272 Resources

Open Dataset

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Credit to the non-financial sector

Credit to the non-financial sector

carol.reyes.dulong
Open Dataset
Creators: BIS statistics
Publication Date: 2025-03-11
Creators: BIS statistics

The data set on credit to the non-financial sector captures borrowing activity by the government sector and the private non-financial sector in more than 40 economies.

Quarterly data on credit to the government sector cover on average 20 years, while those on credit to the private non-financial sector on average more than 45 years. The statistics follow the framework of the System of National Accounts.

On the lending side, two credit data series are provided. On the one hand, total credit comprises financing from all sources, including domestic banks, other domestic financial corporations, non-financial corporations and non-residents. On the other, bank credit includes credit extended by domestic banks to the private non-financial sector.

On the borrowing side, total credit to the non-financial sector is broken down into credit to the government sector and the private non-financial sector, and the latter is further split between non-financial corporations and households (including non-profit institutions serving households).

The financial instruments covered comprise currency and deposits (which are mostly zero in the case of credit to the private non-financial sector), loans and debt securities. The sum of these three instruments is defined here as “core debt”. For the government sector, core debt generally represents the bulk of total debt.

The statistics follow the framework of the System of National Accounts 2008, which stipulates that outstanding credit instruments should be valued at market values. For credit to the government, data are also provided in nominal (face) values, since these can be useful in some forms of debt sustainability analysis.

Credit-to-GDP gaps

Credit-to-GDP gaps

carol.reyes.dulong
Open Dataset
Creators: BIS statistics
Publication Date: 2025-03-11
Creators: BIS statistics

The credit-to-GDP gap data set aims at quantifying the notion of “excessive credit” in a simple way. It serves as an early warning indicator for potential banking crises or severe distress.

The data set covers 44 economies, starting at earliest in 1961 and captures total borrowing from all domestic and foreign sources. The credit-to-GDP gap is defined as the difference between the credit-to-GDP ratio and its long-run trend. The trend is derived using a one-sided (ie backward-looking) Hodrick-Prescott filter.

To facilitate comparability across countries, the credit-to-GDP ratio, as published in the BIS database of total credit to the private non-financial sector, is used as input data. However, it also means that the credit-to-GDP gaps published by the BIS may differ from those used by national authorities as part of their countercyclical capital buffer decisions.

The gap indicator was adopted as a common reference point under Basel III to guide the build-up of countercyclical capital buffers. Authorities are expected, however, to apply judgment in the setting of the buffer in their jurisdiction after using the best information available to gauge the build-up of system-wide risk rather than relying mechanistically on the credit-to-GDP guide. For instance, national authorities may form their policy decisions using credit-to-GDP ratios that are based on data series that differ from the BIS series, leading to credit-to-GDP gaps that differ from those published by the BIS.

Debt service ratios

Debt service ratios

carol.reyes.dulong
Open Dataset
Creators: BIS statistics
Publication Date: 2025-03-11
Creators: BIS statistics

The data set for debt service ratios reflects the share of income used to service debt for households, non-financial corporations and the total private non-financial sector. It provides important information about interactions between the financial and real sectors, and is a reliable early warning indicator for systemic banking crises.

The published series for the total private non-financial sector cover 32 economies, starting at earliest in 1999. The data set also includes a breakdown for households and non-financial corporations, estimated for 17 economies.

To derive the DSRs on an internationally consistent basis, the BIS applies a unified methodological approach and uses, where available, input data compiled on an internationally consistent basis (total stock of debt, income available for debt service payments, average interest rate on the existing stock of debt and the average remaining maturity). Although the applied methodology is subject to an approximation error when aggregate data are used, it correctly captures how the DSR in a particular country changes over time. However, it may not accurately measure the DSR level compared with the result that may be obtained from micro data.

For practical purposes, it is more meaningful to compare national DSRs over time (eg by removing country-specific means) rather than to compare their absolute levels, which are difficult to pinpoint. This approach also takes account of different institutional and behavioural factors affecting average remaining maturities.

Global liquidity indicators

Global liquidity indicators

carol.reyes.dulong
Open Dataset
Creators: BIS statistics
Publication Date: 2025-03-11
Creators: BIS statistics

The BIS uses the term “global liquidity” to refer to the ease of financing in global financial markets. The BIS global liquidity indicators (GLIs) track credit to non-bank borrowers, covering both loans extended by banks and funding from global bond markets through the issuance of international debt securities (IDS). The main focus is on foreign currency credit denominated in three major reserve currencies (US dollars, euros and Japanese yen) to non-residents, ie borrowers outside the respective currency areas.

Exchange-traded derivatives statistics

Exchange-traded derivatives statistics

carol.reyes.dulong
Open Dataset
Creators: BIS statistics
Publication Date: 2025-03-11
Creators: BIS statistics

The exchange-traded derivatives (XTD) statistics cover the turnover and open interest of foreign exchange and interest rate futures and options. The statistics are compiled from commercial data sources and cover contracts traded on over 50 organised exchanges.

OTC derivatives statistics

OTC derivatives statistics

carol.reyes.dulong
Open Dataset
Creators: BIS statistics
Publication Date: 2024-11-21
Creators: BIS statistics

The over-the-counter (OTC) derivatives statistics capture the outstanding positions of derivatives dealers, mainly banks. They cover the outstanding notional value, market value and credit exposure of OTC foreign exchange, interest rate, equity, commodity and credit derivatives, as well as Herfindahl concentration measures. Dealers report on a worldwide consolidated basis, including the positions of their foreign affiliates and excluding intragroup positions. The statistics are collected under the auspices of the Committee on the Global Financial System and reported to the BIS at a country, rather than individual dealer, level. The statistics comprise data reported every six months by dealers in 12 jurisdictions (Australia, Canada, France, Germany, Italy, Japan, the Netherlands, Spain, Sweden, Switzerland, the United Kingdom and the United States) plus data reported every three years by dealers in more than 30 additional jurisdictions. For periods between Triennial Surveys, the outstanding positions of dealers in these additional jurisdictions are estimated by the BIS.

Triennial Survey

Triennial Survey

carol.reyes.dulong
Open Dataset
Creators: BIS statistics
Publication Date: 2022-12-05
Creators: BIS statistics

The Triennial Central Bank Survey of foreign exchange and over-the-counter (OTC) derivatives markets aims to obtain comprehensive and consistent information on the size and structure of global foreign exchange and OTC derivatives markets. The results are intended to increase the transparency of OTC markets and to help central banks, other authorities and market participants monitor developments in global financial markets. They also help to inform discussions on reforms to OTC markets. The Triennial Survey is coordinated by the BIS under the auspices of the Markets Committee and the Committee on the Global Financial System. It is supported through the Data Gaps Initiative endorsed by the G20.

Residential property prices

Residential property prices

carol.reyes.dulong
Open Dataset
Creators: BIS statistics
Publication Date: 2025-03-27
Creators: BIS statistics

The residential property price statistics comprise two data sets: the detailed and the selected.

The detailed residential property prices (RPP) data set contains regional and property type breakdowns for around 60 economies. The selected residential property prices (SPP) data set is designed to enhance cross-country comparability, and hence shows for each jurisdiction a single indicator that is closest to nationwide coverage. In most cases, this selected series covers all types of new and existing dwelling.

Selected residential property prices

The selected residential property prices data set shows the most representative property price indicator for each country. For most economies, this series refers to the whole country and covers the entire market: all property types, both new and existing vintages. The selected residential property prices data set is derived from the detailed data set. The BIS publishes four measures for each indicator: price indices and year-on-year growth rates, in nominal and real (CPI-deflated) terms. An analysis based on these selected indicators is released quarterly. The data set covers around 60 countries. For 23 countries, data are backdated with historical series to around 1970. In several countries, the compilation of the residential property price indicator follows the recommendations of the Handbook on residential property prices, an internationally agreed framework for classifying property price issues.

Detailed residential property prices

The detailed residential property prices data set consists of more than 300 series from around 60 countries collected from national central banks. These data series differ significantly from country to country, varying in frequency, type of property and vintage, covered area, priced unit, compilation method or seasonal adjustment. The specificities of each country’s residential property markets and the absence of binding international standards for property price statistics could explain this variability. Beyond national-level indicators, for most countries the data set contains some subnational data. Subnational data in most cases show price developments in the capital or biggest city.

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