## Foreign Credit Exposure Measure

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---

### Introduction
- The global crisis demonstrated rapid cross-border propagation of shocks; balance sheet linkages among financial institutions and markets determine spillover size and direction.
- Identified high-priority areas where data enhancements are most needed to measure global systemic risk.
- National supervision has access to granular domestic bank data, but:
  - Much of the data needed to identify and track international linkages is not available.
  - Institutional infrastructure for global systemic risk management is inadequate or non-existent.
- The paper highlights four illustrative data-related challenges and summarizes international initiatives to address data gaps.

### Key structural challenges to global systemic risk measurement
- (i) Lack of institutional mechanisms which ensure coordination of national approaches:
  - Authorities in each jurisdiction pursue national objectives that do not necessarily maximize global welfare.
  - Supervision of large, internationally-active financial institutions is dispersed among agencies in many countries, with imperfect information sharing and limited coordination tools.
  - A global framework for the resolution of internationally-active institutions is lacking.
  - There is no global formal lender of last resort for foreign-currency liquidity.
- (ii) Greater complexity in the international context:
  - Large global banks have complex organizational structures (thousands of entities across countries), active via cross-border lending, subsidiaries, and branches.
  - Group-level consolidated data can be misleading because they assume resources are freely transferable across locations and may mask differences between branches and subsidiaries.
  - Consolidated data make it difficult to compare global exposures to a bank group's capital accurately.
- (iii) Scarcity of data that capture international dimensions of systemic risk:
  - Supervisors have granular jurisdictional data but collection practices differ across jurisdictions and confidentiality restricts data sharing.
  - Publicly available bank-level data generally lack consistent information about international activities (cross-currency and cross-border positions).
  - BIS international banking statistics are aggregated across banks and do not provide the detailed breakdowns often needed for analysis.

### Literature on systemic risk assessment
- Category 1 — Balance-sheet linkage analyses:
  - Focus on how balance sheet linkages amplify shocks and influence cross-border propagation.
  - Rely mainly on aggregate banking data (BIS, CPIS, balance of payments).
- Category 2 — Market-data-based indicators:
  - Use high-frequency equity prices, CDS spreads, bond spreads to infer systemic risk premia and correlations across markets.
  - Market data complement balance-sheet studies but are contemporaneous measures of stress and may not be reliable leading indicators.
- Category 3 — Simulations and scenario analysis:
  - Forward-looking simulations using balance-sheet interconnections to trace contagion through interbank linkages and spillovers to non-bank sectors.
  - Often use network measures and multilateral matrices (e.g., Leontief-type input-output matrices) based on BIS consolidated statistics to perform scenario analyses including rounds of asset and funding shocks.

### Four illustrative data challenges in measuring systemic risks internationally
- Key input: BIS international banking statistics (IBS) — aggregated at national banking system level, covering worldwide consolidated exposures by counterparty country, sector, and currency, but not bank-level detail.
- A. Measurement of banks’ foreign credit exposures
  - A banking system i’s foreign credit exposure to country j comprises:
    1. direct cross-border exposures to borrowers in country j booked by all offices of banking system i located outside of country j,
    2. effective exposures via the local positions booked by bank i’s subsidiaries and branches located in country j,
    3. all off-balance sheet exposures (derivatives, credit guarantees, and credit commitments) related to borrowers in country j.
  - Legal treatment differs: a parent bank’s exposure to a subsidiary in country j is legally limited to the subsidiary’s capital plus any non-equity funds provided by the parent; losses on branch exposures are most often fully absorbed by the bank.
  - BIS consolidated banking statistics (CBS) on an ultimate risk basis track banking system i’s foreign claims on borrowers in country j, including worldwide consolidated direct cross-border claims, but have limitations for detailed, bank-level exposure assessment.

### Data limitations highlighted by examples and implications
- Aggregate country-level data overlook bank-level heterogeneity.
- Commercial bank-level data lack borrowers’ country-location and sector detail.
- Syndicated loan data provide only partial creditor participation shares (typically less than half of the total syndicated loan amount).
- Intragroup activity analyses are often based on indirect evidence (subsidiaries’ lending levels) rather than direct measures of intragroup flows.
- Maximum-entropy assumptions for reconstructing interbank matrices can produce materially different conclusions from other assumptions when bilateral data are unavailable; this is especially problematic in global contexts where samples of large global banks are small.

### Current data initiatives and gaps
- The ongoing G20 initiative to close data gaps set forth "20 recommendations" calling for:
  - Improvements to bank-level and aggregate statistics,
  - A framework for collection and sharing of these data across jurisdictions,
  - Rules governing access and use of the data.
- Recommendations emphasize need for more bank-level data including firm-level bilateral linkages, banks’ organizational structures, and breakdowns of assets and liabilities by instrument, counterparty-country, counterparty-sector, currency, and residual maturity.
- Enhancements to aggregate BIS international banking statistics are progressing to better illuminate how banks organize operations across jurisdictions.
- Together, these enhancements aim to provide public-good financial data fundamental to multilateral surveillance and global perspectives on financial stability concerns.

### BOX 1 — The BIS International Banking Statistics (key points)
- BIS IBS consist of BIS Consolidated Banking Statistics (CBS) and BIS Locational Banking Statistics (LBS).
- CBS:
  - Track banks’ worldwide consolidated gross claims and other exposures to individual countries and sectors.
  - Foreign claims = Cross-border claims (A) + Local claims in foreign currency (B) + Local claims in local currency (C).
  - CBS reported on Immediate Borrower (IB) and Ultimate Risk (UR) bases; off-balance sheet items are separately reported in CBS (UR).
  - Banks net out intergroup positions and consolidate across offices; information on branch/subsidiary structure is not included.
- LBS:
  - Residence-based; track cross-border positions and local positions in foreign currencies of banks located in a particular country.
  - Breakdown: by currency, sector, country of residence of counterparty, and nationality of reporting banks; record gross (unconsolidated) positions including vis-à-vis own affiliates.
  - Important for currency composition of banks’ balance sheets and aggregating to banking-system nationality level.
- Measurement of borrower’s reliance on foreign bank credit:
  - Combining BIS data with bank-level data permits adjustment for local funding (e.g., subsidiaries’ local customer deposits).
  - As of September 2010, the adjusted lower-bound measure is, on average, about 10 percent below the upper-bound gross foreign claims measure.
  - Regional patterns: Latin-America adjusted measure is, on average, only 40 percent of banks’ foreign claims; emerging Asia and Europe roughly half; advanced countries 65 percent.
  - Countries with higher adjusted-to-gross ratios saw greater contraction in total foreign funding during the crisis (Dec 2007 – Sep 2010).
- Measurement of cross-currency funding and maturity transformation:
  - Pre-crisis many non-US banks invested heavily in US dollar assets and relied on short-term US dollar funding via interbank borrowing and currency swaps; difficulties rolling over dollar funding increased dollar funding costs.
  - Lehman collapse (September 2008) forced reliance on central bank swap lines to meet dollar funding demand.
  - BIS data provide indirect information on dollar funding needs; only broad tendencies can be identified as residual maturities and use of FX swap markets are not directly observed.
- Modeling systemic risks:
  - IMF cross-border bank contagion scenario module: starts from asset credit exposures, differentiates losses on cross-border claims, affiliates’ claims, and off-balance sheet exposures; captures propagation through bank losses, funding shocks and deleveraging.
  - Scenario rounds include asset-loss depletion of capital, deleveraging to restore a capital threshold (example: Basel III Tier I capital asset ratio), funding shock rounds, and potential public recapitalization simulations.
  - Data constraints: lack of detailed, consistent bank-level bilateral linkage data limits model precision; treating an entire banking system as a single bank is a costly implicit assumption.

### BOX 2 — G-20 Data Gaps Initiative (key points)
- Selected recommendations most related to this paper:
  - R.4: Development of system-wide macro-prudential risk measures (aggregate leverage, maturity mismatches).
  - R.8 and R.9: Common data template for globally systemically important financial institutions (G-SIFIs).
  - R.11: Enhance BIS consolidated banking statistics, including separate identification of non-bank financial institutions and tracking funding patterns.
  - R.14: Standardized template covering international exposure of large non-bank financial institutions.
- Efforts underway:
  - Draft common reporting template for bank-level data to provide breakdowns by counterparty country and sector, instrument, currency, and remaining maturity.
  - Consideration of intragroup positions and counts of branches/subsidiaries.
  - Framework for collection, storage, and controlled access to highly confidential bank-level data (IMF-FSB, 2011); decisions expected in late 2011 (subject to consultation with FSB Plenary).
- Enhanced BIS CBS aims to:
  - Provide more information on currency of banks’ positions, counterparties’ location and sector, extend coverage to entire balance sheets, and broaden to capture all banks’ financial assets/liabilities including local currency positions vis-à-vis residents.
  - Timing: Starting in late 2012, information on the country location of banks’ counterparties should be available separately for banks of a particular nationality in each reporting jurisdiction.
- Benefits if initiatives proceed:
  - Permit joint analyses of global positions of many banks from different jurisdictions; improve detection of vulnerabilities in common exposures or concentrated funding positions.
  - Provide information to assess spillovers from failure of particular institutions and to evaluate regulatory responses (e.g., ring-fencing).
  - Facilitate more realistic modeling of asset and funding interactions during stress.
- Limitations and wider scope:
  - Even with improved banking statistics, non-bank institutions (pension funds, insurance companies, large multinational corporations) may remain inadequately covered; include non-bank institutions in counterparty sector breakdowns and bring large non-bank firms into data gathering.

### Methodology underlying foreign credit exposure and rollover risk analysis (excerpted formulas and definitions)
- A. Improving the Measurement of Foreign Credit Exposures
  - Organizing bank-level balance sheet data requires mapping parent banks and their network of subsidiaries.
  - More formally, a creditor country's foreign credit exposure equals:
    - ijijijij DCBA
  - Definitions:
    - ijA = captures the direct cross-border exposure from creditor banks in country i on debtor country j;
    - ijB = branchsubs__ijijassetstotaldepositsassetstotalB_ captures the exposure to subsidiaries and branches, taking into account the legal differences between them;
    - ijC = branchsubs_ijijijassetstotalclaimslocal represents the non-identified exposure by bank level data with respect to BIS reported affiliates claims;
    - ijD = ijijscommitmentcreditguaranteessderivative_ capture off-balance sheet exposure from country i banks on country j based on BIS data.
  - Foreign credit exposure (FCE_i) measured as percentage of GDP or total banking sector assets in country i:
    - 1 N ijijijij i j i ABC D FCE Z    
    - where iZ is a scaling factor (GDP or total banking sector assets in country i)

- B. Improving the Measurement of Foreign Rollover Risks
  - Focus: borrower country’s rollover risk to crises in its creditor foreign banking systems.
  - Borrower country j’s foreign rollover risk (Rollover Risk) captured by:
    - )1,_(1(*
      ijijijj ratioloandepositMinclaimsLocalclaimsborderCrossskRolloverRi
  - Definitions:
    - ijclaimsrCrossborde = volume of direct cross-border claims from country i on country j;
    - ijclaimsLocal = volume of affiliates (subsidiaries and branches) claims of parent banks from country i on country j;
    - )1,_(1 ij ratioloandepositMin = proxy of the proportion of loans not financed by local consumer deposits.
  - Measurement limitations:
    - Lending by affiliates funded by parent banks cannot be directly measured from available bank-level balance sheet data (Bankscope insufficient).
    - The foreign rollover risk measure could overestimate effective rollover risks.
    - Where affiliates’ bank-level data are not available, borrower-country national deposit-to-loan ratio is used to expand coverage.

- C. Modeling international banks’ assets and liabilities (scenario analysis)
  - Stylized bank balance sheet:
    - sLiabilitieOtherCapitalAssets_
    - AssetsDomesticAssetsForeignAssets__
  - Deleveraging and recapitalization to maintain capital ratio CAR after a loss of LLR percent on foreign assets:
    - DELAssetsForeignLLRAssetsCARRECAPAssetsForeignLLRCapital__
  - In absence of recapitalization, extent of deleveraging:
    -  AssetsForeignLLRCapitalITier CAR AssetsForeignLLRAssetsDEL_ 1 _
  - Deleveraging reduces global cross-border claims by affected international banks. For each recipient country, capital outflows are the aggregation of deleveraging by all creditor countries.
  - Scenario channels:
    1. Insolvency of upstream countries’ banks: banking system of country i becomes insolvent (losses exceed capital) and defaults on a proportion of liabilities to banks of other countries.
    2. Funding shock: banks of country i reduce lending to banks of country j, causing a funding shock ijY; if assets are sold at fire sale, loss ( ijY ) is absorbed by bank capital and may result in further deleveraging jLDE according to:
       - ' )1( j DELY AssetsCAR YCapital 
  - Deleveraging is triggered whenever capital to asset ratio falls below a threshold and is assumed proportional:
    - ijijijiij CBAXDEL
    - where iX is the loan loss ratio and ijijij CBA are the amounts of cross-border and affiliates related foreign credit exposures of country i’s banks on country j.

*Source: IMF working paper — “Foreign Credit Exposure Measure”*

### 1.  Foreign Credit Exposure Measure ....................................................................................

### 1.  Foreign Credit Exposure Measure

### Introduction
- The global crisis demonstrated rapid cross-border propagation of shocks; balance sheet linkages among financial institutions and markets determine spillover size and direction.
- Identified high-priority areas where data enhancements are most needed to measure global systemic risk.
- National supervision has access to granular domestic bank data, but:
  - Much of the data needed to identify and track international linkages is not available.
  - Institutional infrastructure for global systemic risk management is inadequate or non-existent.
- The paper highlights four illustrative data-related challenges and summarizes international initiatives to address data gaps.

### Key structural challenges to global systemic risk measurement
- (i) Lack of institutional mechanisms which ensure coordination of national approaches:
  - Authorities in each jurisdiction pursue national objectives that do not necessarily maximize global welfare.
  - Supervision of large, internationally-active financial institutions is dispersed among agencies in many countries, with imperfect information sharing and limited coordination tools.
  - A global framework for the resolution of internationally-active institutions is lacking.
  - There is no global formal lender of last resort for foreign-currency liquidity.
- (ii) Greater complexity in the international context:
  - Large global banks have complex organizational structures (thousands of entities across countries), active via cross-border lending, subsidiaries, and branches.
  - Group-level consolidated data can be misleading because they assume resources are freely transferable across locations and may mask differences between branches and subsidiaries.
  - Consolidated data make it difficult to compare global exposures to a bank group's capital accurately.
- (iii) Scarcity of data that capture international dimensions of systemic risk:
  - Supervisors have granular jurisdictional data but collection practices differ across jurisdictions and confidentiality restricts data sharing.
  - Publicly available bank-level data generally lack consistent information about international activities (cross-currency and cross-border positions).
  - BIS international banking statistics are aggregated across banks and do not provide the detailed breakdowns often needed for analysis.

### Literature on systemic risk assessment (three broad categories)
- Category 1 — Balance-sheet linkage analyses:
  - Focus on how balance sheet linkages amplify shocks and influence cross-border propagation.
  - Rely mainly on aggregate banking data (BIS, CPIS, balance of payments).
  - Examples: analyses showing parent-country conditions affect international lending and that shocks to BIS-reporting banks relate to slowdowns in international credit.
- Category 2 — Market-data-based indicators:
  - Use high-frequency equity prices, CDS spreads, bond spreads to infer systemic risk premia and correlations across markets.
  - Market data complement balance-sheet studies but are contemporaneous measures of stress and may not be reliable leading indicators.
- Category 3 — Simulations and scenario analysis:
  - Forward-looking simulations using balance-sheet interconnections to trace contagion through interbank linkages and spillovers to non-bank sectors.
  - Often use network measures and multilateral matrices (e.g., Leontief-type input-output matrices) based on BIS consolidated statistics to perform scenario analyses including rounds of asset and funding shocks.

### Four illustrative data challenges in measuring systemic risks internationally
- A key input: BIS international banking statistics (IBS) — aggregated at the level of national banking systems (e.g., UK banks), covering worldwide consolidated exposures by counterparty country, sector, and currency, but not bank-level detail.
- A. Measurement of banks’ foreign credit exposures
  - A banking system i’s foreign credit exposure to country j comprises:
    1. direct cross-border exposures to borrowers in country j booked by all offices of banking system i located outside of country j,
    2. effective exposures via the local positions booked by bank i’s subsidiaries and branches located in country j,
    3. all off-balance sheet exposures (derivatives, credit guarantees, and credit commitments) related to borrowers in country j.
  - Legal treatment differs: a parent bank’s exposure to a subsidiary in country j is legally limited to the subsidiary’s capital plus any non-equity funds provided by the parent; losses on branch exposures are most often fully absorbed by the bank.
  - BIS consolidated banking statistics (CBS) on an ultimate risk basis track banking system i’s foreign claims on borrowers in country j, including worldwide consolidated direct cross-border claims, but have limitations for detailed, bank-level exposure assessment.

### Data limitations highlighted by examples and implications
- Aggregate country-level data overlook bank-level heterogeneity.
- Commercial bank-level data lack borrowers’ country-location and sector detail.
- Syndicated loan data provide only partial creditor participation shares (typically less than half of the total syndicated loan amount).
- Intragroup activity analyses are often based on indirect evidence (subsidiaries’ lending levels) rather than direct measures of intragroup flows.
- Maximum-entropy assumptions for reconstructing interbank matrices can produce materially different conclusions from other assumptions when bilateral data are unavailable; this is especially problematic in global contexts where samples of large global banks are small.

### Current data initiatives and gaps
- The ongoing G20 initiative to close data gaps (IMF-FSB (2009) referenced) has set forth "20 recommendations" calling for:
  - Improvements to bank-level and aggregate statistics,
  - A framework for collection and sharing of these data across jurisdictions,
  - Rules governing access and use of the data.
- Recommendations emphasize the need for more bank-level data including firm-level bilateral linkages, banks’ organizational structures, and breakdowns of assets and liabilities by instrument, counterparty-country, counterparty-sector, currency, and residual maturity.
- Enhancements to aggregate BIS international banking statistics are progressing to better illuminate how banks organize operations across jurisdictions.
- Together, these enhancements aim to provide public-good financial data fundamental to multilateral surveillance and global perspectives on financial stability concerns.

*Source: IMF working paper — “Foreign Credit Exposure Measure”*

### BOX 1: The BIS International Banking Statistics

### BOX 1: The BIS International Banking Statistics

### Overview
- The BIS international banking statistics (IBS) track internationally active banks’ foreign positions through two main datasets: the BIS Consolidated Banking Statistics (CBS) and the BIS Locational Banking Statistics (LBS).
- Collectively, they are a key source of country-level aggregate information for analyzing financial stability.

### BIS Consolidated Banking Statistics (CBS)
- The CBS track banks’ worldwide consolidated gross claims and other exposures to individual countries and sectors.
- Foreign claims reported by banks are composed of:
  - Cross-border claims (A): claims on non-residents booked by either the banks’ head office or a foreign affiliate (branch or subsidiary) in a third country.
  - Local claims in foreign currency (B): claims booked by a foreign affiliate on borrowers residing in the host country, denominated in a foreign currency.
  - Local claims in local currency (C): claims booked by a foreign affiliate on borrowers residing in the host country, denominated in the local currency.
- Banks report foreign claims (A+B+C) on borrowers in individual countries on both an immediate borrower (IB) basis and on an ultimate risk (UR) basis:
  - CBS (IB): claims are allocated directly to the country where the borrower resides. Foreign claims are reported as international claims (A+B) and local claims in local currency (C).
  - CBS (UR): claims are allocated to the country where the ultimate obligor resides (guarantor or head office of a legally dependent branch). Here foreign claims are reported as cross-border claims (A) and local claims in all currencies (B+C). Off-balance sheet items such as derivative contracts and contingent exposures (undisbursed credit commitments and guarantees) are separately reported in CBS (UR).
- Advantages and limits:
  - Banks net out intergroup positions and consolidate positions across offices worldwide, providing upper-bound measures of a banking system’s exposure to a country.
  - Information on the branch/subsidiary structure is not included in BIS CBS; proxies can be derived using bank-level data by subtracting total customer deposits in the subsidiary from total assets of the subsidiary and aggregating to banking-system level.

### BIS Locational Banking Statistics (LBS)
- The LBS are residence-based data (i.e., follow balance-of-payments accounting) and track cross-border positions and local positions in foreign currencies of banks located in a particular country.
- Breakdown dimensions:
  - By currency, by sector (bank and non-bank), by country of residence of the counterparty, and by nationality of reporting banks.
  - Both domestically-owned and foreign-owned banking offices in reporting countries record positions on a gross (unconsolidated) basis, including positions vis-à-vis own affiliates in other countries.
- Importance:
  - One of the few sources of information about the currency composition of banks’ balance sheets, aiding tracking of system-level funding risks.
  - Because reporting jurisdictions provide information on the nationality of reporting banks, LBS can be aggregated across reporting locations along lines of consolidated national banking systems, providing a broad picture of the currency breakdown of banks’ consolidated foreign assets and liabilities.
- Combined use:
  - When combined with CBS data, LBS help track, at the bank nationality level, cross-currency funding and investment patterns which proved fragile during the crisis.

### Measurement of Borrower’s Reliance on Foreign Bank Credit
- BIS foreign claims are one of the few sources on borrowers’ reliance on credit from particular consolidated banking systems, but they can overestimate reliance where part of funding comes from within the borrower country.
- Combining BIS data with bank-level data permits adjustment for local funding (e.g., subsidiaries’ local customer deposits).
- Empirical comparisons:
  - As of September 2010, the adjusted lower-bound measure is, on average, about 10 percent below the upper-bound gross foreign claims measure.
  - Differences are minimal for Swiss banks, but larger for Canadian, Greek, and Spanish banks.
  - When off-balance sheet exposures are included, adjusted lower-bound measures fall below gross measures, especially for Belgian, Swiss and US banks.
- Regional patterns in adjusted rollover risk:
  - For Latin-America (red circles), the adjusted measure is, on average, only 40 percent of banks’ foreign claims.
  - Exposures for emerging Asia and Europe are on average roughly half of foreign claims.
  - The ratio for advanced countries is 65 percent.
- Crisis dynamics:
  - Countries that depended more heavily on resources from parent banks going into the crisis (i.e., a higher adjusted-to-gross ratio) saw a greater contraction in their total foreign funding during the crisis (Dec 2007 – Sep 2010).
- Methodology notes:
  - The adjusted rollover risk measure sums direct cross-border claims and affiliates’ claims that are not financed by local consumer deposits, with the latter proxied by the bank-level deposit to loan ratio of foreign subsidiaries and affiliates.
  - A complete picture using BIS LBS by nationality and CBS (IB) is possible only for countries that are BIS reporters; many emerging markets are excluded.

### Measurement of Cross-currency Funding and Maturity Transformation
- Pre-crisis patterns:
  - Many European and other non-US banks invested heavily in US dollar-denominated assets and relied increasingly on short-term US dollar funding via direct interbank borrowing and currency swaps.
  - When concerns mounted, these banks faced difficulty rolling over dollar funding, driving up dollar funding costs.
- Crisis response:
  - Following the collapse of Lehman Brothers in September 2008, the global demand for short-term dollar funding could only be met through establishment of central bank swap lines.
- BIS data insights and limitations:
  - BIS data provide indirect information on non-US banks’ dollar funding needs. Figure 3 indicates a growing risk of funding problems prior to the crisis: large European banks depended on some $1 trillion in short-term funding on the eve of the crisis, much obtained via FX swaps.
  - With available data, only broad tendencies can be identified since residual maturities or use of FX swap markets are not directly observed. Counterparty type (bank, non-bank, central bank) is used to proxy residual maturities; interbank and net foreign exchange swap positions are assumed to have shorter average maturity than positions vis-à-vis non-banks.

### Modeling Systemic Risks for International Banks
- IMF cross-border bank contagion scenario module:
  - Used for surveillance, spillover analyses, and early warning exercises.
  - Starts from asset credit exposures, differentiates potential losses on cross-border claims, affiliates’ claims, and off-balance sheet exposures, and captures propagation through bank losses, funding shocks and deleveraging.
- Scenario rounds:
  - First round: losses on assets deplete bank capital partially or fully, relying on assumptions about percentage loss on particular asset types (claims on public sector, banking sector, non-bank private sector).
  - Second round: banks restore capital adequacy to at least a certain threshold (here, the Basel III Tier I capital asset ratio) through deleveraging (sale of assets and refusal to roll-over loans).
  - Third round: banks reduce lending to other banks (funding shocks), potentially triggering fire sales, further deleveraging, and additional losses at other banks.
  - Final convergence: achieved when no further deleveraging occurs; possibility of public recapitalization can be simulated to assess policy mitigation.
- Illustration:
  - A common shock causing losses of X_i percent on foreign assets of banks from country i can force deleveraging to restore, for example, a Tier I capital ratio of 6 percent.
  - The funding shock to banks in borrower country j (Y_j) equals the deleveraging across all its creditor countries; subsequent fire sales can amplify losses and deleveraging until steady state.
- Data constraints and implications:
  - Lack of detailed and consistent input data limits use of models; ideally analyses would use bank-level data tracking bilateral interbank linkages.
  - Currently, BIS consolidated banking data are used to model losses due to direct exposures of banking systems to public sector, banking sector, non-bank private sector, and indirect exposures via off-balance sheet contingent positions.
  - Bank-level aggregated data provide estimates of banking systems’ positions vis-à-vis borrowers in the home country and of Tier I capital needed in analysis, neither of which is available in BIS data.
  - Treating an entire banking system as a single bank is a costly implicit assumption: problems within a group of banks of a particular nationality cannot be uncovered.

### What More Data Are Needed?
- Institutional and regulatory differences across countries affect shock scale and propagation; lack of internationally comparable data for largest global institutions complicates analysis.
- Requirements for systemic risk analysis:
  - Joint analysis of data covering many financial institutions to detect common exposures, cross-currency funding patterns and maturity transformation, and volatility of cross-border capital flows.
  - Individual bank-level data collected in a consistent and comparable format across banks to enable aggregation and detection of masked common exposures.
- Supervisory data issues:
  - Bank-level data obtained by national supervisors contain needed information but supervisors often lack critical pieces on how international banks are connected; real-time bilateral linkage data are crucial for crisis management.
  - Supervisory bank-level data are not widely shared across jurisdictions; only broad aggregates are typically disclosed.
- Public and commercial data limitations:
  - Commercial databases compiling annual-report data have lags and gaps, often miss counterparty-sector and country information, and have poor coverage of branches.
  - Many banks disclose only globally consolidated financial statements, losing geographic structure; currency of positions and exposures to counterparty types are often unreported.
- Policy initiatives and priorities:
  - IMF and FSB jointly issued a report to the G20 with 20 recommendations on reducing financial data gaps. Recommendations 8 and 9 require creation of a common reporting template for globally systemically important financial institutions (G-SIFIs).
  - An international working group has produced draft data templates designed to capture detailed bank-level data (work underway).

*Source: BOX 1: The BIS International Banking Statistics (extracted content).*

### Box 2 – G-20 Data Gaps Initiative

### Box 2 – G-20 Data Gaps Initiative

### Recommendations and ongoing efforts
- The joint IMF-FSB The Financial Crisis and Information Gaps report to the G20 made 20 recommendations on reducing financial data gaps. Those most related to this paper:
  - Development of measures of system-wide, macro-prudential risk, such as aggregate leverage and maturity mismatches (R. 4);
  - Development of a common data template for systemically important global financial institutions for the purpose of better understanding the exposures of these institutions to different financial sectors and national markets (R. 8 and 9);
  - Enhancement of BIS consolidated banking statistics, including the separate identification of non-bank financial institutions in the sectoral breakdown, and the tracking of funding patterns of international financial systems (R. 11);
  - Development of a standardized template covering the international exposure of large non-bank financial institutions (R. 14).
- Efforts underway:
  - An international working group has created a draft template for the collection of bank-level data which, if adopted, would provide information on banks’ exposures and funding positions with breakdowns by counterparty country and sector, instrument, currency, and remaining maturity.
  - Collection of information on banks’ intragroup positions and the number of branches and subsidiaries is under consideration.
- Other recommendations focus on:
  - Improvements in country aggregate financial soundness indicators and implementation of standard measures that can provide information on tail risks, concentrations, variation in distributions and the volatility of indicators over time (R. 2 and 3);
  - Improved understanding of risk transfers from credit default swaps (R. 5);
  - Improved securities data through better disclosure requirements for complex structured products and new common templates (R. 6 and 7);
  - Increased frequency and participation in the coordinated portfolio investment survey (R. 10 and 11) and international investment position survey (R. 12);
  - Monitoring and measure nonfinancial corporations cross border exposures (R. 13);
  - Promotion of compilation of sectoral accounts (R. 15);
  - Compile distributional information (such as ranges and quartile information) alongside aggregate figures (R.16);
  - Standardized presentation of government finance statistics (R. 17 and 18);
  - Improved public sector debt data (R. 18);
  - Completion of a real estate prices handbook (R. 19);
  - Enhancement of principal global indicators (R. 20).

### Bank-level and BIS data enhancements
- Proposed bank-level collection:
  - Would provide bank exposures and funding positions with breakdowns by counterparty country and sector, instrument, currency, and remaining maturity.
  - Would include consideration of intragroup positions and numbers of branches and subsidiaries.
  - The group outlined a framework for the collection and storage of highly confidential bank-level data, and a framework governing access to and use of the data (see IMF-FSB, 2011). These proposals are subject to consultation with the FSB Plenary, which is expected to take decisions in late 2011.
- Enhanced BIS consolidated banking statistics aim to:
  - Provide more information on the currency of banks’ positions;
  - Provide more information on banks’ counterparties, specifically on their location and sector;
  - Extend coverage to banks’ entire balance sheets, not just their foreign positions;
  - Broaden coverage to capture all banks’ financial assets and liabilities, including local currency positions vis-à-vis residents of the host country.
- Timing and structural improvements:
  - Starting in late 2012, information on the country location of banks’ counterparties should be available separately for banks of a particular nationality in each reporting jurisdiction.
  - Enhanced data will reveal more about banks’ operational structures (e.g., location, nationality, and location of counterparties simultaneously).

### Benefits, limitations, and wider scope
- Benefits if initiatives proceed:
  - Permit joint analyses of global positions of many banks from different jurisdictions, improving detection of vulnerabilities in common exposures or concentrated funding positions.
  - Provide information to assess spillovers from the failure of particular institutions to other institutions, national markets and sectors, and to evaluate impacts of regulatory responses (e.g., ring fencing restrictions).
  - Facilitate more realistic modeling of how asset and funding exposures endogenously interact during periods of stress.
- Public dissemination:
  - Public dissemination of raw data when possible, and consistent aggregates when not, can help market participants discipline themselves.
  - The release of bank-level sovereign exposure data in the framework of the European stress tests showed public dissemination is feasible during periods of financial distress.
- Limitations and scope extension:
  - Even with improved banking statistics, other dimensions of systemic risk may remain inadequately covered.
  - Non-banks, including pension funds, insurance companies and large multinational corporations, can also be systemically important.
  - Going forward, include non-bank institutions in the counterparty sector breakdown of banks’ exposures and bring large non-bank firms into data gathering.

### Methodology underlying foreign credit exposure and rollover risk analysis

A. Improving the Measurement of Foreign Credit Exposures
- Organizing bank-level balance sheet data requires mapping parent banks and their network of subsidiaries.
- More formally, a creditor country's foreign credit exposure equals:
  - ijijijij DCBA
  - Definitions:
    - ijA = captures the direct cross-border exposure from creditor banks in country i on debtor country j;
    - ijB = branchsubs__ijijassetstotaldepositsassetstotalB_ captures the exposure to subsidiaries and branches, taking into account the legal differences between them;
    - ijC = branchsubs_ijijijassetstotalclaimslocal represents the non-identified exposure by bank level data with respect to BIS reported affiliates claims;
    - ijD = ijijscommitmentcreditguaranteessderivative_ capture off-balance sheet exposure from country i banks on country j based on BIS data.
- Foreign credit exposure (FCE_i) measured as percentage of GDP or total banking sector assets in country i:
  - 1 N ijijijij i j i ABC D FCE Z    
  - where iZ is a scaling factor (GDP or total banking sector assets in country i)

B. Improving the Measurement of Foreign Rollover Risks
- Focuses on a borrower country’s rollover risk to crises in its creditor foreign banking systems.
- Captures potential rollover risks of direct cross-border lending and lending by foreign affiliates funded by parent banks.
- Borrower country j’s foreign rollover risk (Rollover Risk) can be captured by:
  - )1,_(1(*
    ijijijj ratioloandepositMinclaimsLocalclaimsborderCrossskRolloverRi
- Definitions:
  - ijclaimsrCrossborde = captures the volume of direct cross-border claims from country i on country j;
  - ijclaimsLocal = the volume of affiliates (subsidiaries and branches) claims of parent banks from country i on country j;
  - )1,_(1 ij ratioloandepositMin is a proxy of the proportion of loans not financed by local consumer deposits.
- Notes on measurement limitations:
  - The amount of lending by affiliates funded by their parent banks cannot be directly measured since available bank-level balance sheet data from Bankscope is not detailed enough to identify all parent banks’ non-equity claims.
  - The foreign rollover risk measure could overestimate effective rollover risks.
  - Where affiliates’ bank-level data are not available, borrower country national deposit to loan ratio is used to have larger country coverage.

C. Modeling international banks’ assets and liabilities (scenario analysis)
- Stylized bank balance sheet:
  - sLiabilitieOtherCapitalAssets_
  - AssetsDomesticAssetsForeignAssets__
- Deleveraging and recapitalization to maintain capital ratio CAR after a loss of LLR percent on foreign assets:
  - DELAssetsForeignLLRAssetsCARRECAPAssetsForeignLLRCapital__
- In absence of recapitalization, extent of deleveraging:
  -  AssetsForeignLLRCapitalITier CAR AssetsForeignLLRAssetsDEL_ 1 _
- Deleveraging reduces global cross-border claims by affected international banks. For each recipient country, capital outflows are the aggregation of deleveraging by all creditor countries.
- Additional rounds of deleveraging may occur if shocks cause international bank insolvencies and fire sales, triggering further losses until equilibrium when no further deleveraging takes place.
- Scenario channels:
  1. Insolvency of upstream countries’ banks: banking system of country i becomes insolvent (losses exceed capital) and defaults on a proportion of liabilities to banks of other countries.
  2. Funding shock: banks of country i reduce lending to banks of country j, causing a funding shock ijY; if assets are sold at fire sale, loss ( ijY ) is absorbed by bank capital and may result in further deleveraging jLDE according to:
     - ' )1( j DELY AssetsCAR YCapital 
- Deleveraging is triggered whenever capital to asset ratio falls below a threshold and is assumed proportional:
  - ijijijiij CBAXDEL
  - where iX is the loan loss ratio and ijijij CBA are the amounts of cross-border and affiliates related foreign credit exposures of country i’s banks on country j.

*Source: Box 2 – G-20 Data Gaps Initiative (excerpt).*

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_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2011/_wp11222.pdf_