Global sovereign yield signals (10y bond yields, momentum, spreads, anomalies)

Name: Global sovereign yield signals (10y bond yields, momentum, spreads, anomalies)
Creator: Sovereign Yield Signals (derived)
License: https://fred.stlouisfed.org/legal
Keywords: bonds, yields, sovereign-debt, interest-rates, euro-area, spread, volatility, momentum, anomaly-detection, forecasting, signals, fred

Signals derived from the OECD's harmonized 10-year government bond yields (monthly, as published on FRED) for 17 economies: 30-month annualized volatility of monthly yield changes, 3-month momentum, 12-month change, 3-sigma anomaly flags vs a trailing 12-month baseline, naive-drift 1-month forecasts, a per-month cross-country volatility rank, and the euro-area periphery spread — each euro member's 10y yield minus Germany's on the same month (the core-vs-periphery gauge). All rows carry canonical country_code so they join cleanly with country-keyed macro data. Raw series: Federal Reserve Bank of St. Louis (FRED), underlying data OECD Main Economic Indicators.

Source: Sovereign Yield Signals (derived)11,188 rowsUpdated: 9/22/2026

bondsyieldssovereign-debtinterest-rateseuro-areaspreadvolatilitymomentumanomaly-detectionforecastingsignalsfred

Quality

Attribution

Federal Reserve Bank of St. Louis (FRED; OECD; derived signals by Frontier Data Hub)

Schema

Column	Type	Description
date	string	Observation date (FRED API field date; YYYY-MM-DD).
country	string
country_code	string
series_id	string	FRED series ID, e.g. IRLTLT01USM156N; resolves to the series page at https://fred.stlouisfed.org/series/<id>.
series_label	string	Official FRED series title as published for the series (OECD long-term government bond yield, 10-year, monthly).
value	float	10-year government bond yield in percent per annum, as published by FRED/OECD for this series; see the series notes for methodology and revisions.
volatility_30d	float
momentum_3m	float
yoy_change	float
anomaly_flag	integer
forecast_1m	float
rank	integer
spread_vs_de_pp	float

Sample rows

date	country	country_code	series_id	series_label	value	volatility_30d	momentum_3m	yoy_change	forecast_1m	rank	spread_vs_de_pp
1953-04-01	United States	USA	IRLTLT01USM156N	Interest Rates: Long-Term Government Bond Yields: 10-Year: Main (Including Benchmark) for United States	2.83	—	—	—	—	—	—
1953-05-01	United States	USA	IRLTLT01USM156N	Interest Rates: Long-Term Government Bond Yields: 10-Year: Main (Including Benchmark) for United States	3.05	—	—	—	—	—	—
1953-06-01	United States	USA	IRLTLT01USM156N	Interest Rates: Long-Term Government Bond Yields: 10-Year: Main (Including Benchmark) for United States	3.11	—	—	—	—	—	—
1953-07-01	United States	USA	IRLTLT01USM156N	Interest Rates: Long-Term Government Bond Yields: 10-Year: Main (Including Benchmark) for United States	2.93	—	0.10000000000000009	—	—	—	—
1953-08-01	United States	USA	IRLTLT01USM156N	Interest Rates: Long-Term Government Bond Yields: 10-Year: Main (Including Benchmark) for United States	2.95	—	-0.09999999999999964	—	—	—	—

Download sample data

Download the full sample snapshot for this dataset (sample rows, not the complete dataset).

Download sample CSV Download sample JSON

Use with an LLM

Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).

cURL

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

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/yield_signals/global_sovereign_yield_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)

API endpoint: https://datazimuts.com/v1/datasets/yield_signals/global_sovereign_yield_signals

Tip: fetch /llms.txt for the full machine-readable catalog.