research-article

Multivariate Realized Volatility Forecasting with Graph Neural Network

Authors:

Christian-Yann RobertAuthors Info & Claims

ICAIF '22: Proceedings of the Third ACM International Conference on AI in Finance

Pages 156 - 164

https://doi.org/10.1145/3533271.3561663

Published: 26 October 2022 Publication History

Abstract

Financial economics and econometrics literature demonstrate that the limit order book data is useful in predicting short-term volatility in stock markets. In this paper, we are interested in forecasting short-term realized volatility in a multivariate approach based on limit order book data and relational stock market networks. To achieve this goal, we introduce Graph Transformer Network for Volatility Forecasting. The model allows combining limit order book features and a large number of temporal and cross-sectional relations from different sources. Through experiments based on about 500 stocks from S&P 500 index, we find a better performance for our model than for other benchmarks.

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Cited By

Zhang CPu XCucuringu MDong X(2025)Forecasting realized volatility with spillover effects: Perspectives from graph neural networksInternational Journal of Forecasting10.1016/j.ijforecast.2024.09.00241:1(377-397)Online publication date: Jan-2025
https://doi.org/10.1016/j.ijforecast.2024.09.002
Brini AToscano G(2024)SpotV2Net: Multivariate Intraday Spot Volatility Forecasting via Vol-of-Vol-Informed Graph Attention NetworksSSRN Electronic Journal10.2139/ssrn.4692194Online publication date: 2024
https://doi.org/10.2139/ssrn.4692194
Patel MJariwala KChattopadhyay C(2024)A Systematic Review on Graph Neural Network-based Methods for Stock Market ForecastingACM Computing Surveys10.1145/369641157:2(1-38)Online publication date: 10-Oct-2024
https://dl.acm.org/doi/10.1145/3696411
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Multivariate Realized Volatility Forecasting with Graph Neural Network
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cover image ACM Other conferences

ICAIF '22: Proceedings of the Third ACM International Conference on AI in Finance

November 2022

527 pages

ISBN:9781450393768

DOI:10.1145/3533271

Editors:
Daniele Magazzeni
J.P. Morgan AI Research
,
Senthil Kumar
Capital One
,
Rahul Savani
University of Liverpool
,
Renyuan Xu
University of Southern California
,
Carmine Ventre
King's College London
,
Blanka Horvath
University of Oxford
,
Ruimeng Hu
University of California Santa Barbara
,
Tucker Balch
J.P. Morgan AI Research
,
Francesca Toni
Imperial College London

Copyright © 2022 ACM.

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Published: 26 October 2022

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ICAIF '22: 3rd ACM International Conference on AI in Finance

November 2 - 4, 2022

NY, New York, USA

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Cited By

Zhang CPu XCucuringu MDong X(2025)Forecasting realized volatility with spillover effects: Perspectives from graph neural networksInternational Journal of Forecasting10.1016/j.ijforecast.2024.09.00241:1(377-397)Online publication date: Jan-2025
https://doi.org/10.1016/j.ijforecast.2024.09.002
Brini AToscano G(2024)SpotV2Net: Multivariate Intraday Spot Volatility Forecasting via Vol-of-Vol-Informed Graph Attention NetworksSSRN Electronic Journal10.2139/ssrn.4692194Online publication date: 2024
https://doi.org/10.2139/ssrn.4692194
Patel MJariwala KChattopadhyay C(2024)A Systematic Review on Graph Neural Network-based Methods for Stock Market ForecastingACM Computing Surveys10.1145/369641157:2(1-38)Online publication date: 10-Oct-2024
https://dl.acm.org/doi/10.1145/3696411
Lensky AHao M(2024)Learning to Predict Short-Term Volatility with Order Flow Image Representation2024 IEEE Conference on Artificial Intelligence (CAI)10.1109/CAI59869.2024.00154(817-822)Online publication date: 25-Jun-2024
https://doi.org/10.1109/CAI59869.2024.00154
Souto HMoradi A(2024)Can transformers transform financial forecasting?China Finance Review International10.1108/CFRI-01-2024-0032Online publication date: 20-Jun-2024
https://doi.org/10.1108/CFRI-01-2024-0032
Moreno-Pino FZohren S(2024)DeepVol: volatility forecasting from high-frequency data with dilated causal convolutionsQuantitative Finance10.1080/14697688.2024.238722224:8(1105-1127)Online publication date: 5-Sep-2024
https://doi.org/10.1080/14697688.2024.2387222
Souto HHeuvel SNeto F(2024)Time-mixing and Feature-mixing Modelling for Realized Volatility Forecast: Evidence from TSMixer ModelThe Journal of Finance and Data Science10.1016/j.jfds.2024.100143(100143)Online publication date: Oct-2024
https://doi.org/10.1016/j.jfds.2024.100143
Brini AToscano G(2024)SpotV2Net: Multivariate intraday spot volatility forecasting via vol-of-vol-informed graph attention networksInternational Journal of Forecasting10.1016/j.ijforecast.2024.11.004Online publication date: Dec-2024
https://doi.org/10.1016/j.ijforecast.2024.11.004
Souto H(2024)Charting new avenues in financial forecasting with TimesNetExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124851255:PDOnline publication date: 21-Nov-2024
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Souto HMoradi A(2024)Introducing NBEATSx to realized volatility forecastingExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122802242:COnline publication date: 16-May-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122802
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