Abstract

9.1 This chapter defines financial soundness indicators (FSIs) for the OFCs sector and three of its subsectors, explains how they are to be calculated, and deals with data sources and specific compilation issues.

I. Introduction

9.1 This chapter defines financial soundness indicators (FSIs) for the OFCs sector and three of its subsectors, explains how they are to be calculated, and deals with data sources and specific compilation issues.

9.2 The three subsectors include: money market funds (MMFs), insurance corporations (ICs), and pension funds (PFs). All other institutional units performing financial intermediation or auxiliary functions that do not include accepting deposits are combined in the OFCs sector. As explained in Chapter 2, the OFCs not enumerated earlier comprise a diverse group of units, including finance companies, financial leasing companies, non-MMF investment funds, securitization vehicles, and financial auxiliaries. For these units, only indicators of their share within the total financial system and their size relative to the gross domestic product (GDP) need to be compiled.

9.3 Countries are encouraged to compile for their own purposes data and indicators for additional financial sub-sectors when these are relevant to financial stability analysis. Non-MMF investment funds may be significant investors in financial and nonfinancial corporations.

II. Consolidation Basis

9.4 The two FSIs that measure the relative size of the OFC sector are calculated on a residency and institutional unit basis. Consequently, the data should be presented on an aggregated resident-based approach, except for countries with OFCs that have significant cross-border activities, for which cross-border consolidation may be relevant, as described in Chapter 6.

9.5 The Guide recommends compiling additional FSIs for ICs consolidating flows and positions of parent ICs with the flows and positions of their domestic and foreign branches and subsidiaries in the IC industry, following a cross-border, domestically incorporated (CBDI) consolidation basis approach.

9.6 For ICs whose parent is a resident DT, Chapter 6 discussed how data of ICs are not consolidated with flows and positions of their parent’s DT when FSIs are compiled for deposit takers (DTs). Therefore, data from ICs are not captured in the core and additional FSIs for DTs. As a result, there are no issues of overlapping or risk of double counting between ICs and DTs.

9.7 The Guide recommends compiling additional FSIs for MMFs and PFs following an aggregated resident-based approach.

III. Calculation of Financial Soundness Indicators for OFCs

9.8 As for the deposit-taking (DT) sector, most FSIs for the other sectors are calculated by comparing two underlying series to produce a ratio. For some FSIs, when one or both of the underlying series can be defined in alternative ways, these alternatives are explained. Annex 9.1 summarizes the recommended FSIs for the OFC sector.

Other Financial Corporations

9.9 The list of additional FSIs for OFCs includes two indicators for the whole sector of OFCs, measuring their relative size within the financial sector and within the domestic economy. To assess the relevance of the three subsectors specifically identified in the Guide, the same indicators should be compiled for each of these subsectors.

9.10 The two indicators measuring the relative size of the OFC sector are:

  • OFCs’ assets to total financial system assets; and,

  • OFCs’ assets to GDP.

9.11 These two indicators are described further. The data to be used to calculate these FSIs are obtained either from aggregating individual balance sheets of each institutional unit of the OFC sector, or through estimates provided by the responsible supervisory authorities. If neither source is available, an alternative is flow of funds accounts, in which estimates of OFC assets may be built up from counterpart data.

Other financial corporations’ assets to total financial system assets

9.12 The FSI OFCs’ assets to total financial system assets provides a metric to gauge the relative magnitude of the OFCs sector within the domestic financial system.

9.13 This FSI is a ratio where the numerator is OFCs’ total assets and the denominator is total financial system assets, excluding the central bank. As such, the denominator includes only the total assets owned by DTs and OFCs (including the sum of DT total assets, line 14 in Table 5.1; MMF total assets, line 10 in Table 5.2; IC total assets, line 14 in Table 5.3; and PF total assets, line 11 in Table 5.4 plus other OFCs not listed here).

9.14 The Guide recommends similar FSIs for the three subsectors of the OFCs sector. For MMFs, the numerator is total assets of this subsector; for ICs, it is the total assets of this subsector; and for PFs, their total assets. In all three cases, the denominator is total financial system assets, excluding the central bank, which is the same as the denominator for the whole OFC sector.

9.15 The FSI for OFCs’ assets to total financial system assets and the FSIs for the three OFC subsectors are calculated using aggregated data of the resident financial institutions, or alternatively from flow of funds data in which estimates of OFC assets may be built up from counterpart data. Data for total assets of OFCs and DTs can be obtained from the aggregated balance sheets of each subsector and sector.

9.16 It should be noted that aggregated data of total DT assets will be different from total assets used for some core and additional FSIs for DTs, if the latter are calculated using a consolidation basis other than the aggregated resident-based approach, such as cross-border, cross-sector, domestically incorporated (CBCSDI) or cross-border, cross-sector, domestically-controlled (CBCSDC).

9.17 Data sources for the three targeted subsectors of the OFC sector are the aggregated balance sheets of those subsectors.

9.18 Data for one or more of the OFCs subsectors may not be available to compilers if these subsectors are not regulated or do not have an obligation to submit financial information to any authority. In cases where these subsectors are regulated by agencies other than the lead FSI agency, compilers should coordinate with those agencies the regular transmission of the needed information, ideally through a formal agreement.

The Relative Size of the OFC Sector

The magnitude of the OFC sector, and hence its relative size within the financial sector, varies greatly across economies. The graph, based on a sample of data reported to the IMF’s Statistics Department, shows participations going from as low as 6.7 percent (Albania) to 42.1 percent (Mexico) of the total financial system assets.

This FSI for OFCs provides compilers with a metric to assess the importance of devoting additional resources for the collection of data from the sector and its subsectors. Clearly, the larger the size of the OFC sector within the financial system, the higher the value added from additional information.

Figure 9.1.1.
Figure 9.1.1.

Total Financial System Assets of Selected Countries

Source: IMF staff calculations.
Other financial corporations’ assets to nominal gross domestic product

9.19 The FSI OFCs’ assets to nominal gross domestic product provides a metric to gauge the volume of the OFCs sector within the overall economy.

9.20 This FSI is a ratio where the numerator is total assets of the OFCs sector and the denominator is nominal gross domestic product (GDP). Nominal GDP is an aggregate measure of production in the economy, equal to the sum of the gross value added of all resident institutional units engaged in production.

9.21 As for the previous FSI, indicators for the three subsectors can also be calculated as a ratio to nominal GDP.

9.22 Source data for the numerator are the same as elaborated in paragraph 9.13, with nominal GDP data for the denominator available from national accounts.

9.23 Compilation issues are the same as the ones elaborated in paragraphs 9.16 and 9.18 for total assets.

Money Market Funds

9.24 The systemic relevance of money market funds (MMFs) varies across countries and can have a substantial impact on financial stability. Like other mutual funds, investors in MMFs are considered as shareholders and they are entitled to receive the value of each share with its accumulated income, but the yield is not predetermined. MMFs compete with banks for funds although an important distinction is that investments in MMFs are generally not covered by deposit insurance schemes.

9.25 MMFs invest in high-quality, short-term, income instruments, such as treasury bills, commercial paper, certificates of deposits, and repurchase agreements. Despite the relatively high quality of the invested instruments, their maturity and their issuing sectors can have an impact on asset quality.

9.26 In some countries, MMFs and DTs are closely linked because the MMFs provide short-term funding to DTs by investing in instruments issued by DTs. In this case, a run on MMFs could have an impact on DTs’ short-term liquidity. The maturity transformation through MMFs is also relevant for financial stability analysis, as some of MMFs’ assets have maturities of more than 90 days while balances in their share accounts may be withdrawn on demand.

9.27 The Guide recommends compiling two FSIs to gauge the credit and liquidity risk of the investment portfolios of MMFs: (1) sectoral distribution of investments and (2) maturity distribution of investments.

Sectoral distribution of MMFs’ investments

9.28 The FSI sectoral distribution of MMFs’ investments provides some measure of the risk exposure faced by MMFs’ investments. Although in principle, MMFs invest only in high-quality financial assets, their investments are nevertheless subject to counterparty risk. In particular, since MMFs’ shares/units are not protected by deposit insurance, a perception that MMFs’ investments are placed with subprime counterparts might intensify the risk of a run against MMFs in times of financial instability. Also, MMFs could be prone to runs if a significant shortfall emerges between the value of their underlying assets and their liabilities. This FSI gives information on the quality of assets held by MMFs based on the sectoral distribution of the issuers of those assets.

9.29 This FSI presents MMFs investments broken down into domestic economic sectors and nonresidents. Following the definitions of the System of National Accounts (SNA), the domestic economic sectors are grouped into: central bank, DTs (correspond to the SNA’s sector deposit-taking corporations except the central bank), OFCs (all, including MMFs),1 general government, and nonfinancial corporations. The investment by sector is presented as a ratio to the total MMFs’ investments.

9.30 Source data for this indicator are the sectoral balance sheets of MMFs, which should identify their financial investments by counterpart economic sector. Total investment should be calculated from the asset side of the sectoral balance sheet (see Table 5.2). If the counterpart sectors of MMFs’ investments are not identified in their balance sheets, memorandum series need to be compiled on the sectoral distribution of their investments.

9.31 Sectoral analysis is a concept used in national accounts that classifies institutional units according to the nature of their economic activity. For this reason, this indicator is a general measure of credit risk, but it does not provide a measure of risk within an economic sector according to, for instance, the industry within the nonfinancial sector (e.g., extractive, commerce, tourism).

9.32 Availability of data for MMFs may vary by jurisdiction. Compilers may be able to obtain the data from the relevant regulatory authority, or directly from MMFs. In this case, similar considerations as the ones described for the two previous FSIs for OFCs apply here (see paragraphs 9.17 and 9.22).

Maturity distribution of MMFs’ investments

9.33 The FSI maturity distribution of MMFs’ investments provides a measure of the liquidity of MMFs’ investments by breaking down the maturity structure of assets of the MMF sector. The maturity transformation through MMFs is relevant for financial stability analysis as MMFs’ assets typically have longer maturities than their liabilities, which may be withdrawn on demand. The liquidity problems might be exacerbated if DTs also have investments in MMFs, as DTs may withdraw funds from their share accounts with MMFs to avoid potential losses.

9.34 Beyond the short-term analysis, the usefulness of this indicator is to show how the maturity of MMFs’ assets is evolving through time, serving as an early warning of possible problems in cases where the term structure is deteriorating.

9.35 This FSI is defined as the distribution of MMFs’ assets in three brackets: 1 to 30 days, 31 to 90 days, and more than 90 days (see Table 5.2, line 25 i– iii). This FSI is presented as a ratio, where the numerator is the volume of assets invested in each maturity bracket and the denominator is total investments of MMFs.

9.36 Source data for this indicator requires additional information not contained in the sectoral balance sheet of MMFs, as presented in Table 5.2. Memorandum series need to be reported by MMFs, breaking down their investments by maturity in the three brackets required for this indicator.

9.37 Compilers will need to rely on supervisory data or data collected directly from MMFs. In such cases, they will need to coordinate the reporting and data sharing of these supplementary series with the relevant supervisory authority, if any.

9.38 For this FSI, the preferred maturity is the remaining maturity of the MMFs’ asset holdings. However, if remaining maturity is not available, original maturity can be reported, but it should be explained in the corresponding metadata.

9.39 Much of the earlier provided discussion also pertains on Non-MMF Mutual Funds. Countries are encouraged for their own purposes to compile similar FSIs for Non-MMF Mutual Funds when they are significant in their country. The Non-MMF Mutual Funds may present a wider range of financial stability issues because of the diversity of their investments, wide maturity range, currency mix, possible use of derivatives, and possible withdrawal restrictions.

Insurance Corporations

9.40 Insurance corporations provide financial benefits to policyholders through risk-sharing and risk-transfer contracts. Main types of insurance include:

  • life or long-term insurance and

  • nonlife insurance (including reinsurance).

9.41 The Guide recommends the compilation of specific FSIs separately for both the life and nonlife ICs, as the two industries are very different in terms of products they offer and resulting balance sheet structure as well as risks they face.

9.42 Insurance is based on probability theory, where the price (insurance premium) is set before knowing an exact cost of the product (insurance contract or policy). ICs broadly face two main types of risks: (i) technical risks and (ii) investment risks.

9.43 Technical risks stem from the very nature of insurance business. Policyholders buy protection against occurrence of defined events whose occurrence is uncertain, and insurers set reserves against the projected total cost of claims. Insurance liabilities are projected using actuarial techniques. If these projections are incorrect, premiums may be insufficient and liabilities may be understated, which may result in both solvency and liquidity problems.

9.44 Investment risks affect the value, performance, return, liquidity, and structure of ICs’ investment portfolio. While liquidity risks are not dominant for ICs, they are exposed to market risks arising from changes in interest rates, exchange rates, and asset prices (equity, securities, and real estate); and counterparty credit risks.

9.45 ICs may also contract reinsurance, which is insurance provided by one insurer (usually specializing in reinsurance) to another, whereby the reinsurer agrees, in exchange for a premium, to indemnify the latter for losses on one or more contracts that it has issued. Much reinsurance business is cross-border and must be appropriately accounted for in the compiled data.

9.46 Against this backdrop, six FSIs for ICs are to be compiled, covering two broad categories of financial soundness separately for life and non-life IC: (i) capital adequacy and (ii) earnings and profitability. These six FSIs are presented in the following sub-sections.

Shareholder equity to invested assets (life and nonlife insurance)

9.47 The FSIs shareholder equity to invested assets are both a measure of capital adequacy and leverage. Unlike banking, there is no accepted international standard for capital adequacy for insurance companies. Regional or national standards for capital adequacy in advanced economies have common features of a ratio in which the numerator is an amount of capital determined for prudential purposes and not taken directly from financial statements, and a denominator which is a risk-based determination of the required amount of capital.2 A ratio of 100 percent or above is usually required and expected in normal operating conditions. In contrast to the complex calculations within these types of capital adequacy frameworks, the Guide uses a balance sheet measure of capital and reserves (line 30 in Table 5.3), defined as the difference between total assets (line 14 in Table 5.3) and Liabilities (line 24 in Table 5.3), as an indicator of capital adequacy.

9.48 Capital adequacy is one of the key indicators of ICs’ financial soundness, measuring the corporations’ capital strength to absorb losses. This FSI focuses on the amount of capital that is available to meet potential losses from insurance corporations’ investments. Additionally, this total provides an indication of the financial leverage of ICs; that is, the extent to which their assets are funded by sources other than their own capital.

9.49 This FSI uses IC capital and reserves (line 30 in Table 5.3) as the numerator. The denominator is the sum of ICs’ holdings of currency and deposits, loans, debt securities, equity and investment fund shares, other financial assets, and financial derivatives (line 16 in Table 5.3), plus their nonfinancial assets held for investment purposes (line 15.ii in Table 5.3).

9.50 Source data are the sectoral balance sheets of life and nonlife ICs. Since the indicator is calculated using a CBDI consolidation basis, positions of domestically incorporated ICs vis-à-vis their resident and nonresident IC subsidiaries should be eliminated. Data of each domestically incorporated IC in the reporting population—consolidating its positions with its IC subsidiaries—should be available to supervisors.

9.51 Shareholder equity is measured as the accounting concept of capital and reserves (line 30 in Table 5.3). For CBDI consolidated data, investment in resident and nonresident subsidiaries is deducted from the overall capital in the sector, so that capital and reserves held within the sector are not double counted.

9.52 Unlike in the case of DTs, where total assets are included in the denominator, only invested assets are used to calculate the denominator here, excluding nonfinancial assets not held for investment purposes and reinsurance claims.

9.53 Compilers will likely require supervisory data. In cases where the lead agency for compiling FSIs is not also the insurance supervisor, compilers should coordinate with the relevant agency to ensure the regular transmission of the needed information, ideally through a formal agreement.

Combined ratio (nonlife insurance)

9.54 The FSI combined ratio should be calculated only for nonlife ICs.3 This ratio measures the profitability of a given year’s insurance underwriting, calculated as the sum of net incurred losses and underwriting expenses divided by net earned premiums, expressed as a percent. For nonlife insurers operating in a healthy market, this ratio should be less than 100 percent, indicating profitable underwriting. If the nonlife industry has combined ratios consistently over 100 percent, that is a sign of risk mispricing and an incentive to invest in riskier assets to try to make insurers profitable overall.

9.55 The FSI combined ratio for nonlife ICs is calculated using the sectoral income and expense statement of domestically incorporated nonlife ICs. The recommended CBDI consolidation basis requires eliminating intra-group flows between resident ICs and their resident and nonresident IC subsidiaries.

9.56 Net claims and underwriting expenses are part of the income and expense statement. Net claims are total claims (line 2.i in Table 5.3) minus claims paid by reinsurance (line 2.ii in Table 5.3), while underwriting expenses are a component of other operating expenses (line 7.ii in Table 5.3). Net premium earned is equal to gross premium earned (line 1.i in Table 5.3) minus premium ceded to reinsurers (line 1.ii in Table 5.3). If claims on reinsurance or premium ceded are not presented in the ICs’ income and expense statement, supplementary information should be requested as a memorandum series.

9.57 This FSI is calculated as a ratio of two flows. To avoid sudden fluctuations from period to period and to foster cross-country comparability, the numerator and denominator should accumulate the flows from the beginning of the year until the reporting period, rather than be calculated only for the reporting period (month or quarter).

Return on assets (life insurance)

9.58 The FSI return on assets (ROA) is intended to measure the efficiency of life ICs in using their stock of assets. It is a common operating ratio used to assess a corporation’s profitability. As for the case of similar indicators for DTs, this indicator may be interpreted in combination with the FSI on return on equity (ROE).

9.59 This FSI is a ratio where the numerator is defined as net income and the denominator is total assets. The preferred definition of net income is net income before taxes (line 10 in Table 5.3), which would produce a more comparable measure of efficiency across economies. The denominator is the balance sheet measure of total assets (line 14 in Table 5.3).

9.60 Source data for net income are the life ICs’ consolidated sectoral income and expense statement, while source data for total assets are their consolidated sectoral balance sheet. To avoid double counting, life ICs’ intragroup positions should be eliminated for data compiled on a CBDI consolidation basis.

9.61 Net income is calculated on an accounting and supervisory basis (see paragraphs 5.110–5.118), with premiums earned (line 1) and investment income (line 8) usually being the main source of income. Premiums earned are presented net of reinsurance ceded. Net income includes (1) gains and losses on revaluation of financial assets and liabilities (line 9); (2) gains and losses from the sales of fixed assets (line 5.iii, measured as the difference between the sale value and the balance sheet value at the previous end period); and (3) net change in technical reserves (line 3). The amount of technical reserves that need to be constituted in any specific period might not only be based on actuarial calculations, but also reflect supervisory guidelines.

9.62 The Guide recommends that investment income not include accrual of interest on nonperforming assets. It also recommends including realized and unrealized gains and losses arising during each period on all financial instruments valued at market or fair value through profit and loss, excluding equity in associates, subsidiaries, and any reverse equity investment.

9.63 Being a ratio of a flow (income) to a stock (assets), this FSI should be calculated in a way that facilitates time series and cross-country comparisons. For the numerator, net income should be annualized. The denominator should be a measure of the average stock of total assets either over the annualization period or, alternatively, from the beginning of the calendar year until the end of the reporting period, using the more frequent possible number of observations.4

Return on equity (life and nonlife insurance)

9.64 The FSI return on equity (ROE) should be separately calculated for life and non-life insurance due to the very different capital structures of life and non-life insurance companies. It is intended to measure the efficiency of ICs in using capital. It also indicates the ability of ICs to internally generate capital through retained earnings, and potentially attract new equity investment. The ratio needs to be interpreted in combination with FSIs on capital adequacy (equity to invested assets), because a high ROE could indicate high profitability but also low capitalization, while a low ratio could be caused by a high level of capitalization.

9.65 This FSI is a ratio where the numerator is defined as net income and the denominator is total capital. For life insurance, net income is the same concept as the numerator of ROA. Equity corresponds to capital and reserves of the sectoral balance sheet of ICs (line 30 in Table 5.3).

9.66 As for the case of ROA (see paragraph 9.60), the source data for this indicator are the consolidated sectoral income and expense statements and the sectoral balance sheets of ICs. ICs’ intragroup positions should be eliminated for data compiled on a CBDI consolidation basis.

9.67 Since the main goal of this indicator is to measure the sustainability of a corporation, the Guide recommends using as numerator net income after taxes (line 12 in Table 5.3). Using two different measures of income for two different indicators enables compilers and analysts to better grasp the effect of income taxes on the final profitability of the enterprise. Also, net income after taxes provides a more cross-country comparable measure of profitability for the investor, which is the driving force for capital flows among economies: low (after tax) profitability may signal fundamental problems for ICs and may be considered as a leading indicator for solvency problems.

9.68 As with the FSI on ROA, net income flows should be annualized. Equity should be calculated averaging stocks over the annualization period or, alternatively, from the beginning of the year until the reporting period, using the most frequent observations available.

Pension Funds

9.69 Pension Funds (PFs) play an important role in the financial system in many countries and have a potential impact on the stability of financial markets in several ways, most significantly through their investment behavior. PFs hold a large amount of financial assets and, therefore, any sizable reallocation of their assets (e.g., between fixed income and equities) could have macrofinancial implications. The Guide recommends compiling two indicators to measure potential risks for PFs.

Liquidity ratio

9.70 The FSI liquidity ratio is intended to assess the adequacy of liquid assets held by PFs. In particular, this FSI gauges PFs’ capacity to meet their financial obligations arising from pension payments over a one-year time horizon.

9.71 This FSI is a ratio where the numerator is defined as PFs’ liquid assets (line 31 in Table 5.4) and the denominator is the estimated pension payments for the next 12 months (line 32 in Table 5.4). Pension payments are based on actuarial calculations.

9.72 Liquid assets are those assets readily available to an entity to meet a demand for cash. For a financial asset to be classified as liquid, the holder must have a reasonable certainty that it can be converted into cash at short notice, in large volumes, without substantially affecting their price.

9.73 For this indicator, liquid assets are defined in paragraph 5.132.

9.74 Whether an instrument is considered liquid or not depends on judgment and is influenced by market conditions. In particular, for securities, liquidity depends on the breadth of secondary markets. Compilation issues of liquid assets for PFs are the same as the ones already discussed for liquidity measures for DTs in Chapter 7 (see paragraph 7.66).

Return on assets

9.75 The FSI return on assets (ROA) is an indication of the yield on investments net of the costs of managing the fund. Since the costs of managing the fund generally are small relative to investment returns, unlike ROA for DTs and ICs, ROA for PFs provides little insight into efficiency. While a higher ROA could signal more efficient management, it could also result from higher yields on higher risk investments. For defined benefit schemes, a sound ROA—one reflecting appropriately balanced risk and return—indicates that the PFs will be able to fulfill future pension obligations. For defined contribution schemes, the obtained ROA will affect the level of future benefits to be paid by the PFs.

9.76 This FSI is a ratio where the numerator is the net income and the denominator is total assets of PFs. To foster cross-country comparability, the Guide recommends using net income before taxes (line 7 in Table 5.4). The denominator is the balance sheet measure of total assets (line 11 in Table 5.4).

9.77 Source data for net income are the resident-based sectoral income and expense statement of PFs, which are not being consolidated. Net income before taxes is calculated as net investment income (investment income from own financial and nonfinancial assets less investment expenses) plus other income less total administrative expenses and plus the net actuarial gains or losses of the period.

9.78 Total assets are sourced from the sectoral balance sheet of PFs, which is compiled using a resident-based approach. Total assets comprise financial and nonfinancial assets.

9.79 Net income is calculated on an accounting and supervisory approach. This is particularly important for long-term assumptions on actuarial gains or losses, which are subject to supervisory approval, including changes in benefits.

9.80 Similar considerations as the ones elaborated for the case of ICs regarding how to annualize net income and average total assets (see paragraph 9.63) apply for the case of PFs: (1) net income should be accumulated from the beginning of the year until the end of the reporting period and then annualized; (2) average total assets should make use of the most frequent available observations.

Annex 9.1: Summary of Financial Soundness Indicators for Other Financial Corporations

1

Intra-MMF subsector positions are aggregated, not consolidated.

2

Regional and national examples of measures of insurance capital adequacy requirements are: (i) the EU-wide Solvency II which has two measures of capital adequacy, the Solvency Capital Ratio (SCR) and Minimum Capital Ratio (MCR); (ii) the United States state-based insurance regulatory framework risk-based capital ratio which is calculated under different methodologies for the life insurance, property and casualty and health insurance industries; and (iii) the Canadian Life Insurance Capital Adequacy Test for life insurance companies, the Minimum Capital Test for property and casualty companies, and the Mortgage Insurance Capital Adequacy Test for mortgage insurance companies.

3

Much of the income of a life insurance company comes from invested assets, so the combined ratio is not a meaningful indicator of profitability in the life sector.

4

Using the same example for data reported at end-May, the numerator should be the average of stock of total assets at the end of December of year 0, and January, February, March, April, and May of year 1.

  • 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