Paper: arXiv 2410.16858

Abstract

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.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced graph neural network theory and temporal attention mechanisms, indicating high math complexity, while the 15-year backtest on eight global indices with sensitivity analysis demonstrates strong empirical rigor.

Research Flowchart

  flowchart TD
  Start["Research Goal: Improve Volatility Prediction"] --> Method["Proposed Method: Temporal Graph Attention Network (GAT)"]
  Method --> Data["Data: 8 Major Global Indices (15 Years)"]
  Data --> Process["Process: Construct Dynamic Graphs using Correlation & Spillover Indices"]
  Process --> Compute["Compute: Apply GNNs to Capture Non-Linear Dependencies"]
  Compute --> Outcome["Outcome: Outperforms GARCH & ML Models"]
  Outcome --> Result["Result: Enhanced Accuracy for Short-Mid Term Forecasts"]

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1 of 50 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.