benchmarks. In the empirical study, we collect financial trading data in both China and the US, and compare the performances of different models in predicting 5 days and 10 days ahead volatilities of stock index futures. In all, our analysis supports the use of LSTM(PCA) model to tackle those irregular and complex datasets. Public Library of Science 2022-07-28 /pmc/articles/PMC9333249/ /pubmed/35901029 http://dx.doi.org/10.1371/journal.pone.0271595 Text en © 2022 Chen, Hu https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Chen, Xue
Hu, Yan
Volatility forecasts of stock index futures in China and the US–A hybrid LSTM approach
title Volatility forecasts of stock index futures in China and the US–A hybrid LSTM approach
title_full Volatility forecasts of stock index futures in China and the US–A hybrid LSTM approach
title_fullStr Volatility forecasts of stock index futures in China and the US–A hybrid LSTM approach
title_full_unstemmed Volatility forecasts of stock index futures in China and the US–A hybrid LSTM approach
title_short Volatility forecasts of stock index futures in China and the US–A hybrid LSTM approach
title_sort volatility forecasts of stock index futures in china and the us–a hybrid lstm approach
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9333249/
https://www.ncbi.nlm.nih.gov/pubmed/35901029
http://dx.doi.org/10.1371/journal.pone.0271595
work_keys_str_mv AT chenxue volatilityforecastsofstockindexfuturesinchinaandtheusahybridlstmapproach
AT huyan volatilityforecastsofstockindexfuturesinchinaandtheusahybridlstmapproach