Dynamic graph neural networks for enhanced volatility prediction in financial markets

Volatility forecasting is essential for risk management and decision-making in financial markets. Traditional models like Generalized Autoregressive Conditional Heteroskedasticity (GARCH) effectively capture volatility clustering but often fail to model complex, non-linear interdependencies between...

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Bibliographic Details
Published in:arXiv.org 2024-10
Main Authors: Pulikandala Nithish Kumar, Umeorah, Nneka, Alochukwu, Alex
Format: Article
Language:English
Subjects:
Artificial neural networks
Autoregressive models
Clustering
Dynamic structural analysis
Financial management
Graph neural networks
Graph theory
Graphs
International finance
Machine learning
Neural networks
Parameter sensitivity
Risk management
Securities markets
Stochastic models
Volatility
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Summary:Volatility forecasting is essential for risk management and decision-making in financial markets. Traditional models like Generalized Autoregressive Conditional Heteroskedasticity (GARCH) effectively capture volatility clustering but often fail to model complex, non-linear interdependencies between multiple indices. This paper proposes a novel approach using Graph Neural Networks (GNNs) to represent global financial markets as dynamic graphs. The Temporal Graph Attention Network (Temporal GAT) combines Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs) to capture the temporal and structural dynamics of volatility spillovers. By utilizing correlation-based and volatility spillover indices, the Temporal GAT constructs directed graphs that enhance the accuracy of volatility predictions. Empirical results from a 15-year study of eight major global indices show that the Temporal GAT outperforms traditional GARCH models and other machine learning methods, particularly in short- to mid-term forecasts. The sensitivity and scenario-based analysis over a range of parameters and hyperparameters further demonstrate the significance of the proposed technique. Hence, this work highlights the potential of GNNs in modeling complex market behaviors, providing valuable insights for financial analysts and investors.
ISSN:2331-8422