Paper: arXiv 2408.12839
Abstract
Granger causality can uncover the cause and effect relationships in financial networks. However, such networks can be convoluted and difficult to interpret, but the Helmholtz-Hodge-Kodaira decomposition can split them into a rotational and gradient component which reveals the hierarchy of Granger causality flow. Using Kenneth French’s business sector return time series, it is revealed that during the Covid crisis, precious metals and pharmaceutical products are causal drivers of the financial network. Moreover, the estimated Granger causality network shows a high connectivity during crisis which means that the research presented here can be especially useful to better understand crises in the market by revealing the dominant drivers of the crisis dynamics.
Complexity vs Empirical Score
- Math Complexity: 8.5/10
- Empirical Rigor: 7.0/10
- Quadrant: Holy Grail — high math complexity, high empirical rigor
Why this score: The paper employs advanced mathematical concepts like Granger causality, Helmholtz-Hodge-Kodaira decomposition, and PCA, with dense derivations, but is grounded in a specific financial dataset (Kenneth French’s 49 sectors) with detailed estimation procedures and empirical results during the Covid crisis.
Research Flowchart
flowchart TD Start["Research Goal: Analyze the hierarchical structure of causality in financial markets during the Covid crisis"] --> InputData["Input: Kenneth French business sector return time series"] InputData --> GC["Methodology: Construct Granger Causality Network"] GC --> Decomp["Computational Process: Helmholtz-Hodge-Kodaira Decomposition"] Decomp --> Comp1["Gradient Component (Potential Flow)"] Decomp --> Comp2["Rotational Component (Cyclic Flow)"] Comp1 --> Hierarchy["Output: Causal Hierarchy Extraction"] Comp2 --> Hierarchy Hierarchy --> Findings["Key Findings: Precious metals & pharmaceuticals as causal drivers; High network connectivity during crisis"]
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