Paper: arXiv 2401.03443

Abstract

In this paper, we employ Credit Default Swaps (CDS) to model the joint and conditional distress probabilities of banks in Europe and the U.S. using factor copulas. We propose multi-factor, structured factor, and factor-vine models where the banks in the sample are clustered according to their geographic location. We find that within each region, the co-dependence between banks is best described using both, systematic and idiosyncratic, financial contagion channels. However, if we consider the banking system as a whole, then the systematic contagion channel prevails, meaning that the distress probabilities are driven by a latent global factor and region-specific factors. In all cases, the co-dependence structure of bank CDS spreads is highly correlated in the tail. The out-of-sample forecasts of several measures of systematic risk allow us to identify the periods of distress in the banking sector over the recent years including the COVID-19 pandemic, the interest rate hikes in 2022, and the banking crisis in 2023.

Complexity vs Empirical Score

  • Math Complexity: 8.0/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced statistical methods like factor copulas and Variational Bayes inference, indicating high mathematical complexity, while its application uses extensive historical CDS data and out-of-sample forecasting for real-world risk measures, demonstrating strong empirical rigor.

Research Flowchart

  flowchart TD
  A["Research Goal: Model systemic risk of EU & US banks using CDS"] --> B["Methodology: Structured Factor Copulas<br>(Multi-factor, Factor-vine, Geographically clustered)"]
  B --> C["Data Input: Credit Default Swaps (CDS) spreads"]
  C --> D["Computational Process: Model Joint & Conditional Distress Probabilities"]
  D --> E{"Key Findings / Outcomes"}
  E --> F1["Systematic Contagion: Global & Region-specific factors drive distress"]
  E --> F2["Tail Dependence: High co-movement during extreme events"]
  E --> F3["Out-of-Sample Forecasts: Identified distress periods<br>(COVID-19, 2022 Rate Hikes, 2023 Banking Crisis)"]

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