Paper: arXiv 2408.05659

Abstract

We propose a novel data-driven network framework for forecasting problems related to E-mini S&P 500 and CBOE Volatility Index futures, in which products with different expirations act as distinct nodes. We provide visual demonstrations of the correlation structures of these products in terms of their returns, realized volatility, and trading volume. The resulting networks offer insights into the contemporaneous movements across the different products, illustrating how inherently connected the movements of the future products belonging to these two classes are. These networks are further utilized by a multi-channel Graph Convolutional Network to enhance the predictive power of a Long Short-Term Memory network, allowing for the propagation of forecasts of highly correlated quantities, combining the temporal with the spatial aspect of the term structure.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 6.0/10 (capped from 8.5: transaction costs not modelled; see penalty flags)
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced machine learning architectures (GCN-LSTM) and graph theory to model complex, high-dimensional temporal-spatial dependencies, which involves non-trivial mathematical formulations. It is also heavily data-driven, with explicit details on data preprocessing, graph construction, hyperparameter tuning, multiple evaluation metrics, and backtesting on real financial data (ES-mini and VX futures).

Research Flowchart

  flowchart TD
  A["Research Goal:<br/>Forecast ES & VX Futures"] --> B["Data Sources<br/>ES-mini & VX Futures"]
  B --> C["Network Construction<br/>Correlation-based Graph"]
  C --> D["Spatial-Temporal Modeling<br/>GCN-LSTM Hybrid"]
  D --> E{"Training & Validation"}
  E --> F["Key Findings"]
  
  subgraph F ["Outcomes"]
      F1["Enhanced Forecast Accuracy"]
      F2["Interpretable Term Structure"]
      F3["Capturing Cross-Market Dynamics"]
  end

  style A fill:#e1f5fe,stroke:#01579b,stroke-width:2px
  style F fill:#f1f8e9,stroke:#33691e,stroke-width:2px

Cited References (45)

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and 37 more
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0 of 45 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.