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Credit-to-GDP gap signals (BIS macroprudential credit cycle)

Quarterly macroprudential signals derived from BIS credit-to-GDP gaps: the deviation of private-sector credit from its long-run trend for ~44 economies — the Basel III countercyclical-buffer guide — alongside the underlying credit-to-GDP ratio. Each series carries quarter-on-quarter and year-on-year changes, 30-quarter annualized change volatility, 1-quarter momentum, 3-sigma anomaly flags vs a trailing 12-quarter baseline, naive-drift 1-quarter forecasts, a per-quarter cross-country volatility rank, plus Basel guide flags (gap > 2pp, gap > 10pp) and 10-year z-scores. Country codes are normalized to ISO alpha-3 so rows join cleanly with other country-keyed datasets. Raw series: Bank for International Settlements (WS_CREDIT_GAP).

Source: Credit-to-GDP Gap Signals (derived)17,000 lignesMis à jour: 22/09/2026
creditcredit-cyclemacroprudentialfinancial-stabilitybasel-iiiccybbismomentumvolatilityanomaly-detectionforecastingsignals

Qualité

92.2

Attribution

Bank for International Settlements (derived signals by Frontier Data Hub)

Schéma

ColonneTypeDescription
datestringFirst day of the reference quarter (BIS SDMX TIME_PERIOD, e.g. 2026-Q1 -> 2026-01-01).
countrystring
country_codestring
series_idstringBIS BORROWERS_CTY code plus the gap-data-type suffix: .GAP for the credit-to-GDP gap, .RATIO for the credit-to-GDP ratio (official CL_AREA and CL_CREDT_GAP_DTYPE codelists).
series_labelstringBIS CL_AREA English name plus the official CL_CREDT_GAP_DTYPE label: 'Credit-to-GDP gap (actual minus HP-filter trend)' or 'Credit-to-GDP ratio (actual data)', for the private non-financial sector.
valuefloatCredit-to-GDP gap or ratio in percentage points, as published in the BIS WS_CREDIT_GAP dataflow (private non-financial sector, all lenders).
qoq_change_ppfloat
yoy_change_ppfloat
volatility_30dfloat
momentum_3mfloat
anomaly_flaginteger
forecast_1mfloat
rankinteger
ccyb_guide_flagfloat
high_gap_flagfloat
gap_z_10yfloat
ratio_z_10yfloat

Exemple de lignes

datecountrycountry_codeseries_idseries_labelvalueqoq_change_ppyoy_change_ppvolatility_30dmomentum_3manomaly_flagforecast_1mrankccyb_guide_flaghigh_gap_flaggap_z_10yratio_z_10y
1947-10-01United StatesUSAUS.RATIOUnited States — Credit-to-GDP ratio (actual data)47.0601902854280
1948-01-01United StatesUSAUS.RATIOUnited States — Credit-to-GDP ratio (actual data)47.5735574787390.5133671933109980.5133671933109980
1948-04-01United StatesUSAUS.RATIOUnited States — Credit-to-GDP ratio (actual data)47.876674657870.30311717913100010.30311717913100010
1948-07-01United StatesUSAUS.RATIOUnited States — Credit-to-GDP ratio (actual data)48.0467899979510.170115340081004260.170115340081004260
1948-10-01United StatesUSAUS.RATIOUnited States — Credit-to-GDP ratio (actual data)48.598597322160.55180732420900111.53840703673200350.55180732420900110

Télécharger un échantillon

Téléchargez l'échantillon complet de ce jeu de données (lignes d'exemple, pas le jeu complet).

Utiliser avec un LLM

Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (JSON-LD + Croissant).

cURL

curl "https://datazimuts.com/v1/datasets/bis_gap_signals/credit_to_gdp_gap_signals" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/bis_gap_signals/credit_to_gdp_gap_signals").json()
print(ds["title"], ds["rows"], "rows")

# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
    print(row)

Point d’accès API: https://datazimuts.com/v1/datasets/bis_gap_signals/credit_to_gdp_gap_signals

Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.