BIS debt-service stress signals (debt-burden z-scores, stress flags)
Quarterly financial-stress signals derived from BIS debt service ratios (keyless SDMX, WS_DSR, ~30 economies, 1999 ->): the DSR (interest + amortisation over income, %) for households, non-financial corporations and the private non-financial sector, with quarter-on-quarter and year-on-year changes, 30-quarter change volatility, 3-sigma anomaly flags vs a trailing-12-quarter baseline, drift forecasts, per-quarter cross-country volatility ranks, 10-year DSR z-scores, high-stress and rising-burden flags, and the household-minus-corporate sectoral spread. The debt-burden-stress companion to the credit-cycle gap signals. Country codes normalized to ISO alpha-3. Raw data: Bank for International Settlements.
Qualité
Attribution
Bank for International Settlements (derived signals by Frontier Data Hub)
Schéma
| Colonne | Type | Description |
|---|---|---|
| date | string | First day of the reference quarter (BIS SDMX TIME_PERIOD, e.g. 2026-Q1 -> 2026-01-01). |
| country | string | Economy short name (BIS CL_AREA label, normalized via the shared layer). |
| country_code | string | ISO 3166-1 alpha-3 code mapped from the BIS REF_AREA code (e.g. US -> USA, HK -> HKG). |
| series_id | string | BIS REF_AREA code plus the borrower suffix: .H for households & NPISHs, .N for non-financial corporations, .P for the private non-financial sector (official CL_AREA and DSR_BORROWERS codelists). |
| series_label | string | Borrower-sector label plus the BIS CL_AREA economy name (e.g. 'Households & NPISHs — United States'). |
| value | float | Debt service ratio in percent — interest payments plus amortisations relative to income — as published in the BIS WS_DSR dataflow. |
| qoq_change_pp | float | |
| yoy_change_pp | float | |
| volatility_30d | float | |
| momentum_3m | float | |
| anomaly_flag | integer | |
| forecast_1m | float | |
| rank | integer | |
| dsr_z_10y | float | |
| high_stress_flag | integer | |
| rising_flag | integer | |
| household_corporate_spread | float |
Exemple de lignes
| date | country | country_code | series_id | series_label | value | qoq_change_pp | yoy_change_pp | volatility_30d | momentum_3m | anomaly_flag | forecast_1m | rank | dsr_z_10y | high_stress_flag | rising_flag | household_corporate_spread |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1999-01-01 | Australia | AUS | AU.H | Households & NPISHs — Australia | 10 | — | — | — | — | 0 | — | — | — | 0 | 0 | -34.3 |
| 1999-04-01 | Australia | AUS | AU.H | Households & NPISHs — Australia | 10.1 | 0.09999999999999964 | — | — | 0.09999999999999964 | 0 | — | — | — | 0 | 1 | -33.4 |
| 1999-07-01 | Australia | AUS | AU.H | Households & NPISHs — Australia | 10.3 | 0.20000000000000107 | — | — | 0.20000000000000107 | 0 | — | — | — | 0 | 1 | -34 |
| 1999-10-01 | Australia | AUS | AU.H | Households & NPISHs — Australia | 10.5 | 0.1999999999999993 | — | — | 0.1999999999999993 | 0 | — | — | — | 0 | 1 | -34.8 |
| 2000-01-01 | Australia | AUS | AU.H | Households & NPISHs — Australia | 10.8 | 0.3000000000000007 | 0.8000000000000007 | — | 0.3000000000000007 | 0 | — | — | — | 0 | 1 | -35.599999999999994 |
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/dsr_stress_signals/bis_debt_service_stress_signals" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/dsr_stress_signals/bis_debt_service_stress_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/dsr_stress_signals/bis_debt_service_stress_signals
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.