2011年-ECB欧洲央行_Systemic_Risk_Methodologies_8页_1mb

C SYSTEMIC RISK METHODOLOGIES Summary

Core Content

This document outlines three methodologies developed by the ECB for measuring and monitoring systemic risk in the financial system. These tools aim to assess different dimensions of systemic risk, including contagion, shared exposure to shocks, and the overall level of financial stress. The methodologies are grounded in quantitative analysis and econometric modeling, using financial and macroeconomic data to provide insights into the stability of the financial system.

Main Models and Their Purposes

1. Measuring Systemic Risk Contribution Using Multivariate Regression Quantiles

• Purpose: To estimate the extent to which individual financial institutions contribute to systemic risk.
• Methodology: Uses a vector autoregressive (VAR) model to assess the sensitivity of individual financial institutions' Value at Risk (VaR) to shocks in the financial system.
• Key Insight: The model applies multivariate regression quantiles to capture tail dependence and spillover effects. It provides a cross-sectional view of the financial system.
• Application: Analyzed 22 large EU banks. The model shows that the most systemically important banks experience significantly higher VaR increases from market shocks compared to the least systemically important ones.
• Chart C.1: Displays impulse responses of VaR for the most and least systemically important banks to a 1% stock market shock.
• Chart C.2: Shows the time evolution of the average VaR of these groups, highlighting the increase in systemic risk during the 2007-2009 financial crisis.
• Limitation: The model does not provide information on the location of stress at the firm level, making it a screening tool rather than a comprehensive risk assessment.

2. Coincident and Early Warning Indicators Based on Credit Risk Conditions

• Purpose: To capture shared exposure to common financial distress drivers and identify potential simultaneous failures of financial institutions.
• Methodology: Combines macro-financial data and credit risk data to estimate coincident and forward-looking indicators of financial stress.
• Key Insight: The model uses a large-dimensional factor model to infer joint failure probabilities and assess the correlation of defaults across firms.
• Application: Examines failure rates of over 800 financial firms across the US and EU, revealing a substantial risk of simultaneous failures, especially in 2010.
• Chart C.3: Plots the model-implied failure rate for financial firms, showing a correlation with macro-financial fundamentals.
• Chart C.4: Illustrates the probability of simultaneous failures across different thresholds, emphasizing the risk of systemic events.
• Early Warning Signal: Tracks deviations of credit risk conditions from macro-financial fundamentals, providing signals of potential distress.
• Chart C.5: Compares credit risk deviations across the US, EU, and the rest of the world, highlighting the pre-crisis and post-crisis divergence from fundamentals.

3. A Coincident Indicator of Systemic Stress (CISS)

• Purpose: To measure the current level of systemic stress in the financial system and condense it into a single composite statistic.
• Methodology: Applies standard portfolio theory to aggregate individual stress indicators from different financial market segments.
• Key Insight: The CISS accounts for cross-correlations among market segments, giving more weight to periods of widespread stress.
• Components: Aggregates five sub-indices representing key financial market segments: banks and non-bank financial intermediaries, money markets, equity and bond markets, and foreign exchange markets.
• Chart C.6: Displays the CISS for the euro area, showing its evolution from 1999 to 2011, with peaks during the 2007 financial crisis and the 2010 sovereign debt crisis.
• Limitation: The CISS does not incorporate firm-level data, so it cannot pinpoint the source of stress within individual institutions.
• Chart C.7: Illustrates the relationship between financial stress (CISS) and economic activity (industrial production growth), showing a clear negative correlation when the CISS exceeds a threshold of 0.36.
• Conclusion: The CISS provides a "horizontal" view of systemic stress (widespread instability) and a "vertical" view (costly impact on the economy).

Key Information

• Systemic Risk Forms: The document identifies three main forms of systemic risk: contagion, financial imbalances, and shared exposure to shocks.
• Data Sources: Relies on financial market data (e.g., stock prices, VaR), credit risk data, and macroeconomic indicators.
• Model Validation: The models are validated using historical data and compared to other indicators like EDF (Expected Default Frequency) and macroeconomic trends.
• Policy Implications: The tools help financial authorities monitor, identify, and respond to systemic risks more effectively, especially during crises.
• Limitations: All models have specific limitations, such as the lack of firm-level data in the CISS, which means they should be used in conjunction with other qualitative and quantitative assessments.

Conclusion

The ECB has developed three distinct models to assess systemic risk from different perspectives: individual institutional contribution, shared exposure to financial distress, and overall systemic stress. These models provide valuable tools for monitoring financial stability and can be used to support policy-making in the context of macro-prudential supervision. However, they should not be used in isolation and must be complemented by market intelligence and judgment. The financial crisis has underscored the need for such tools, and the ECB continues to refine and expand them to better understand and manage systemic risk.