Econometric Measures of Connectedness and Systemic Risk in theFinanceand Insurance Sectors

Paper: SSRN 1963216

Abstract

We propose several econometric measures of connectedness based on principal-components analysis and Granger-causality networks, and apply them to the monthly re

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 employs advanced econometric methods including principal-components analysis and Granger-causality networks, which are mathematically dense. It also applies these measures to real-world financial and insurance sector data for systemic risk assessment, demonstrating strong empirical backing.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Quantify Systemic Risk<br>in Finance & Insurance"] --> B["Data Input<br>Monthly Returns: Equities"]
  B --> C["Methodology 1<br>Principal Components Analysis"]
  B --> D["Methodology 2<br>Granger-Causality Networks"]
  C --> E["Computational Process<br>Measure Factor-Based Connectedness"]
  D --> F["Computational Process<br>Estimate Causal Linkages"]
  E --> G["Key Outcomes<br>Network Density & Volatility Metrics"]
  F --> G