Abstract

8.1 This chapter brings together the concepts and definitions previously set out—including accounting principles, underlying series, and calculation methods—to explain how additional FSIs for deposit takers (DTs) are to be calculated. Unless otherwise stated, all the “lines” mentioned in this chapter refer to Table 5.1.

I. Introduction

8.1 This chapter brings together the concepts and definitions previously set out—including accounting principles, underlying series, and calculation methods—to explain how additional FSIs for deposit takers (DTs) are to be calculated. Unless otherwise stated, all the “lines” mentioned in this chapter refer to Table 5.1.

8.2 Beyond the core FSIs for DTs discussed in Chapter 7, an additional set of indicators is recommended to provide additional information on deposit takers’ financial health. Specifically, 12 additional FSIs for DTs are recommended by the Guide, which are listed in Table 1.1 and discussed in the rest of this chapter. Annex 8.1 summarizes the concepts, calculation methods, source data, and compilation issues for these additional FSIs for DTs.

II. Additional FSIs for DTs Large Exposures to Capital

8.3 The FSI Large exposures to capital is intended to identify vulnerabilities arising from the concentration of credit risk. The assessment of large exposures aims at capturing the potential negative impact on financial institutions’ capital if a few counterparties experience difficulties in servicing their obligations. As recognized by the Basel Committee on Banking Supervision (BCBS), “Banks did not always consistently measure, aggregate and control exposures to single counterparties or to groups of connected counterparties across their books and operations.”1

8.4 This FSI is calculated by taking the value of large exposures (line 46) as the numerator, and Tier 1 capital (line 33) as the denominator. Large exposures refer to one or more credit exposures2 to the same counterparty or group of connected counterparties that exceed a specified percentage of the DT’s capital. The Guide recommendations are based on BCBS guidance, but national implementation may vary. Supervisory data will be the source for this FSI, and any national variations from the BCBS framework outlined further should be disclosed in the metadata.

8.5 The BCBS defines a large exposure as being equal to or larger than 10 percent of its Tier 1 capital as defined in Basel III.3 The BCBS imposes an exposure limit of 25 percent of Tier 1 capital to a single counterparty or group of connected counterparties.

8.6 When calculating the aggregated FSI for the whole DT sector, the numerator is the sum of the large exposures after application of the eligible credit risk mitigation techniques of each DT group within the reporting population, while the denominator is the aggregated Tier 1 capital of all reporting DT groups. Data on large exposures should be available from supervisory sources. The BCBS stresses the need for banks to have methodologies in place for the measurement and control of large exposures, including the need for appropriate levels of large exposure limits, with special attention paid to connected lending.4 Any national variance from the BCBS guidance with respect to definition of large exposures should be noted in the metadata.

Geographical Distribution of Loans to Total Gross Loans

8.7 The FSI Geographical distribution of loans to total gross loans provides information on the geographical distribution of gross loans, by regional grouping of countries. It allows the monitoring of credit risk arising from exposures to a group of countries. Moreover, this FSI can help in assessing the impact of adverse events in these countries on the domestic financial system. If lending to any individual country or sub-region is particularly significant, further disaggregation—and identification of the country or sub-region—is welcome.5

8.8 The numerator of this FSI are loans to the different geographical regions, while the denominator is total gross loans. The loans in the numerator are gross loans to regional grouping of countries.

8.9 Information on total loans is available from the DTs’ balance sheet, as described in paragraph 7.38. Gross loans (line 18.i of Table 5.1) are defined in paragraphs 5.41 to 5.43. Supervisory sources might have available information on the geographic distribution of loans (e.g., BIS’s consolidated international banking statistics). Otherwise, additional data might be requested. In recording the geographic distribution of loans, claims are attributed to economies on the basis of the residency of the entity on which DTs have claims. Residency is based on the concept of economic territory, which is not always based strictly on physical or political borders (see paragraph 2.11). The suggested regional grouping of countries of Box 8.1 is based on the classification provided in the IMF’s World Economic Outlook. Details of the countries included in each group of the World Economic Outlook are presented in Annex 8.2.

8.10 For cross-border consolidated data, lending is attributed based on the residence of the domestic reporting entity. Therefore, lending by any foreign branches or DT subsidiaries of the reporting group to residents of the local economy where they are located (including any local-currency-denominated lending) is classified as lending to nonresidents and allocated to the appropriate region of the world. Lending to residents of the economy for which the FSI data are being compiled is classified as lending to the domestic economy. For data compiled on a domestic location consolidation basis, any lending among DTs in the reporting population that are part of the same group should be excluded, but loans to DT branches and subsidiaries abroad are included in the data as lending to nonresidents.

Gross Asset Positions in Financial Derivatives to Capital

8.11 The FSI Gross asset positions in financial derivatives to capital is intended to gauge the exposure of DTs’ asset positions in financial derivatives relative to capital. While net positions may be more readily available, and there are legitimate reasons to focus attention on them as a risk management tool, gross positions provide a more comparable metric across countries, markets, and products. Moreover, counterparty risk is particularly relevant in the financial derivative markets, and thus it is important to monitor the magnitude of the gross positions.

8.12 The gross asset position is calculated by using the market value of financial derivative assets (line 21 in Table 5.1) as the numerator and capital as the denominator. Capital is measured as total regulatory capital (line 39). Financial derivatives are defined in paragraphs 5.55–5.65.

8.13 Data on the market value position of financial derivative assets should be available from accounting records as well as supervisory sources. The coverage of financial derivatives includes forwards, futures, options and swaps of currency or interest rates, and instruments such as swaptions, among others, combining multiple derivative elements. Regarding capital, sources of data are discussed in 7.9–7.12.

Gross Liability Positions in Financial Derivatives to Capital

8.14 The FSI Gross liability positions in financial derivatives to capital is intended to gauge the exposure of DT’s liability positions in financial derivatives relative to capital. It is the mirror indicator of the previous FSI, in this case measuring the liability exposure. In this regard, all the considerations on data sources and issues for compilers presented in the previous section also apply for this indicator.

Regional Grouping of Countries

Following the IMF’s World Economic Outlook, the Guide recommends the following grouping of countries for the indicator on geographical distribution of loans.

  • Advanced economies

    • Euro area

    • Major advanced economies (G7)

  • Emerging market and developing economies

    • Emerging and developing Asia

    • Emerging and developing Europe

    • Latin America and the Caribbean

    • Middle East and Central Asia

    • Sub-Saharan Africa

Compilers are also encouraged to track lending to significant regional groupings that are relevant in their financial dealings; for example, East African Community (EAC) or Gulf Cooperation Council, (GCC), among others.

Source: World Economic Outlook (https://www.imf.org/external/pubs/ft/weo/2019/02/weodata/weoselgr.aspx).

8.15 Similarly to the previous indicator, this FSI is calculated by using the market value of financial derivative liabilities (line 21) as the numerator and total regulatory capital (line 39) as the denominator.

Trading Income to Gross Income

8.16 The FSI Trading income to gross income is intended to capture the share of DTs’ income generated from financial market activities, including currency trading, and thus helps in assessing risks from the business model. The evolution of this FSI over time provides an indication of the DTs’ reliance on activities other than intermediation to generate profits.

8.17 Data on gains and losses on financial instruments should be available from accounting records and be accessible to supervisors, but the extent to which they meet the definitions of the Guide could depend on national commercial accounting practices. Regarding gross income, sources of data are discussed in paragraphs 7.58–7.59. Since this a flow-based FSI, both for numerator and denominator, income should be accumulated from the beginning of the year until the end of the reporting period (month, quarter), as explained in paragraph 7.57.

8.18 This FSI is calculated by using gains or losses on financial instruments (line 4.ii of Table 5.1) as the numerator and gross income (line 5) as the denominator. Gains and losses on financial instruments are defined in paragraphs 5.19–5.21, and gross income is defined in paragraph 5.16.

8.19 Gains and losses on financial instruments are those arising during the period under review. Compilers should be aware that the Guide recommends the inclusion of gains and losses during each reporting period on all financial instruments (in domestic and foreign currency) valued at market or fair value through profit and loss; excluding equity in associates, subsidiaries, and any reverse equity investment that are designated at FVOIC where no recycle is allowed in accordance with IFRS 9 (see paragraph 5.19). The numerator calls for a net value, where gains and losses from trading income are netted out.

Personnel Expenses to Noninterest Expenses

8.20 The FSI Personnel expenses to noninterest expenses measures the incidence of personnel costs in total noninterest expenses (operating or overhead expenses). This FSI is used to gauge the management efficiency of a DT and its evolution over time. Different banking business models (wholesale corporate banking, investment banking, retail banking, micro-credit, personal banking, or others) require different staffing levels, so the ratio will heavily depend on the mix of models included in the reporting population. For instance, retail banking or personal banking require more staff per account, which will imply higher personnel costs and a larger value of the indicator compared to wholesale corporate banking or investment banking models. This fact should be highlighted when making cross-country and peer group comparisons.

8.21 This FSI is calculated by using personnel costs (line 6.i in Table 5.1) as the numerator and noninterest expenses (line 6 of Table 5.1) as the denominator. Noninterest expenses and personnel costs are defined in paragraphs 5.25–5.26.

8.22 Data for personnel costs are available from accounting records and should be accessible to supervisors. National practices will also determine the extent to which the data meet the definitions in the Guide. Regarding noninterest expenses, sources of data and issues for compilers are discussed in paragraph 7.62–7.63. Regarding employee stock options (see paragraph 5.63), the Guide recommends treating them as an increase in equity with a corresponding expense comprising the fair value of the stock options at the dates such options are granted.

Spread between Reference Lending and Deposit Rates

8.23 The FSI Spread between reference lending and deposit rates (SLDR) provides an indicator of the intermediation income earned by the DT sector. Spreads between lending and deposit rates can serve as indicators of trends in DTs’ net interest income, and can also provide information on DTs’ pricing behavior. However, further information would be required to understand the causes of that behavior. High spreads might signal less competitive pressures on banks; but they can also be attributable to bank inefficiency, higher counterpart risk, insufficient collateral, or weak protection by the judicial system.

8.24 This FSI is the difference (expressed in basis points) between the weighted average loan rate and the weighted average deposit rate, excluding interest charged on loans and deposits between DTs. To measure the SLDR, the Guide recommends one of two options. The first option entails calculating the weighted average of all lending and deposit interest rates (excluding loans and deposits among DTs) with the highest available frequency during the reference period (month or quarter) and reporting the spread between them as the indicator for that period. The second option consists on approximating weighted averages using interest income (line 1 of Table 5.1) divided by non-interbank gross loans (line 18.i.ii of Table 5.1) and interest expense (line 2 of Table 5.1) divided by customer deposits (line 24.i of Table 5.1), respectively (see Annex 8.3 for more details). While the first approach is more accurate, countries which are starting to report this indicator may choose the second approach because it is less computational intensive. Reporters should note their chosen approach in the metadata.

Spread between Highest and Lowest Interbank Rates

8.25 The FSI Spread between highest and lowest interbank rates measured in basis points is an indicator of the perceived risk of lending among DTs. Borrowing in the interbank market is the most immediate source of bank liquidity and interbank rates a key element of the monetary policy transmission mechanism. Interbank rates measure the cost of funds to DTs in the domestic interbank market, namely the cost of borrowing the excess reserves of other DTs. Interest-rate spreads, such as those between borrowers with different credit risk profiles, can serve to indicate the level of perceived risk within the financial system. Therefore, the spread between the highest and lowest interbank rates would help to capture banks’ own perception of idiosyncratic problems and risks facing banks with access to the interbank market. Increasing spreads indicate increasing risk premium charged to DTs under stress (liquidity or solvency problems).6

8.26 This FSI is calculated as the spread between highest and lowest interbank rates, measured in basis points. Interbank rates are usually short-term in nature. Since this FSI provides information on DTs’ own perceptions of risks facing other banks, and perceptions can change very quickly, the Guide encourages daily or weekly compilation of interbank rates for loans of the same maturity (overnight or weekly), and averaging them for the reporting period (month or quarter).

8.27 The source of these data is usually interbank dealers or brokers. The data might be available to supervisory authorities or the statistical departments of central banks.

Customer Deposits to Total (Non-interbank) Loans

8.28 The FSI Customer deposits to total (noninterbank) loans measures the share of DT’s gross loans (excluding interbank activity) funded through customer deposits, which are generally presumed to be more stable than wholesale funding through the interbank market. When stable deposits are low relative to loans, there is greater funding risk: greater dependence on more “volatile” funding for DTs’ portfolios.

8.29 This FSI is calculated by using customer deposits (line 24.i in Table 5.1) as the numerator and non-interbank loans (line 18.i.ii) as the denominator. Customer deposits are defined in paragraph 5.40, and loans are defined in paragraphs 5.41–5.43.

8.30 Supervisory sources will usually provide data that allow for the compilation of a measure of customer deposits consistent with the approach of the Guide. Regarding total loans, sources of data are the same as for the NPLs to total gross loans indicator, while loans to other DTs in the reporting population should be available from supervisors.

8.31 This FSI, which is the inverse of the loan to deposit ratio widely used by supervisors and analysts, provides a shorthand view of banks’ reliance on volatile funding. A low value indicates greater reliance on non-deposit funding. More nuanced analysis requires more detail on the customer deposit base to assess the relative stability of the various types of deposits within the broader category of customer deposits.

8.32 The Guide recommends that the type of depositor be the primary factor in defining customer deposits, both because of its relevance and general applicability. Thus, customer deposits include all deposits (from residents and nonresidents) except those placed by other DTs and OFCs (resident or nonresident). Customer deposits considered to be a more stable financing source comprise current accounts (used for regular business transactions), time deposits with remaining maturity over one year, and deposits covered by deposit insurance schemes.

Foreign-Currency-Denominated Loans to Total Loans

8.33 The FSI Foreign-currency-denominated loans to total loans measures one aspect of DTs’ exposure to exchange rate risk. This FSI is particularly relevant for countries where lending in foreign currency constitutes a significant share of total lending. Exchange rate changes will create holding gains or losses on the national-currency equivalent value of these loan positions. It is also important to monitor the ratio of foreign-currency-denominated loans to gross loans for residents, due to the increased credit risk associated with the ability of local borrowers to service their foreign-currency-denominated liabilities, particularly in the context of large devaluations or a lack of foreign currency earnings. This risk is ameliorated when borrowers’ earnings are in foreign currency, such as the case of exporters, because a devaluation will have parallel effects on debt and earnings.

8.34 This FSI is calculated using the foreign-currency and foreign-currency-linked part of gross loans (line 53 in Table 5.1) to residents and nonresidents as the numerator, and gross loans (line 18.i) as the denominator.

8.35 Domestic currency is that which is legal tender in the economy and issued by the monetary authority for that economy or a common currency area. Any currencies that do not meet this definition are foreign currencies to that economy (see paragraph 4.53). Foreign currency instruments are those payable in a currency other than the domestic currency. In the special case where an economy uses as its only legal tender a foreign currency, this FSI could be compiled excluding borrowing in, and linked to, that foreign currency. A special case is presented by instruments payable in domestic currency but with their principal and interest linked to a foreign currency. These foreign-currency-linked instruments should be considered as if denominated in that foreign currency7

8.36 Data on foreign-currency-denominated loans should be available from supervisory sources because of the supervisory interest in banks’ exposure to foreign currency. A difficultly can arise with data on foreign-currency-linked loans, since most probably they are reported as being denominated in the domestic currency although in some countries they might be separately identified. Regarding total loans, the sources of data are the same as described in paragraph 7.38.

8.37 For cross-border consolidated data, the question of whether a currency is a foreign currency is determined by the residence of the parent entity of that specific consolidated group. The currency composition of assets (and liabilities) is primarily determined by the currency denomination of future payment(s).

8.38 Foreign-currency-linked loans are included in the numerator, as movements in the domestic exchange rate will affect their value in domestic currency terms (see paragraph 4.53). The most appropriate exchange rate to be used for conversion of a position into the unit of account is the market (spot) exchange rate prevailing on the reference date to which the position relates. The midpoint between buying and selling rates is preferred (see paragraph 4.55).

Foreign-Currency-Denominated Liabilities to Total Liabilities

8.39 The FSI foreign-currency-denominated liabilities to total liabilities measures the relative importance of funding in foreign currency within total liabilities. The magnitude of this ratio should be considered together with the value of the FSI foreign-currency-denominated loans to total loans. Exchange rate changes will create holding gains or losses on the national-currency equivalent value of these positions. Although it is desirable that domestically incorporated DTs have access to international markets, a high reliance on foreign-currency borrowing may signal that DTs are taking greater risks, by increasing their exposure to exchange rate movements and foreign currency funding reversals. It can also be a sign of residents’ mistrust in the domestic currency and their preference for saving in a foreign currency. Extensive foreign currency lending funded by foreign currency borrowing in the same currency can help reduce the DTs’ foreign exchange exposure. However, DTs could remain exposed if loans are being granted to domestic borrowers without foreign currency income as the debtors face difficulties servicing the loans in case of a large devaluation.

8.40 This FSI is calculated using liabilities denominated in foreign currency (line 54 in Table 5.1) as the numerator and total debt (line 28) plus financial derivative liabilities (line 29) minus financial derivative assets (line 21) as the denominator.

8.41 Data on foreign-currency-denominated liabilities should be available from supervisory sources. Total liabilities (line 23) may be sourced from accounting or supervisory data. If foreign-currency-linked loans comprise a significant volume of credit in a jurisdiction, the data should be available from supervisory sources.

8.42 The definitions of foreign currency, foreign-currency-denominated, and foreign-currency-linked instruments, as well as exchange rate conversion, are presented in paragraphs 4.52–4.56. They are the same as those set out in the issues for compilers in the previous section on Foreign-currency-denominated loans to total loans. Foreign currency liabilities are defined in paragraph 5.101, while financial derivatives are defined in paragraphs 5.55–5.62 and liabilities in paragraph 5.35. Metadata should disclose any national variation from these definitions.

8.43 For total liabilities, it is recommended that the net market value position (liabilities less assets) of financial derivatives be included, rather than the gross liability position, because of the market practice of creating offsetting contracts and the possibility of forward-type instruments switching between asset and liability positions from one period to the next. In the special case where an economy uses as its only legal tender a foreign currency, this ratio should be compiled excluding positions in, and linked to, this currency.

Credit Growth to Private Sector

8.44 The FSI credit growth to private sector is intended to capture emerging systemic risks and can serve as a forward-looking indicator of potential asset quality problems and vulnerabilities in the DT sector. Rapid credit expansion may, at times, exceed banks’ capacity to assess credit risks, thereby leading to reduced asset quality and increased probability of default. Rapid credit growth can also be an indicator of deteriorating underwriting standards, leading to elevated risk in the portfolio.

Co-circulation of Foreign Currency

Co-circulation—also commonly known as dollarization—results when a foreign currency (often the United States dollar, euro, or a regional currency) is used as a means of payment and store of value in parallel with the domestic currency. Several factors may affect the degree of co-circulation of an economy, among them its legal framework. While some countries do not allow deposits and loans in foreign currency, others accept them de jure or de facto. In extreme cases, some countries have adopted a foreign currency as the only legal tender.

Residents of countries with high and variable inflation may prefer to save and do business in a foreign currency whose value is more stable. The interest rate differential between instruments denominated in domestic and foreign currency also influences the preferences of the public, together with expectations of future exchange rate movements.

Both FSIs on foreign-currency-denominated loans and liabilities to total loans and total liabilities may serve as gauges of the level of co-circulation in an economy. High ratios for these FSIs can also result from high proportions of tourism or foreign trade in the economy. The graphs given next, constructed from data reported by countries for publication on the IMF’s FSI website, show a sample of countries with different levels of co-circulation and its evolution through time

Figure 8.1.1.
Figure 8.1.1.
Source: IMF, Financial Soundness Indicators website.
Figure 8.1.2.
Figure 8.1.2.
Source: IMF, Financial Soundness Indicators website.

8.45 Excessive credit growth, especially if concentrated in a few sectors, is an indicator of potential vulnerabilities in the financial sector. In fact, cross-country empirical studies of systemic bank distress suggest that banking crisis tend to be preceded by credit booms.8 That is why, as discussed in Chapter 13, this indicator can be used as one input for macroprudential policies.

8.46 This FSI is calculated using the year-over-year growth rate of total credit to the nonfinancial private sector. The rate is computed as the difference between stocks of total credit to the non-financial private sector at the end of the reporting period and 12 prior months, divided by the stock of credit to the private sector a year earlier. The indicator is reported on a percentage basis.

8.47 Credit to the private sector (line 57) is defined in paragraph 5.103 and includes gross loans extended by DTs to the nonfinancial private sector, plus debt securities issued by private NFCs and held by DTs. Total credit is calculated on a gross basis, that is, excluding provisions for doubtful loans or debt securities.

8.48 Information on credit to the private sector is typically available from accounting records and supervisory sources.

Annex 8.1 Summary of Additional Financial Soundness Indicators for Deposit Takers

Annex 8.2 Geographical Distribution of Countries

The (October 2019) IMF’s World Economic Outlook clusters countries in different groups, as described next.

Advanced Economies

Composed of 39 countries: Australia, Austria, Belgium, Canada, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hong Kong Special Administrative Region, Iceland, Ireland, Israel, Italy, Japan, Korea, Latvia, Lithuania, Luxembourg, Macao Special Administrative Region, Malta, Netherlands, New Zealand, Norway, Portugal, Puerto Rico, San Marino, Singapore, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Taiwan Province of China, United Kingdom, and United States.

Euro Area

Composed of 19 countries: Austria, Belgium, Cyprus, Estonia, Finland, France, Germany, Greece, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Portugal, Slovak Republic, Slovenia, and Spain.

Major Advanced Economies (G7)

Composed of seven countries: Canada, France, Germany, Italy, Japan, United Kingdom, and United States.

Emerging Market and Developing Economies

Composed of 155 countries: Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Aruba, Azerbaijan, The Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei Darussalam, Bulgaria, Burkina Faso, Burundi, Cabo Verde, Cambodia, Cameroon, Central African Republic, Chad, Chile, China, Colombia, Comoros, Democratic Republic of the Congo, Republic of Congo, Costa Rica, Côte d’Ivoire, Croatia, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Eswatini, Ethiopia, Fiji, Gabon, The Gambia, Georgia, Ghana, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, India, Indonesia, Islamic Republic of Iran, Iraq, Jamaica, Jordan, Kazakhstan, Kenya, Kiribati, Kosovo, Kuwait, Kyrgyz Republic, Lao P.D.R., Lebanon, Lesotho, Liberia, Libya, Madagascar, Malawi, Malaysia, Maldives, Mali, Marshall Islands, Mauritania, Mauritius, Mexico, Micronesia, Moldova, Mongolia, Montenegro, Morocco, Mozambique, Myanmar, Namibia, Nauru, Nepal, Nicaragua, Niger, Nigeria, North Macedonia, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Qatar, Romania, Russia, Rwanda, Samoa, São Tomé and Príncipe, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Solomon Islands, Somalia, South Africa, South Sudan, Sri Lanka, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Sudan, Suriname, Syria, Tajikistan, Tanzania, Tailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Tuvalu, Uganda, Ukraine, United Arab Emirates, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, Yemen, Zambia, and Zimbabwe.

Emerging and Developing Asia

Composed of 30 countries: Bangladesh, Bhutan, Brunei Darussalam, Cambodia, China, Fiji, India, Indonesia, Kiribati, Lao P.D.R., Malaysia, Maldives, Marshall Islands, Micronesia, Mongolia, Myanmar, Nauru, Nepal, Palau, Papua New Guinea, Philippines, Samoa, Solomon Islands, Sri Lanka, Tailand, TimorLeste, Tonga, Tuvalu, Vanuatu, and Vietnam.

Emerging and Developing Europe

Composed of 16 countries: Albania, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Hungary, Kosovo, Moldova, Montenegro, North Macedonia, Poland, Romania, Russia, Serbia, Turkey, and Ukraine.

Latin America and the Caribbean

Composed of 33 countries: Antigua and Barbuda, Argentina, Aruba, The Bahamas, Barbados, Belize, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominica, Dominican Republic, Ecuador, El Salvador, Grenada, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Suriname, Trinidad and Tobago, Uruguay, and Venezuela.

Middle East and Central Asia

Composed of 31 countries: Afghanistan, Algeria, Armenia, Azerbaijan, Bahrain, Djibouti, Egypt, Georgia, Islamic Republic of Iran, Iraq, Jordan, Kuwait, Lebanon, Libya, Mauritania, Morocco, Oman, Pakistan, Qatar, Saudi Arabia, Somalia, Sudan, Syria, Tajikistan, Tunisia, Turkmenistan, United Arab Emirates, Uzbekistan, and Yemen.

Sub-Saharan Africa

Composed of 45 countries: Angola, Benin, Botswana, Burkina Faso, Burundi, Cabo Verde, Cameroon, Central African Republic, Chad, Comoros, Democratic Republic of the Congo, Republic of Congo, Côte d’Ivoire, Equatorial Guinea, Eritrea, Eswatini, Ethiopia, Gabon, The Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritius, Mozambique, Namibia, Niger, Nigeria, Rwanda, São Tomé and Príncipe, Senegal, Seychelles, Sierra Leone, South Africa, South Sudan, Tanzania, Togo, Uganda, Zambia, and Zimbabwe.

Annex 8.3 Weighted Average Interest Rate for a Loan Portfolio

8.49 A method of calculating the weighted average lending rate described for the spread between reference lending and deposit rates consists of dividing the accrued amount of interest income on loans reported by DTs for a given period (numerator) by the average position of loans (denominator) for the same period. The weighted average deposit rate can be computed by dividing interest expense on deposits (numerator) by the average position of deposits (denominator) for the same period. Positions should be averaged using the most frequent observations available. Contracted interest rates (i.e., price data) can also be used to calculate weighted average interest rates for a given reference period, using the loan amounts as weights.9

8.50 In principle, using this method, the weighted average interest rate for a portfolio of n loans (types of deposits) can be constructed as follows:

Weightedaverageinterestrate=Σi=1nRiLi/(Σi=0nSt/T),

10

where Ri = Interest rate for Loan i that is outstanding during the period,11 Li = Loan i,

St = Stock of loans observed at time t, and T = Total number of observations during the period.

8.51 Under accrual accounting, interest costs accrue continuously on debt instruments, thus matching the cost of funds with the provision of funds. The rate at which these costs accrue is known as the interest rate, and for deposits and loans, it is typically established by contractual arrangement. For compiling the SLDR, annualized interest rates should be calculated.

8.52 Average-period interest rates are more closely related to profitability and pricing behavior than end-period rates and are not subject to the possibility of exceptional daily fluctuations. However, an SLDR based on end-period rates, directly measured, with appropriate metadata, provides reliable information. Such a spread between lending and deposit rates would be calculated as the difference between the weighted averages of end-period interest rates for the different types of loans and the different types of deposits (i.e., three-month and six-month). The weights for each type of loan and deposit would be calculated using end-period position data.

8.53 The Guide recommends at a minimum the compilation of an SLDR for outstanding business, as this is directly related to profitability. For the purposes of this FSI, outstanding business is the stock of deposits placed with DTs and the stock of loans extended by DTs, excluding deposits from, and loans to, other resident DTs.

8.54 To reflect more closely, current market developments and DTs’ pricing behavior, rather than outstanding business, countries could also compile an SLDR for new business, particularly if the necessary data are readily available. New business is defined as deposits placed with DTs and loans extended by DTs during the reference period. New business includes “rolled over” or renewed loans and deposits.

8.55 In Chapter 5, the Guide recommends that interest should no longer accrue on nonperforming loans, resulting in an implicit interest rate of zero. While there might be some analytical benefit in excluding NPLs from the SLDR calculation, the Guide’s preferred approach is to include such loans in the calculation. In other words, when compiling the interest rate on loans, positions in NPLs (less specific provisions against NPLs)12 should be included in the denominator and zero interest included in the numerator. This approach has the benefit of reflecting the adverse impact on DT’s yield on assets of high volumes of NPLs.

8.56 In some economies, a certain amount of lending by DTs can be directed to priority sectors at prescribed interest rates for economic development. As in the discussion on NPLs, the Guide prefers that such loans and the interest that accrues be included in the calculation of an SLDR, because excluding such business could give a misleading indication of profitability.13

8.57 As noted earlier, while the Guide recommends at a minimum the compilation of the SLDR on all outstanding business (excluding among DTs), this SLDR could be supplemented with information on various subcategories. In this context, the SLDR for all outstanding business could be supplemented with SLDRs for:

  • both the nonfinancial corporations sector and the household sector;

  • both short-term and long-term (original maturity) interest rates;

  • peer groups, to ascertain the pricing behavior of different subgroups within the total resident DTs; or

  • both domestic and foreign currency business.

1

BCBS, Supervisory Framework for Measuring and Controlling Large Exposures, Basel, 2014, page 1.

2

Net of specific provisions.

3

See BCBS, Supervisory Framework for Measuring and Controlling Large Exposures, Basel, 2014, page 4. The BCBS establishes precise rules on how to measure different types of exposures, including its reduction via credit risk mitigation techniques. Banks have to report their largest 20 exposures.

4

See BCBS, Basel III: A Global Regulatory Framework for More Resilient Banks and Banking Systems, Basel, 2011, paragraph 119.

5

The Bank for International Settlements (BIS) collects and publishes international banking statistics on both a locational (residency) and consolidated basis for a group of economies with significant international banking activities. The definitions in the Guide are broadly consistent with those of the BIS. For countries compiling the BIS series “summary of foreign claims (immediate counterparty basis) by nationality of reporting bank,” such data serve the purpose of this FSI. Annex 8.2 maps countries, which are the basis of the BIS series, to the regional groupings used for this FSI (see Box 8.1).

6

Several studies conducted after the financial crisis of 2008–2009 indicate that counterparty risk played a prominent role in the pricing of the interbank market, with a significant increase in the spread charged to poorly performing banks. See, among others, Afonso, G., A. Kovner and A. Schoar; (2011) Stressed, Not Frozen: The Federal Funds Market in the Financial Crisis, Federal Reserve Bank of New York Staff Report, Number 437; or Angelini, P. , A. Nobili and M.C. Picillo; (2009), The Interbank Market after August 2007: What Has Changed and Why?, Banca d’Italia Working Papers, Number 731.

7

See Balance of Payments and International Investment Position Manual, sixth edition, paragraph 3.101 and Monetary and Financial Statistics Manual and Compilation Guide, paragraph 4.205. This treatment reflects a statistical rather than an accounting approach.

8

See: Demirgüç-Kunt, A. and Detragiache E., 2005, Cross-Country Empirical Studies of Systemic Bank Distress: A Survey, IMF Working Paper, Number 05/96; or Reinhart, C.M. and Rogoff K.S., 2011, “From Financial Crash to Debt Crisis,” American Economic Review, Volume. 101, Number 5.

9

This method of calculation could minimize the reporting burden on DTs if data on accrued amounts of interest on loans and deposits are readily available from the accounting systems of DTs, as typically data on DTs’ positions in loans and deposits are regularly reported to central banks in balance sheet reports required for the compilation of monetary statistics. Compilers need to ensure that the numerator and the denominator cover the same set of DTs. The ideal is to have frequent observations of positions, thus matching the data in the numerator. If less frequent observations of positions are available, then the numerator may capture flows unrelated to the amounts in the denominator. If loans or deposits in the denominator are valued at fair value, the implicit interest rate will move in line with changes in market rates.

10

For example, if during the period of the first quarter, there are end-month observations for December (200), January (100), February (200), and March (300), then St is the sum of the four observations (800) and T is the number of observations (4), so the denominator in the equation would be 800/4 = 200.

11

The amount of accrued interest in the numerator depends on the time over which the associated loans are outstanding. For instance, for a loan that is issued midway through the quarter, the numerator should capture accrued interest over one and one-half months only.

12

Specific provisions have already reduced profits, as well as capital and reserves, and thus are deducted from the denominator (that is, from loans).

13

Nonetheless, if significant, another SLDR could be calculated that excludes such prescribed lending and the average interest rate received. In such circumstances, there may be analytical interest in information on the total amount of such lending.

  • Afonso, Gara, Kovner Anna, and Schoar Antoinette. 2011. “Stressed, Not Frozen: The Federal Funds Market in the Financial Crisis.” Federal Reserve Bank of New York Staff Report, Number 437.

    • Search Google Scholar
    • Export Citation
  • Andrews, Michael A. 2017. “Experience with Financial Soundness Indicators; A Practitioner’s Perspective.” Paper prepared for the IMF Statistics Department Workshop on Financial Soundness Indicators, Washington, DC, April 25–26.

    • Search Google Scholar
    • Export Citation
  • Angelini, Paolo, Nobili Andrea, and Picillo M. Cristina. 2009. “The Interbank Market After August 2007: What Has Changed and Why?Banca d’Italia Working Papers, Number 731.

    • Search Google Scholar
    • Export Citation
  • Babihuga, Rita. 2007. “Macroeconomic and Financial Soundness Indicators: An Empirical Investigation.” IMF Working Paper 07/115, International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Bank for International Settlements. 2018. Annual Economic Report. https://www.bis.org/publ/arpdf/ar2018e.pdf

  • Bank Indonesia. 2017. Financial Stability Report, Indonesia. https://www.bi.go.id/en/publikasi/perbankan-dan-stabilitas/kajian/Pages/KSK_0917.aspx

    • Search Google Scholar
    • Export Citation
  • Bank of Canada. 2017. Financial Stability Report, Canada. https://www.bankofcanada.ca/wp-content/uploads/2017/11/fsr-november2017.pdf

  • Bank of England. 2016. The Financial Policy Committee’s Approach to Setting the Countercyclical Capital Buffer. London.

  • Bank of England. October 17, 2018. Financial Stability Report. Accessed April 27, 2018. https://www.bankofengland.co.uk/financial-stability

    • Search Google Scholar
    • Export Citation
  • Bank of France. 2018. Financial Stability Report, France. https://publications.banque-france.fr/sites/default/fles/medias/documents/financial_stability_Review_22.pdf

    • Search Google Scholar
    • Export Citation
  • Bank of Italy. 2017. Financial Stability Report, Italy. https://www.bancaditalia.it/pubblicazioni/rapporto-stabilita/2017-2/en-FSR-2-2017.pdf?language_id=1

    • Search Google Scholar
    • Export Citation
  • Bank of Japan. 2018. Financial Stability Report, Japan. https://www.boj.or.jp/en/research/brp/fsr/data/fsr180419a.pdf

  • Bank of Korea. 2017. Financial Stability Report, Korea. http://www.bok.or.kr/broadcast.action?menuNaviId=2578

  • Bank of Singapore. 2017. Financial Stability Report, Singapore. http://www.mas.gov.sg/~/media/resource/publications/fsr/FSR%202017.pdf

  • Bank of Spain. 2017. Financial Stability Report, Spain. https://www.bde.es/f/webbde/Secciones/Publicaciones/InformesBoletinesRevistas/InformesEstabilidadFinancera/17/IEF_Noviembre2017Ing.pdf

    • Search Google Scholar
    • Export Citation
  • Bank of Uganda. 2016. Financial Stability Report. https://www.bou.or.ug/bou/bou-downloads/financial_stability/Rpts/All/Financial-Stability-Report--June-2016.pdf.

    • Search Google Scholar
    • Export Citation
  • Basel Committee on Banking Supervision (BCBS). 1988. International Convergence of Capital Measurement and Capital Standards. Basel, Switzerland: Bank for International Settlements.

    • Search Google Scholar
    • Export Citation
  • Basel Committee on Banking Supervision (BCBS). 1992. Minimum Standards for the Supervision of International Banking Groups and their Cross-border Establishments. Basel, Switzerland: Bank for International Settlements.

    • Search Google Scholar
    • Export Citation
  • Basel Committee on Banking Supervision (BCBS). 1996. Amendment to the Capital Accord to Incorporate Market Risks. Basel, Switzerland: Bank for International Settlements.

    • Search Google Scholar
    • Export Citation
  • Basel Committee on Banking Supervision (BCBS). 2003a. New Basel Capital Accord: Third Consultative Paper. Basel, Switzerland: Bank for International Settlements.

    • Search Google Scholar
    • Export Citation
  • Basel Committee on Banking Supervision (BCBS). 2003b. Overview of the New Basel Capital Accord. Basel, Switzerland: Bank for International Settlements.

    • Search Google Scholar
    • Export Citation
  • Basel Committee on Banking Supervision (BCBS). 2006. International Convergence of Capital Measurement and Capital Standards. Basel, Switzerland: Bank for International Settlements.

    • Search Google Scholar
    • Export Citation
  • Basel Committee on Banking Supervision (BCBS). 2011. A Global Regulatory Framework for More Resilient Banks and Banking Systems. Basel, Switzerland: Bank for International Settlements.

    • Search Google Scholar
    • Export Citation
  • Basel Committee on Banking Supervision (BCBS). 2012a. Core Principles for Effective Banking Supervision. Basel, Switzerland: Bank for International Settlements.

    • Search Google Scholar
    • Export Citation
  • Basel Committee on Banking Supervision (BCBS). 2012b. Models and Tools for Macroprudential Analysis.” Working Paper No. 21. Basel, Switzerland: Bank for International Settlements.

    • Search Google Scholar
    • Export Citation
  • Basel Committee on Banking Supervision (BCBS). 2013. Basel III: The Liquidity Coverage Ratio and Liquidity Risk Monitoring Tools. Basel, Switzerland: Bank for International Settlements.

    • Search Google Scholar
    • Export Citation
  • Basel Committee on Banking Supervision (BCBS). 2014a. Basel III: The Net Stable Funding Ratio. Basel, Switzerland: Bank for International Settlements.

    • Search Google Scholar
    • Export Citation
  • Basel Committee on Banking Supervision (BCBS). 2014b. Minimum Capital Requirements for Market Risk (2016). Basel, Switzerland: Bank for International Settlements.

    • Search Google Scholar
    • Export Citation
  • Basel Committee on Banking Supervision (BCBS). 2014b. Supervisory Framework for Measuring and Controlling Large Exposures. Basel, Switzerland: Bank for International Settlements.

    • Search Google Scholar
    • Export Citation
  • Basel Committee on Banking Supervision (BCBS). 2017a. Finalization of Post Crisis Reforms. Basel, Switzerland: Bank for International Settlements.

    • Search Google Scholar
    • Export Citation
  • Basel Committee on Banking Supervision (BCBS). 2017b. Supervisory and Bank Stress Testing: Range of Practices. Basel, Switzerland: Bank for International Settlements.

    • Search Google Scholar
    • Export Citation
  • Basel Committee on Banking Supervision (BCBS). 2017c. Regulatory Treatment of Accounting Provisions–Interim Approach and Transitional Arrangement. Basel, Switzerland: Bank for International Settlements.

    • Search Google Scholar
    • Export Citation
  • Basel Committee on Banking Supervision (BCBS). 2019. Minimum Capital Requirements for Market Risk. Basel, Switzerland: Bank for International Settlements.

    • Search Google Scholar
    • Export Citation
  • Beck Torsten, Demirgüç-Kunt Asli, and Levine Ross. 2006. “Bank Concentration, Competition, and Crises: First Results.” Journal of Banking and Finance 30(5): 1581603.

    • Search Google Scholar
    • Export Citation
  • Bergo, Jarle. 2002. “Using Financial Soundness Indicators to Assess Financial Stability.” Paper presented at Challenges to Central Banking from Globalized Financial Systems, IMF Washington, DC, September 16–17.

    • Search Google Scholar
    • Export Citation
  • Bessis, Joel. 2015. Risk Management in Banking, fourth edition. West Sussex: John Wiley & Sons.

  • Bluedorn, John, Rupa Duttagupta, Jaime Guajardo, and Petia Topalova. 2013. “Capital Flows Are Fickle: Anytime, Anywhere.” IMF Working Paper 13/183, August, International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Borio, Claudio. 2003. “Towards a Macroprudential Framework for Financial Supervision and Regulation?BIS Working Papers No 128. Basel, Switzerland: Bank for International Settlements.

    • Search Google Scholar
    • Export Citation
  • Borio, Claudio. 2014. “Macroprudential Frameworks: (Too) Great Expectations?” In Macroprudentialism, edited by Dirk Schoenmaker. London: Centre for Economic Policy Research, pp. 2946.

    • Search Google Scholar
    • Export Citation
  • Borio, Claudio, and Mathias Drehmann. 2009. “Towards an Operational Framework for Financial Stability: “Fuzzy” Measurement and Its Consequences.” BIS Working Papers No. 248. Basel, Switzerland: Bank for International Settlements.

    • Search Google Scholar
    • Export Citation
  • Borio, Claudio, Mathias Drehmann, and Kostas Tsatsaronis. 2012. “Stress Testing Macro-Stress Testing: Does It Live Up to Expectations?BIS Working Papers No. 369. Basel, Switzerland: Bank for International Settlements.

    • Search Google Scholar
    • Export Citation
  • Boyd, John H., and David E. Runkle. 1993. “Size and Performance of Banking Firms: Testing the Predictions of Theory.” Journal of Monetary Economics 31(1): 4767.

    • Search Google Scholar
    • Export Citation
  • Bundesbank. 2017. Financial Stability Report, Germany. https://www.bundesbank.de/Redaktion/EN/Downloads/Publications/Financial_Stability_Review/2017_financial_stability_review.pdf?__blob=publicationFile

    • Search Google Scholar
    • Export Citation
  • Bussière, Matthieu. 2013. “In Defense of Early Warning Signals.” Working Paper No. 420. Paris: Banque du France.

  • Cabello, Miguel, Jose Lupu, and Minaya Elias. 2017. “Macroprudential Policies in Peru: The effects of Dynamics Provisioning and Conditional Reserve Requirements.” Working Paper No. 2017–002. Lima: Banco Central de Reserva Del Peru.

    • Search Google Scholar
    • Export Citation
  • Carson, Carol. 2001. “Toward a Framework for Assessing Data Quality.” IMF Working Paper 01/25. International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Catalán, Mario, and Dimitri Demekas. 2015. “Challenges for Systemic Risk Assessment in Low-Income Countries.” Journal of Risk Management in Financial Institutions 8(2): 11829.

    • Search Google Scholar
    • Export Citation
  • Central Bank of Argentina. 2017. Financial Stability Report, Argentina. http://www.bcra.gob.ar/Pdfs/PublicacionesEstadisticas/ief0217i.pdf

    • Search Google Scholar
    • Export Citation
  • Central Bank of Brazil. 2017. Financial Stability Report, Brazil. http://www.bcb.gov.br/?fsr201710

  • Central Bank of Nigeria. 2016. Financial Stability Report. Accessed December 27, 2017. https://www.cbn.gov.ng/out/2017/fprd/fsr%20december%202016%20(2).pdf

    • Search Google Scholar
    • Export Citation
  • Central Bank of the Republic of Turkey. 2017. Financial Stability Report, Turkey. http://www.tcmb.gov.tr/wps/wcm/connect/6c95b5fe-4815-4064-a9a4-80ff33b51906/fulltext25.pdf?MOD=AJPERES&CACHEID=ROOTWORKSPACE-6c95b5fe-4815-4064-a9a4-80f33b51906-m52f977.

    • Search Google Scholar
    • Export Citation
  • Central Bank of Russia. 2017. Financial Stability Report, Russia. http://www.cbr.ru/Eng/publ/Stability/OFS_17-02_e.pdf

  • Choudhry, Moorad. 2012. The Principles of Banking. Singapore: John Wiley & Sons.

  • Čihák, Martin. 2006. “How Do Central Banks Write on Financial Stability.” IMF Working Paper 06/163. International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Čihák, Martin. 2014. “Stress Tester: A Toolkit for Bank-By-Bank Analysis.” In A Guide to IMF Stress Testing: Models and Methods, edited by Li Ong (Washington, DC: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Čihák, Martin, Sonia Muñoz, Shakira Teh Sharifuddin, and Kalin Tintchev. 2012. “Financial Stability Reports: What Are They Good For?IMF Working Paper 12/1. International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Čihák, Martin, and Klaus Schaech. 2007. “How Well Do Aggregate Bank Ratios Identify Banking Problems.” IMF Working Paper 07/275. International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Claessens, Stijn. 2014. “A n Overview of Macropru-dential Policy Tools.” IMF Working Paper 14/214. International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Committee on the Global Financial System. 2010. “Macroprudential Instruments and Frameworks: A Stocktaking of Issues and Experience.” CGFS Paper No. 28. Basel, Switzerland: Bank for International Settlements.

    • Search Google Scholar
    • Export Citation
  • Committee on the Global Financial System. 2016. “Objective-Setting and Communication of Macroprudential PoliciesCGFS Paper No. 57. Basel, Switzerland: Bank for International Settlements.

    • Search Google Scholar
    • Export Citation
  • Costa Navajas, Matias, and Aaron Tegeya. 2013. “Financial Soundness Indicators and Banking Crises.” IMF Working Paper 13/263. International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Craig, Sean R. 2002. “Role of Financial Soundness Indicators in Surveillance: Data Sources, Users and Limitations.” IFC Bulletin No. 12. Basel, Switzerland: Bank for International Settlements, pp. 199209.

    • Search Google Scholar
    • Export Citation
  • Crowley, Joseph, Plapa Koukpamou, Elena Lou-koianova, and André Mialou. 2016. “Pilot Project on Concentration and Distribution Measures for a Selected Set of Financial Soundness Indicators.” IMF Working Paper WP/16/26. International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • De Haan, Jakob, and Poghosyan Tigran. 2012. “Bank Size, Market Concentration, and Bank Earnings Volatility in the US.” Journal of International Financial Markets, Institutions and Money 22(1): 3554.

    • Search Google Scholar
    • Export Citation
  • De Nederlandsche Bank. 2017. Financial Stability Report, Netherlands. https://www.dnb.nl/en/binaries/OFS_Autumn%202017_tcm47-363954.pdf

  • Demirgüç-Kunt, Asli, and Enrica Detragiache. 2005. “Cross-Country Empirical Studies of Systemic Bank Distress: A Survey.” IMF Working Paper 05/96. International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Deutsche Bundesbank. 2006a. Concentration and Risk in Credit Portfolios, Monthly Report, June.

  • Deutsche Bundesbank. 2006b. Financial Stability Review, November. https://www.bundesbank.de/resource/blob/621872/a0c2a5a4a9bae205a74b7149f7e709b2/mL/2006-fnanzstabilitaetsbericht-data.pdf

    • Search Google Scholar
    • Export Citation
  • Drehmann, Mathias, and Mikael Juselius. 2013. “Evaluating Early Warning Indicators of Banking Crises: Satisfying Policy Requirements.” BIS Working Papers No. 42. Basel, Switzerland: Bank for International Settlements.

    • Search Google Scholar
    • Export Citation
  • Emmer, Susanne, and Dirk Tasche. 2005. “Calculating Credit Risk Capital Charges with the One-factor Model.” The Journal of Risk 7(2): Winter.

    • Search Google Scholar
    • Export Citation
  • European Commission. 2015. “Commission Implementing Regulation (EU) 2015/1278.” Brussels.

  • European Commission. 2018. “Commission Implementing Regulation (EU) 2018/292.” Brussels.

  • European Parliament. 2009. “Solvency II, Directive 2009/138/EC.” Official Journal of the European Union, Brussels, pp. L335/1L335/155.

    • Search Google Scholar
    • Export Citation
  • European Systemic Risk Board. 2017. ESRB Dashboard (November).

  • European Union. 2017. 2017 Financial Stability Report, European Union. https://www.ecb.europa.eu/pub/pdf/other/ecb.financialstabilityreview201711.en.pdf?7a775eed7ede9aee35acd83d2052a198

    • Search Google Scholar
    • Export Citation
  • Eurostat. 2013. Handbook on Residential Property Prices Indices. Luxembourg.

  • Evans, Owen, Alfredo Leone, Mahinder Gill, and Paul Hilbers 2000. Macroprudential Indicators of Financial System Soundness. Occasional Paper 192. Washington, DC: International Monetary Fund.

    • Search Google Scholar
    • Export Citation
  • Evrensel, Ayşe. 2008. “Banking Crisis and Financial Structure: A Survival-Time Analysis.” International Review of Economics and Finance 17(4): 589602.

    • Search Google Scholar
    • Export Citation
  • Fjármálaeftirlitið (Iceland Financial Supervisory Authority), Rules on Maximum Loan-to-Value Ratios for Mortgages. July 2017. https://en.fme.is/media/frettir/FME---LTV-Memorandum-July-2017.pdf">https://en.fme.is/media/frettir/FME---LTV-Memorandum-July-2017.pdf.

    • Search Google Scholar
    • Export Citation
  • Gadanecz, Blaise, and Jayaram Kaushik. 2015. “Macroprudential Policy Frameworks, Instruments and Indicators: A Review.” IFC Bulletin No. 41. Basel, Switzerland: Bank for International Settlements.

    • Search Google Scholar
    • Export Citation
  • Galati, Gabriele and Richhild Moessner. 2011. “Macroprudential Policy—A Literature Review.” BIS Working Papers No. 337. Basel, Switzerland: Bank for International Settlements.

    • Search Google Scholar
    • Export Citation
  • Goodhart, Charles. 2014. “The Use of Macroprudential Instruments.” In Macroprudentialism, edited by Dirk Schoenmaker (London: Centre for Economic Policy Research), pp. 1120.

    • Search Google Scholar
    • Export Citation
  • Gordi, Michael B. 2003. “A Risk Factor Model Foundation of Ratings-based Bank Capital Rules.” Journal of Financial Intermediation 12(3): 199232.

    • Search Google Scholar
    • Export Citation
  • Gorton, Gary. 2008. “The Panic of 2007.” Working Paper No. 14358. The National Bureau of Economic Research.

  • Grippa, Pierpaolo, and Lucyna Gornica. 2016. “Measuring Concentration Risk—A Partial Portfolio Approach.” IMF Working Paper WP/16/158. International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Hong Kong Monetary Authority. May 19, 2017. “Press Release.” http://www.hkma.gov.hk/eng/key-information/press-releases/2017/20170519-5.shtml.

    • Search Google Scholar
    • Export Citation
  • Hong Kong Monetary Authority. 2018. Financial Stability Report, Hong Kong SAR. http://www.hkma.gov.hk/media/eng/publication-and-research/quarterly-bulletin/qb201803/E_Half-yearly_201803.pdf

    • Search Google Scholar
    • Export Citation
  • Hull, John C. 2015. Risk Management and Financial Institutions, fourth edition. Hoboken, NJ: John Wiley & Sons.

  • Hyndman, Rob J., and Yanan Fan. 1996. “Sample Quantiles in Statistical Packages.” The American Statistician 50(4): 36165.

  • IFC Bulletin No. 41. 2016. Combining Micro and Macro Statistical Data for Financial Stability. Basel, Switzerland: Bank for International Settlements.

    • Search Google Scholar
    • Export Citation
  • IFC Bulletin No. 46. 2017. Data Needs and Statistics Compilation for Macroprudential Analysis. Basel, Switzerland: Bank for International Settlements.

    • Search Google Scholar
    • Export Citation
  • International Accounting Standards Board. 2004. “Provisions, Contingent Liabilities and Contingent Assets.” International Financial Reporting Standards 37.

    • Search Google Scholar
    • Export Citation
  • International Accounting Standards Board. May 2011. “Fair Value Measurement.” International Financial Reporting Standards 13.

  • International Accounting Standards Board. July 2014. International Financial Reporting Standards 9.

  • International Accounting Standards Board. 2018. “Conceptual Framework for Financial Reporting.” Paragraph 4.54.

  • International Monetary Fund, European Commission, Organization for Economic Cooperation and Development, United Nations, and the World Bank. 2009c. 2008 System of National Accounts. New York.

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund, Financial Stability Board, and Bank for International Settlements. 2016a. Elements of Effective Macroprudential Policies: Lessons from International Experience. https://www.imf.org/external/np/g20/pdf/2016/083116.pdf">https://www.imf.org/external/np/g20/pdf/2016/083116.pdf.

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund. 2003a. External Debt Statistics: Guide for Compilers and Users. Washington, DC.

  • International Monetary Fund. 2003b. Financial Soundness Indicators. http://www.imf.org/external/np/sta/fsi/eng/2003/051403.htm

  • International Monetary Fund. 2006. Financial Soundness Indicators Compilation Guide. Washington, DC.

  • International Monetary Fund. 2009a. Balance of Payments and International Investment Position Manual. https://www.imf.org/external/pubs/f/bop/2007/pdf/bpm6.pdf

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund. 2009b. The Financial Crisis and Information Gaps: Report to the G-20 Finance Ministers and Central Bank Governors. https://www.imf.org/external/np/g20/pdf/102909.pdf

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund. 2009c. Global Financial Stability Report. https://www.imf.org/~/media/Websites/IMF/imported.../GFSR/2009/01/.../_textpdf.ashx

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund. 2013a. External Debt Statistics Guide for Compilers and Users. Washington, DC.

  • International Monetary Fund. 2013b. Modifications to the Current List of Financial Soundness Indicators. http://www.imf.org/external/np/pp/eng/2013/111313.pdf

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund. 2013c. Modifications to the Current List of Financial Soundness Indicators—Background Paper. https://www.imf.org/external/np/pp/eng/2013/111313b.pdf

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund. 2014a. Rising Challenges, Regional Economic Outlook. Washington, DC.

  • International Monetary Fund. 2014b. Government Finance Statistics Manual. Washington, DC.

  • International Monetary Fund. 2014c. Staff Guidance Note on Macroprudential Policy. http://www.imf.org/external/np/pp/eng/2014/110614.pdf

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund. 2014d. Staff Guidance Note on Macroprudential Policy—Considerations for Low Income Countries. http://www.imf.org/external/np/pp/eng/2014/110614b.pdf

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund. 2014e. Sustaining the Momentum: Vigilance and Reforms, Regional Economic Outlook. Washington, DC.

  • International Monetary Fund. 2015. The Handbook on Securities Statistics. Washington, DC.

  • International Monetary Fund. 2016b. Financial Stability Report, Mexico. http://www.banxico.org.mx/publicaciones-y-discursos/publicaciones/informes-periodicos/reporte-sf/%7B838A2500-845F-2BC0-DF4A-167F4601542F%7D.pdf

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund. 2016c. Financial Stability Report, China. http://www.pbc.gov.cn/english/130736/3130899/index.html

  • International Monetary Fund. 2016d. Managing Transitions and Risks, Regional Economic Outlook. Washington, DC.

  • International Monetary Fund. 2016e. Monetary and Financial Statistics Manual and Compilation Guide. Washington, DC.

  • International Monetary Fund. 2017. “Experience with Financial Soundness Indicators; A Practitioner’s Perspective.” Paper prepared for the IMF Statistics Department Workshop on Financial Soundness Indicators, Washington, DC, April 25–26.

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund. 2018. World Economic Outlook. http://www.imf.org/external/pubs/f/weo/2018/01/weodata/weoselagr.aspx.

  • Islamic Finance Services Board. 2013. Revised Capital Adequacy for Institutions Offering Islamic Financial Services. Kuala Lumpur, Malaysia.

    • Search Google Scholar
    • Export Citation
  • Islamic Finance Services Board. 2017. PSIFI Compilation Guide. Kuala Lumpur, Malaysia.

  • Israël, Jean-Marc, Patrick Sandars, Aurel Schubert, and Björn Fischer. 2013. “Statistics and Indicators for Financial Stability Analysis and Policy.” Occasional Paper Series No. 145. Frankfurt: European Central Bank.

    • Search Google Scholar
    • Export Citation
  • Jobst, Andreas, Li Ong, and Christian Schmieder. 2013. “A Framework for Macroprudential Bank Solvency Stress Testing: Application to S-25 and Other G-20 Country FSAPs.” IMF Working Paper 13/68. International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Maino, Rodolfo, and Steven Barnett, eds. 2013. Macroprudential Frameworks in Asia. Washington, DC: International Monetary Fund.

  • Mishkin, Frederic S. 1999. “International Experiences with Different Monetary Policy Regimes.” NBER Working Paper No. 6965.

  • Mueller, Glenn. 2002. “What Will the Next Real Estate Cycle Look Like?Journal of Real Estate Portfolio Management 8(2): 11525.

  • National Bank of Belgium. 2017. Financial Stability Report, Belgium. https://www.nbb.be/doc/ts/publications/fsr/fsr_2017.pdf

  • National Bank of Georgia. 2011. Financial Stability Report. https://www.nbg.gov.ge/uploads/publications/fnstability/fnans_stabil_web_2011new.pdf.

    • Search Google Scholar
    • Export Citation
  • O’Hara, Maureen, and Wayne Shaw. 1990. “Deposit Insurance and Wealth Effects: The Value of Being ’Too Big to Fail’.” Journal of Finance 45(5): 1587600.

    • Search Google Scholar
    • Export Citation
  • Ong, Li. 2014. A Guide to IMF Stress Testing: Methods and Models. Washington, DC: International Monetary Fund.

  • Parzen, Emanuel. 1979. “Nonparametric Statistical Data Modeling.” Journal of the American Statistical Association 74(365): 10521.

    • Search Google Scholar
    • Export Citation
  • Reinhart, Carmen M., and Kenneth N. Rogoff. 2011. “From Financial Crash to Debt Crisis.” American Economic Review 101(5): 1676706.

  • Reserve Bank of Australia. 2018. Financial Stability Report, Australia. http://www.rba.gov.au/publications/fsr/2018/apr/

  • Reserve Bank of India. 2017. Financial Stability Report, India. https://rbidocs.rbi.org.in/rdocs/PublicationReport/Pdfs/0FSR201730210986ADDA44E2A946A3F6C4408581.PDF

    • Search Google Scholar
    • Export Citation
  • Reserve Bank of South Africa. 2018. Financial Stability Report, South Africa. https://www.resbank.co.za/Lists/News%20and%20Publications/Attachments/8420/FSR%20First%20Edition%202018.pdf

    • Search Google Scholar
    • Export Citation
  • Saudi Arabia Monetary Authority. 2017. Financial Stability Report, Saudi Arabia. http://www.sama.gov.sa/en-US/EconomicReports/Financial%20Stability%20Report/Financial%20Stability%20Report%202017-EN.PDF

    • Search Google Scholar
    • Export Citation
  • Schmieder, Christian, Claus Puhr, and Maher Hasan. 2011. “Next Generation Balance Sheet Stress Testing.” IMF Working Paper 11/83. International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Silver, Mick. 2013. “Understanding Commercial Property Price Indexes.” World Economics 14(3): 2741.

  • Smaga, Pawel. 2014. “The Concept of Systemic Risk.” SRC Special Paper No. 5 . London School of Economics.

  • Sundararajan, Vasudevan, Charles Enoch, Armida San Jose, Paul Louis Ceriel Hilbers, Russell Krueger, Marina Moretti, and Graham Slack. 2002. “Financial Soundness Indicators: Analytical Aspects and Country Practices.” Occasional Paper No. 212. International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Sveriges Riksbank. 2017. Financial Stability Report, Sweden. https://www.f.se/contentassets/3613a7a9f24e425c8b6dfe6e861d6567/stab2-17_engny.pdf and https://www.riksbank.se/en-gb/financial-stability/financial-stability-report/2017/financial-stability-report-20172/

    • Search Google Scholar
    • Export Citation
  • Swiss National Bank. 2017. Financial Stability Report, Switzerland. https://www.snb.ch/en/mmr/reference/stabrep_2017/source/stabrep_2017.en.pdf

    • Search Google Scholar
    • Export Citation
  • United Nations. 2008. International Standard Industrial Classification of All Economic Activities, Statistical Papers, Series M, Number 4/Rev.4 (New York). https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf

    • Search Google Scholar
    • Export Citation
  • United States Treasury. 2017. Financial Stability Report, United States. https://www.financialresearch.gov/financial-stability-reports/fles/OFR_2017_Financial-Stability-Report.pdf and https://www.treasury.gov/initiatives/fsoc/studies-reports/Documents/FSOC_2017_Annual_Report.pdf

    • Search Google Scholar
    • Export Citation
  • Worrell, DeLisle. 2004. “Quantitative Assessment of the Financial Sector: An Integrated Approach.” IMF Working Paper 04/153. International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation