Paper: arXiv 2404.14337

Authors: Agathe Sadeghi, Zachary Feinstein

Abstract

In this paper, we introduce an impact centrality measure to evaluate shock propagation on financial networks capturing a notion of contagion and systemic risk contributions, permitting comparisons of these risks over time. In addition, we provide a statistical validation method when the network is estimated from data, as is done in practice. This statistical test allows us to reliably assess the computed centrality values. We validate our methodology on simulated data and conduct empirical case studies using financial data. We find that our proposed centrality measure increases significantly during times of financial distress and is able to provide insights into the (market implied) risk-levels of different firms and sectors.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a novel impact centrality measure for financial networks alongside a robust statistical validation framework for estimated graph structures. It combines rigorous network science mathematics with solid empirical case studies and simulation data, effectively addressing systemic risk monitoring.

Research Flowchart

  flowchart TD
    A[Research Goal: Quantify & Validate Systemic Risk / Contagion in Financial Networks] --> B[Data Inputs: Multi-Asset Financial Market Data]
    B --> C[Estimate Financial Network & Calculate Impact Centrality Measure]
    C --> D[Develop Statistical Validation Method for Centrality under Estimation Uncertainty]
    D --> E[Testing: Synthetic Simulations & Empirical Financial Case Studies]
    E --> F[Findings: Measure spikes during distress & reliably quantifies firm/sector risk levels]

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1 of 156 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.