2017年-世界发展银行全球_Deposit_Insurance_Systems___Addressing_Emerging_Challenges_in_Funding_Investment_Risk-Based_Contributions_and_Stress_Testing_168页_2mb

Summary of "Deposit Insurance Systems: Addressing Emerging Challenges in Funding, Investment, Risk-Based Contributions & Stress Testing"

Core Content

This document provides a comprehensive analysis of the challenges and methodologies related to deposit insurance systems, focusing on four key areas: funding, investment, risk-based contributions, and stress testing. It is designed to assist deposit insurers, particularly those in data-poor jurisdictions, in developing robust and forward-looking deposit insurance frameworks. The publication is part of a series by the Financial Sector Advisory Center (FinSAC) of the World Bank, aimed at strengthening deposit insurance systems in the Emerging Europe and Central Asia (ECA) region.

Main Topics and Key Points

1. Introduction to Deposit Insurance Systems

• A well-functioning deposit insurance system is essential for maintaining financial stability and depositor confidence.
• The Core Principles for Effective Deposit Insurance Systems (IADI, 2009) provide a minimum standard for deposit insurance schemes globally.
• The European Union's Deposit Guarantee Scheme Directive (2014/49/EU) introduced a "maximum harmonization" approach, including risk-based premiums, stress testing, and a minimum target level for deposit insurance funds.
• The document outlines a framework for deposit insurers to determine the Target Fund Ratio (TFR), which is the ratio of the deposit insurance fund balance to estimated insured deposits.

2. Determining the Target Deposit Insurance Fund

2.1. States of the World

• The framework identifies three key economic states:

  • Crisis periods (e.g., 1990:Q3 – 1991:Q4, 2008:Q1 – 2014:Q1)
  • Through-the-cycle periods (2006:Q1 – 2016:Q1)
  • Current periods (2014:Q1 – 2016:Q1)

• These states are identified using principal component analysis (PCA) on macroeconomic indicators such as GDP growth, unemployment, and nonperforming loans.

2.2. Loss Distribution Approach

• Many countries use historical data to estimate the loss distribution.
• This approach is backward-looking and may not reflect current or future conditions.
• It is particularly unsuitable for countries with limited data on bank failures.

2.3. Credit Portfolio Approach

• A more forward-looking method based on the Merton-Vasicek model.
• This model incorporates:
  • Probability of default (PD)
  • Loss given default (LGD)
  • Exposure at default (EAD)
  • Correlation of defaults
• The model allows deposit insurers to estimate expected losses in different states of the world using equation:

$$
\text{Expected Losses in state of world } t = PD_t \times LGD_t \times EAD_t
$$

• It is used in several countries including Nigeria, Zimbabwe, and the U.S.

3. Probability of Bank Failure

• The probability of default (PD) is a core component of the model and is composed of:

  • Credit failures (PDₐ)
  • Liquidity failures (PDₗ)
  • Systemic failures (PDₛ)

• Credit failures occur when a bank’s asset value falls below its liabilities, modeled using the Merton-Vasicek framework.

• Monte Carlo simulations are used to estimate PD based on random asset returns.

• The model assumes that asset returns are normally distributed, which simplifies the calculation of insurance losses.

4. Loss Given Default (LGD)

• LGD represents the percentage of a deposit that is lost in the event of a bank failure.
• The paper uses FDIC loss rates as a benchmark for LGD estimation.
• It also discusses failure resolution mechanisms, such as the priority of claimants and the structure of losses.

5. Exposure at Default (EAD)

• EAD is the total amount of deposits at risk in the event of a bank failure.
• The paper uses the U.S. case to illustrate EAD calculations, drawing from FDIC data.

6. Correlation of Bank Failures

• The correlation of defaults is an important factor in estimating the total deposit insurance losses.
• The Vasicek model is used to capture the relationship between obligors' asset values and the probability of default.
• Correlation of defaults is determined by the common systemic risk factor.

7. Model Calibration

• The model is calibrated using:
  • Probability of Default (PD): Estimated using statistical models, issuer default ratings, and industry failure rates.
  • Loss Given Default (LGD): Based on historical loss data and actuarial estimates.
  • Exposure at Default (EAD): Based on current deposit levels and historical trends.
  • Correlation of Default: Based on the common systemic risk factor.

8. Model Results

• The model generates desired fund sizes based on the level of risk tolerance.
• It considers longer horizons to account for different economic conditions and risk profiles.
• The results are sensitive to assumptions about low-probability, high-loss events.

9. Inherent Weaknesses of the Model

• The model assumes that the features of the financial safety net remain constant over time, which may not be the case.
• It is based on historical data, which may not reflect current or future economic and regulatory changes.
• The model may not fully account for systemic risks or moral hazard issues.

10. Conclusion

• The Credit Portfolio Approach is a robust and forward-looking method for determining the target deposit insurance fund.
• It allows for the incorporation of current economic conditions and banking sector risks.
• The framework is applicable to various deposit insurance systems, regardless of their mandate.
• The model is used to estimate expected losses and necessary fund levels, with the goal of ensuring prompt reimbursement and systemic stability.

Key Information

• Target Fund Ratio (TFR) is the main metric used to determine the adequacy of deposit insurance funds.
• Crisis periods are identified using PCA and break tests on macroeconomic data.
• Monte Carlo simulations are used to estimate the probability of bank failure and associated losses.
• The Merton-Vasicek model is central to the framework, providing a way to model credit and liquidity risks.
• The model is applied to U.S. data and adapted for use in other countries.
• The framework emphasizes transparency, data customization, and risk-based contributions.

Authors and Acknowledgements

• The paper is edited by Jan P. Nolte & Isfandyar Z. Khan.
• It is authored by John P. O’Keefe & Alexander B. Ufer.
• The Financial Sector Advisory Center (FinSAC) of the World Bank is responsible for this publication.
• The research was supported by the FIRST initiative for technical assistance projects in Nigeria and Zimbabwe.

References and Appendices

• Appendix A provides details on the Principal Component Analysis (PCA) used to identify economic states.
• The document references various studies and reports, including the Core Principles for Effective Deposit Insurance Systems (IADI, 2009) and the Deposit Guarantee Scheme Directive (2014/49/EU).

Key Takeaways

• The Credit Portfolio Approach is a preferred method for determining the target deposit insurance fund, especially for countries with limited historical data.
• The model integrates macroeconomic indicators, banking risks, and financial stability into a comprehensive risk assessment.
• Stress testing and contingency planning are essential components of a robust deposit insurance system.
• The framework is designed to be customizable, transparent, and data-driven.