Paper: arXiv 2610.37487

Authors: Minh Duc Duong, Alessio Moneta, Fabrizio Lillo, Caterina Giannetti, Tu Anh Doan Nguyen

Abstract

We study how contemporaneous equity-return transmission shapes systemic exposure among 51 listed European banks. Using a recursive non-Gaussian network model on volatility-standardized returns, we construct two market-capitalization-weighted measures based on direct and indirect transmission effects: the Systemic Impact Score (SIS), capturing a bank’s potential influence on other institutions, and the Systemic Vulnerability Score (SVS), capturing its exposure to disturbances originating elsewhere. The fitted network reveals distinct transmitting and receiving roles, highlighting differences between institutions with substantial outward influence and those with high incoming sensitivity. Receiving roles are generally more reproducible than transmitting roles across repeated network discovery, and selected connections remain informative about lower-tail dependence in subsequent observations. Volatility standardization substantially reduces detected dependence among recovered shocks, although the full-sample model does not satisfy the designated criterion for supporting shock independence. Alternative recursive orderings also yield comparable directional tail asymmetries, limiting the extent to which these patterns validate a particular ordering. The results provide complementary measures of model-implied systemic transmission and vulnerability, with explicit attention to uncertainty and identification limits

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 non-Gaussian network models (VAR-LiNGAM) for causal discovery in financial networks, demonstrating high mathematical sophistication. It applies these methods to real-world European banking data with careful consideration of statistical assumptions and robustness checks, indicating strong empirical rigor. The combination of these elements places it firmly in the ‘Holy Grail’ quadrant.

Research Flowchart

  flowchart TD
    A[Research Goal: Assess Contemporaneous Transmission & Systemic Risk in European Banking] --> B{Key Methodology: Recursive Non-Gaussian Network Model};
    B --> C[Data/Inputs: Equity Returns from 51 Listed European Banks];
    C --> D[Computational Process: Volatility Standardization & Network Construction];
    D --> E{Computational Process: Systemic Impact Score (SIS) & Systemic Vulnerability Score (SVS) Calculation};
    E --> F[Key Findings: Distinct Transmitting & Receiving Roles, Reproducible Receiving Roles, Informative Connections for Tail Dependence, Tail Asymmetries];