EBA欧洲银行-CSV-guide_8页_1mb

EBA Stress Test Dataset Summary

Core Content

The EBA (European Banking Authority) has developed a comprehensive dataset for stress testing, which is divided into three CSV files: Credit_risk.csv, Sovereign.csv, and Others.csv. These files contain bank-by-bank data from transparency templates and are designed to be used with analytical tools, including Excel and other software. The dataset includes approximately 12,000 data points per bank, with a total sample of 123 banks.

Main Features

• Three CSV Files: Each file corresponds to a specific category of stress test data.

• Data Categories:

  • Credit_risk.csv: Contains credit risk data, including impairment rates and exposure classes.
  • Sovereign.csv: Focuses on sovereign risk data.
  • Others.csv: Includes data on capital, RWA (Risk-Weighted Assets), P&L (Profit and Loss), securitization, and capital measures.

• Metadata and Data Dictionary: These files are provided to help users understand the structure and meaning of the data, including column descriptions and possible values for variables like Scenario and Country.

Key Information

CSV File Structure

Each CSV file contains the following common columns:

• Period: Time period (e.g., 201312, 201612).
• Item: Code of each variable.
• Label: Name of the item.
• Scenario: Code of the scenario (0, 1, 2, 3), with the following meanings:
  • 0: No breakdown by scenario
  • 1: Actual figures
  • 2: Baseline scenario
  • 3: Adverse scenario
• Amount: The value of the variable.
• LEI: A bank identifier.
• Name: Name of the bank.
• NSA: ISO code of the country of the bank.

Additional Columns in Credit Risk Dataset

• Country: Code of the counterparty's country.
• Country_rank: Ranking of the counterparty's country in terms of exposures.
• Exposure: Exposure class (e.g., Corporates, Retail).
• Portfolio: Regulatory portfolio (Standardized, Advanced, Foundation).
• Status: Status of the item.

Example 1: Capital Data

• Objective: Analyze CET1 ratio for each bank by scenario using a pivot table.
• Steps:
  1. Import the Others.csv file into Excel using the text import wizard.
  2. Use the metadata file to identify relevant items (e.g., "Common Equity Tier 1 ratio, %").
  3. Set up a pivot table by selecting Name, Period, and Scenario as row and column labels, and Amount as the value field.
  4. Aggregate the Amount values by sum to visualize the CET1 ratio across different scenarios and periods.

Example 2: Credit Risk Data

• Objective: Analyze impairment rates for Retail and Corporates exposures at the group level.
• Steps:
  1. Import the Credit_risk.csv file into Excel.
  2. Use the metadata file to filter the Label variable to "Impairment rate".
  3. Set up a pivot table by dragging Name and Exposure into the row label box.
  4. Filter the Exposure variable to select only the values corresponding to Corporates and Retail (e.g., 3 and 4).
  5. Drag Scenario and Period into the column label box to analyze impairment rates across scenarios and years.

Example 3: Credit Risk Data (Country Breakdown)

• Objective: Analyze impairment rates for Greek banks, broken down by counterparty country.
• Steps:
  1. From the pivot table in Example 2, remove the Country filter and drag it into the row label box under Name.
  2. Select the NSA variable and filter it to GR (Greece) to focus on Greek banks.
  3. The pivot table will then display impairment rates for each country of the counterparty under the adverse and baseline scenarios.

Summary

The EBA Stress Test dataset provides a structured and comprehensive set of tools and data for analyzing financial stress scenarios across European banks. The dataset is divided into three CSV files, each containing specific categories of data. Metadata and a data dictionary are included to aid in understanding the data structure and content. The dataset can be analyzed using Excel pivot tables, which allow users to filter, group, and visualize data across different scenarios, periods, and exposure classes. This facilitates a deeper insight into the financial resilience and risk exposure of banks under various stress conditions.