EBA欧洲银行-A2-I.-van-Lelyveld-Discussion_21页_464kb

BSLoss: A Comprehensive Measure for Interconnectedness

Core Content

BSLoss is introduced as a comprehensive measure to assess the interconnectedness of banks, particularly in the context of systemic risk and shock transmission. The paper emphasizes the importance of understanding how financial distress in one institution can propagate through the network to others, highlighting the role of counterparty risk and the dynamic nature of financial networks.

Main Points

1. Loss Realisation and Shock Transmission

• Chain of Effects: An increase in the probability of default (PD) of one bank (PDₙ) leads to an increase in the loss given default (LGD) of another bank (LLAᵢ), which in turn reduces the total assets (TAᵢ) and capital ratios (Tier1/RWAᵢ), ultimately increasing the probability of default for the latter bank (PDᵢ).
• Expectation-Based Model: This chain of effects is considered in expectation, not as a deterministic process.
• Default Probability Formula:
  $$
  Pr(default) = F(\alpha + \beta \ln(CapRat) + \gamma X)
  $$
  Where $ X $ includes efficiency, profitability, liquidity, and size. The model also considers sector-specific factors and the impact of (government) intervention or market stress.

2. Static vs. Dynamic Networks

• Static Networks: Traditional models assume fixed network structures, which may not reflect real-world conditions.
• Dynamic Networks: Structures change over time, and dynamic models are necessary to capture the evolving nature of financial systems.
• Key Studies: Squartini et al. (2013) and Halaj and Kok (2014) highlight the importance of dynamic network analysis. Brauning et al. (2014) also contribute to this field with agent-based models.

3. Comparison with Traditional Contagion Models

• Old Style Domino Contagion: Traditional models may not fully capture the complexity of modern financial systems.
• Question Raised: Does the old style domino contagion model produce the same results as the BSLoss framework?
• Reference: Upper and Worms (2004) are cited in this context.

Key Information

Network Structure Overview

• Netherlands: 100 banks, network density 8%, average core banks ±15, average core size ±15%
• Germany: 1800 banks, network density 0.4%, average core banks ±45, average core size ±2.5%
• Italy: ±120 banks, network density ±15%, average core banks ±30, average core size ±25%
• UK: 176 banks, network density 3.2%, average core banks 16, average core size 9.1%

Data and Algorithms

• Number of Jurisdictions: 12 jurisdictions including BIS, Brazil, Canada, Denmark, France, Germany, Hungary, Korea, Mexico, Netherlands, UK, and US.
• Network Types: Payments, interbank, repo, and CDS networks.
• Algorithms: Six algorithms are used for network reconstruction and analysis:
  • Anand et al. (2013)
  • Baral and Fique (2012)
  • Battiston et al. (2012)
  • Drehmann and Tarashev (2013)
  • Mastrandrea et al. (2014)
  • Others (not fully listed)

Research Contributions

• BCBS Research Task Force: Focuses on liquidity stress testing and the use of networks in assessing systemic risk.
• Agent-Based Models: Used to simulate and analyze the behavior of financial networks under stress.
• Interbank Tiering: Craig and von Peter (2014) discuss the structure of interbank markets and the role of money center banks.

References

• Adrian, T. and H. S. Shin (2010): "The Changing Nature of Financial Intermediation and the Financial Crisis of 2007-09"
• Aikman, D., P. G. Alessandri, B. Eklund, P. Gai, S. Kapadia, E. Martin, N. Mora, G. Sterne, and M. Willison (2009): "Funding Liquidity Risk in a Quantitative Model of Systemic Stability"
• Anand, K., B. Craig, and G. von Peter (2013): "Filling in the Blanks: Interbank Linkages and Systemic Risk"
• Baral, P. and J. P. Fique (2012): "Estimation of Bilateral Exposures - A Copula Approach"
• Battiston, S., M. Puliga, R. Kaushik, P. Tasca, and G. Caldarelli (2012): "Debrank: Too Central to Fail? Financial Networks, the Fed and Systemic Risk"
• Brauning, F., F. Blasques, and I. van Lelyveld (2014): "A Dynamic Stochastic Network Model of the Unsecured Interbank Lending Market"
• Craig, B. and G. von Peter (2014): "Interbank Tiering and Money Center Banks"
• Drehmann, M. and N. Tarashev (2013): "Measuring the Systemic Importance of Interconnected Banks"
• Fricke, D. and T. Lux (2012): "Core-Periphery Structure in the Overnight Money Market"
• Squartini, T., I. van Lelyveld, and D. Garlaschelli (2013): "Early-Warning Signals of Topological Collapse in Interbank Networks"
• Upper, C. (2011): "Simulation methods to assess the danger of contagion in interbank markets"
• Upper, C. and A. Worms (2004): "Estimating Bilateral Exposures in the German Interbank Market"
• Van den End, J. W. (2008): "Liquidity Stress-Tester: A macro model for stress-testing banks liquidity risk"
• Van der Leij, M., C. Hommes, and D. in 't Veld (2014): "The formation of a core-periphery structure in financial networks"
• Van Lelyveld, I. and F. Liedorp (2006): "Interbank Contagion in the Dutch Banking Sector: A Sensitivity Analysis"