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Summary of "Identifying and Tracking Global, EU and Eurozone Systemically Important Banks with Public Data"

Core Content

This paper presents a methodology to identify and track systemically important banks (SIBs) at the global, EU, and Eurozone levels, based on the BCBS (2011) framework. The approach uses publicly available data to produce transparent and reliable SI scores, which are used to rank banks according to their systemic importance.

Main Objectives

• To identify European SIBs using the BCBS methodology.
• To provide insights into systemic importance through cross-sectional and dynamic analysis.
• To bridge the gap between market and regulatory information by relying on public data.

Key Motivations

• The global financial crisis highlighted the risks posed by SIBs, emphasizing the need for better identification and oversight.
• The EU and Eurozone banking systems are increasingly interconnected, necessitating a framework that accounts for this.
• The paper contributes to the "systemic risk" literature by analyzing systemic importance from both micro- and macro-prudential perspectives.

BCBS Methodology Overview

The BCBS approach evaluates systemic importance through five categories, each weighted at 20%:

1. Size (20%)
2. Interconnectedness (20%)
   • Intra-financial system assets (6.67%)
   • Intra-financial system liabilities (6.67%)
   • Total marketable securities (6.67%)
3. Substitutability (20%)
   • Assets under custody (6.67%)
   • Payments cleared and settled through payment systems (6.67%)
   • Values of underwritten transactions (6.67%)
4. Complexity (20%)
   • OTC derivatives notional value (6.67%)
   • Level 3 assets (6.67%)
   • Held for trading and available for sale value (6.67%)
5. Cross-jurisdictional Activity (20%)
   • Cross-jurisdictional claims (10%)
   • Cross-jurisdictional liabilities (10%)

Data and Methodology

• The dataset includes the largest 100 banks for each sample (global, EU, Eurozone).
• Foreign subsidiaries are included in EU and Eurozone samples, based on BCBS guidelines.
• Data sources include Bankscope, Dealogic, BIS International Banking Statistics, SNL Financial, etc.
• Some assumptions are necessary to align data with the BCBS categories, particularly in the case of complexity and cross-jurisdictional activity.

Key Findings

• The results are stable over time, with only minor changes in bank characteristics from 2010 to 2011.
• The list of G-SIBs closely matches the official FSB list, showing high reliability.
• 9 out of 35 EU-SIBs are foreign subsidiaries, mostly from the UK, indicating a growing need for oversight.
• In the Eurozone, foreign subsidiaries play a less prominent role, but their SI is still relevant for both domestic and EU regulators.
• The SI of Eurozone banks is shrinking, with non-Eurozone EU banks gaining more systemic importance.

Correlation Analysis

• Spearman and Kendall tau-b correlation coefficients show a strong relationship between the categories and systemic importance, with higher correlation in smaller samples.
• This suggests that the methodology is effective in ranking banks by SI, especially within the EU and Eurozone.

Herfindahl Index (HHI*)

• The normalized HHI indicates that systemic importance is more evenly distributed in larger markets.
• The index shows a decrease in global SI concentration and an increase in EU and Eurozone concentration, suggesting a shift in systemic risk distribution.

Recent Developments

• The methodology has been refined in BCBS (2013), allowing for more detailed analysis and inclusion of regional specifics.
• The extended dataset (2007–2012) reveals increasing SI for Asian banks and greater concentration in the EU.
• EU and Eurozone SIB lists remain stable over time, with some notable changes, such as the UK maintaining its lead and Germany’s SI score decreasing.

Further Steps

• The framework can be used to analyze the role of each category in systemic importance.
• It is well-suited for incorporating European-specific factors such as sovereign-bond holdings and market-making activities.

Selected Empirical Evidence

G-SIBs (2010–2011)

EU-SIBs (2010–2011)

EZ-SIBs (2010–2011)

Data Assumptions

• Substitutability: No reliable data for payment systems; only 7% of the overall score is affected.
• Complexity: OTC derivatives data is often not broken down, so overall derivatives are used. A correction factor is applied for banks following different accounting standards.
• Cross-jurisdictional Activity: Data is only available at the country level; a weighted function is used to allocate data to individual banks.