EBA欧洲银行-CSV_guide_Transparency_2018_5页_436kb

EBA 2018 EU-wide Transparency Exercise Summary

Core Content

The European Banking Authority (EBA) conducted the 2018 EU-wide transparency exercise, which aimed to provide detailed and transparent data on the financial health and risk exposure of banks across the European Union (EU) and the European Economic Area (EEA). The dataset includes bank-by-bank information from 130 banks in 25 countries, along with aggregated data for the 'All other banks' bucket to facilitate EU-wide reconciliation.

The dataset is organized into four CSV files, each corresponding to specific transparency templates. These files contain a total of more than 7,000 data points per bank, grouped into categories that reflect the content of the transparency templates.

Main Views

• Purpose of the Transparency Exercise: To enhance transparency and provide detailed data for regulatory and analytical purposes.
• Scope of Data: Includes data from 130 banks across 25 EU/EEA countries, plus aggregated data for the 'All other banks' bucket.
• Data Structure: The dataset is divided into four CSV files, each representing a specific set of transparency templates.
• Tools and Resources: The EBA provides practical tools to assist users in working with the data, including interactive maps, Excel aggregation tools, and CSV datasets for import into analytical software.
• Metadata and Data Dictionary: Users are provided with metadata and data dictionary files to understand the database structure, column definitions, and variable explanations.

Key Information

CSV Files and Templates

CSV File Name	Transparency Template(s)
Credit risk	Credit Risk_STA, Credit_Risk_IRB, NPE, Forborne Exposure
Market risk	Market Risk
Sovereign exposures	2017 Sovereign, 2018 Sovereign
Other templates	Capital, Leverage, Risk Exposure Amount, P&L

Dataset Structure

Each CSV file contains the following columns:

• Lei_code: A unique identifier for the bank.
• NSA: ISO code of the bank's country.
• Period: Time period (e.g., 201712 for December 2017, 201806 for June 2018).
• Item: Code of each variable.
• Label: Decodification of the item, providing a human-readable description.
• Amount: The actual value of the variable.
• N_quarters: The number of quarters to which P&L data refers.
• Footnote: Specific clarifications provided by the bank in its PDF report, applicable to relevant items.

Metadata and Data Dictionary

• Metadata File (TR_Metadata.xlsx): Contains explanations of the values each column can assume, including:

  • Perf_status (Performance status):
    • 0: No breakdown by Perf_status
    • 1: Performing
    • 2: Non Performing
    • 3: Performing but past due >30 days and <=90 days
    • 4: Non Performing and Defaulted

• Data Dictionary File (TR Data dictionary.xlsx): Provides additional decoding information for the dataset.

Example: Using the Dataset

1. Download and Import: Users can download the CSV file for the 'Capital' category (tr_oth.csv) and import it into Excel using the Text Import Wizard.
2. Set Up Pivot Table:
   • Drag 'LEI_code' into the 'Row Labels' box.
   • Drag 'Period' into the 'Column Labels' box.
   • Drag 'Label' into the 'Report Filter' box and select 'Common Equity Tier 1 Capital Ratio (fully loaded)'.
   • Drag 'Amount' into the 'Values' box and aggregate by sum.
3. Interpretation: The final pivot table will display the CET1 Ratio for each bank by period, enabling users to analyze capital adequacy across different time frames and institutions.

Additional Resources

• The 'Banks' sheet in the metadata file provides the bank name corresponding to each LEI code, along with other bank properties such as the country of origin and financial year end.

This transparency exercise is a valuable resource for stakeholders seeking to analyze banking data at the EU level, offering both detailed and aggregated insights.