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Overview

Overview. Introduction Micro-databases in the Banco de Portugal SSIS – Securities Statistics Integrated System database CCR – Central Credit Register database CBSD – Central Balance Sheet Database Prudential supervision data (micro-data) Quality control

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Overview

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  1. Overview • Introduction • Micro-databasesin the Banco de Portugal • SSIS – Securities Statistics Integrated System database • CCR – Central Credit Register database • CBSD – Central Balance Sheet Database • Prudential supervision data (micro-data) • Quality control • Conclusions and challenges ahead

  2. Introduction • Ensure high quality and timely statistical production • Statistical dissemination fully meeting users’ needs • Financial innovation and globalization highlighted the shortcomings of the current statistical framework and created new challenges • Discussion on how collecting systems may reduce the reporting burden without affecting the quality and the statistical coverage • The use of micro-databases and item-by-item reporting as an alternative to conventional data collecting systems

  3. Introduction Advantages of micro-databases and item-by-item reporting: • reduce significantly the response burden • prevent data redundancy • enable a more efficient data quality management • improve the responsiveness to ad hoc information requests

  4. Micro-databasesin Banco de Portugal Banco de Portugal has been increasingly using the following micro-databases and item-by-item reporting: • SSIS - Securities Statistics Integrated System database • CCR – Central Credit Register database • CBSD – Central Balance Sheet Database • Prudential supervision data (micro-data)

  5. The SSIS- Securities Statistics Integrated System database • SSIS was launched in 1999 • Three former different reporting systems were replaced: • the MFI’s securities portfolio in the context of money and banking statistics • in the case of BoP/IIP report, residents’ portfolio in securities issued by non-residents and non-residents’ portfolio in securities issued by residents • securities’ issues statistics

  6. The SSIS With SSIS: • answer effectively to the several statistical requirements in the domain of securities statistics • regular reporting of statistical data to several international organizations, as for instance the Centralized Securities Database (CSDB) • answer to several statistical enquiries of the ECB, IMF and Eurostat

  7. The SSIS • Database managed by the Statistics Department • Report on a s-b-s and investor by investor basis (except for households) • Reporting institutions (for portfolios) – MFI, Dealers, Brokers and other residents with securities held outside the resident financial sector • Issues and portfolios (own and customers’ when applicable) in a single database • Monthly report with a lag of 12 working days

  8. The SSIS • Securities concept – ESA 95 - securities other than shares (short and long term) and shares and other equity (financial derivatives are not included) • Securities identification: • ISIN code • a built code (according to pre-fixed rules) • Investors identification: • Residents: the company registration number • Non-residents: a code provided by the reporting institution

  9. The SSIS • Information reported: • flows (purchases and sales) • stocks (end-of-period-values) • Valuation method: • flows - transactions value • stocks - (i) market value, (ii) acquisition value and (iii) nominal value

  10. The SSIS The information reported refers to: • Issues by resident entities in Portugal • Residents’ portfolios in domestic and external securities • Non-residents’ portfolios in domestic securities

  11. The SSIS Some figures: • About 45 000 shares, 75 000 debt securities and 5 000 mutual funds shares/units are stored • Above 154 000 entities (issuers and/or investors) are registered • Over 200 000 registers relating to portfolio data (flows and stocks) are processed monthly

  12. The CCR – Central Credit Register database • An administrative database storing credit-related information supplied by the participants (financial institutions that grant credit) for their assessment of the risks attached to granting/extending credit • Database managed by the Statistics Department • Information received on an individual basis, but fulfilling all the legal requirements concerning the protection of individual data

  13. The CCR Use of CCR data for other purposes: • Statistics (e.g., business register, data quality control, complementary data, separate statistical outputs) • Banking supervision and regulation (e.g., assessment of credit risk and concentration of risk exposures both, at micro and macro level, improvement of on-site inspection practices) • Economic research and policy

  14. The CCR On a monthly basis, all participants report information on all its borrowers’ credit, with a minimum threshold of 50 €, mainly: • Amounts outstanding of loans granted to individuals and entities, broken down by type and purpose (interbank market balances are excluded) • Potential liabilities (e.g., unused amounts of credit cards and open credit lines) • Type or value of collateral or guarantee securing the loan • Original and residual maturity

  15. The CCR (cont.) • Securitized loans, syndicated loans, loans used to back mortgage bonds, etc. all identified separately • Credit defaults • Number of days loan is past due (in case of default) • Currency • Country where the loan was granted (to cover loans granted to residents by foreign branches of Portuguese credit institutions)

  16. The CCR Advantages of the use of CCR for statistical purposes: • reduction in the respondents’ reporting burden • improvement in the quality of MFI and OFI balance sheet statistics • enabling an enhanced quality control • additional breakdowns to the existing statistics (e.g., by type, purpose, institutional sector, branch of economic activity, regions and size) • greater accuracy in the classification of the reporting entities by institutional sector • better assessment of credit developments • useful for meeting the new ECB requirements on securitization without increasing the reporting burden on Financial Vehicle Corporations (FVCs)

  17. The CCR Some figures: • 5.6 million private individuals are registered • over 280 thousand corporations are registered • 216 participants, covering all the credit-granting financial institutions • 15 types of financial products • 20.5 million records per month, on average

  18. The CBSD – Central Balance Sheet Database Prior to 2007 • Managed by the Banco de Portugal • Established in 1983 (on a voluntary basis) • In 2000 (after several improvements): • it started covering all sectors of economic activity • a sampling method was introduced

  19. The CBSD As from 2007 • Data reported under the Simplified Corporate Information (SCI) – allowing to fulfil four reporting obligations, in a single electronic form, and at one only moment in time • The SCI (IES in Portuguese) is a joint electronic submission of accounting, fiscal and statistical information by companies to: • (i) the Ministry of Finance • (ii) the Ministry of Justice • (iii) the Statistics Portugal • (iv) the Banco de Portugal

  20. The CBSD With the launch of CBSD the overall social costs were reduced: • Banco de Portugal decided to discontinue the CBSD annual survey and to reduce the surveys on direct investment • Similarly, Statistics Portugal stopped surveying companies on annual data included in IES Covers, besides the financial sector, the whole set of non financial corporations (more than 350 000 companies)

  21. The CBSD Data reported are within 6 months after the end of the economic year of the company (accounting year) Data cover 1600 items (on a mandatory basis): • basic identifying information • comprehensive accounting data (balance sheets and income statements) on an unconsolidated basis • additional data for statistical and fiscal purposes (in the case of non-financial corporations)

  22. The CBSD Currently the CBSD information is largely used in several domains: • Aggregate statistics on non-financial corporations • Contributions of Portugal to the international databases BACH and ESD • Enterprise and Sector Tables reported to companies • Several items for the compilation of Financial Accounts (trade credits, own funds for non-listed companies, inter-company loans, pension funds, loans granted by private shareholders) • Several items for BoP/IIP compilation (external trade services, trade credits, direct investment, loans granted by foreign credit institutions) • Contributions to update the Business Register

  23. Prudential supervision data (micro-data) • The Statistics Department of the Banco de Portugal has full access to the accounting data submitted for supervisory purposes to the Supervision Department • These data are used for: • quality control of MFI statistics • for the compilation of non MFI statistics (these institutions do not report to the Banco de Portugal data for statistical purposes)

  24. Quality control The use of micro-databases and item-by-item reporting enables a more efficient data quality management: • The nature of the information (in particular accounting data) requires, very often, to be previously approved or certified • All these databases are managed by the Statistics Department of Banco de Portugal (and also MFI report for money and banking statistics and the report for BoP/IIP purposes) • More efficient use of cross checking among different sources • Cross checking performed on elementary data and/or aggregated data • Reporters may be approached for clarification • The compilation of Financial accounts (also under the responsibility of Statistics Department of Banco de Portugal) is the overall cross-checking test

  25. Quality control “Quality Assessment Reports” are prepared on a regular basis: • on the elementary statistical data submitted by the major resident financial groups (covering about 75 percent of the statistical results compiled) • covering the report for monetary and financial statistics, balance of payments statistics, securities statistics and central credit register • high level periodical statistical meetings are held with those responsible for the production of statistical information within these financial institutions

  26. Quality control The following cross-check analyses are performed: • MFI report to money and banking statistics (MBS) versus accounting data (loans vis-à-vis the non-monetary sector, securities portfolio and deposits and securities issued) • MFI report to MBS versus report for Central Credit Register (loans granted by MFI to the non-monetary sector) • MFI report to MBS versus data collected/reported for securities statistics (MFI’s securities issued and MFI’s portfolios) • Securities portfolios versus securities issues • MFI report to MBS versus report for BoP statistics (external assets and liabilities)

  27. Quality control The results of this work have been quite positive: • enhancing the overall consistency of the statistics • impacting, sometimes, the organizational arrangements of the financial institutions themselves

  28. Conclusions and challenges ahead Conclusions: The Statistics Department of Banco de Portugal has been making good use of the advantages entailed by the use of micro-databases and item-by-item reporting: • reducing respondents’ burden • enhancing quality control • cross checking elementary/raw data • taking advantage of the centralised management of these databases • improving responsiveness to ad hoc information requests • developing appropriate software and friendly interfaces in order to explore efficiently the stored information • deepening the coordination among all staff in the Statistics Department, which was translated into a more cooperative work

  29. Conclusions and challenges ahead Challenges ahead: • To continue improving the overall efficiency of the statistical framework by further exploring the still unused statistical potential of the already existing micro-databases • To set up flash indicators based on information resident (already existent for business’ purposes other than statistics) provided by some representative corporations in key economic sectors, which might contribute to improve the economic/financial analysis, in particular the ability to anticipate macro risks based on micro data assessment • To enlarge the feedback information currently produced and delivered to the reporting entities

  30. THANK YOUFOR YOUR ATTENTION!

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