Geopolitical-risk signals (Caldara-Iacoviello GPR)
Monthly geopolitical-risk signals from the Caldara-Iacoviello GPR data export (1985 -> latest): the benchmark GPR index plus the threats and acts subindices, with 1-month and 12-month changes, 30-month annualized change volatility, 3-sigma anomaly flags, drift forecasts, 5-year tension-regime z-scores, high-risk and spike flags, and the threat-minus-act spread. The news-risk lens on geopolitics — the geopolitical companion to the policy lens in epu-signals and the financial lens in finstress-signals. Raw data: Caldara & Iacoviello.
Quality
Attribution
Caldara & Iacoviello, Geopolitical Risk index (derived signals by Frontier Data Hub)
Schema
| Column | Type | Description |
|---|---|---|
| date | string | Reference month (GPR is a monthly index). |
| country | string | World (the GPR index is global). |
| country_code | string | Stable World aggregate code: WLD. |
| series_id | string | GPR series code: GPR (benchmark index), GPRT (threats subindex) or GPRA (acts subindex). |
| series_label | string | Series label as documented on the GPR page. |
| value | float | Geopolitical Risk index value as published: the index is normalized so that its 2000-2009 average equals 100. |
| momentum_3m | float | |
| yoy_change_pct | float | |
| volatility_30d | float | |
| anomaly_flag | integer | |
| forecast_1m | float | |
| rank | integer | |
| gpr_z_5y | float | |
| high_risk_flag | integer | |
| spike_flag | integer | |
| threat_act_spread | float |
Sample rows
| date | country | country_code | series_id | series_label | value | momentum_3m | yoy_change_pct | volatility_30d | anomaly_flag | forecast_1m | rank | gpr_z_5y | high_risk_flag | spike_flag | threat_act_spread |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1985-01-01 | World | WLD | GPR | Geopolitical Risk index (benchmark, monthly) | 102.17337799072266 | — | — | — | 0 | — | — | — | 0 | 0 | 17.926681518554688 |
| 1985-02-01 | World | WLD | GPR | Geopolitical Risk index (benchmark, monthly) | 117.10202026367188 | 14.928642272949219 | — | — | 0 | — | — | — | 0 | 0 | 29.84105682373047 |
| 1985-03-01 | World | WLD | GPR | Geopolitical Risk index (benchmark, monthly) | 124.77815246582031 | 7.6761322021484375 | — | — | 0 | — | — | — | 0 | 0 | 10.083580017089844 |
| 1985-04-01 | World | WLD | GPR | Geopolitical Risk index (benchmark, monthly) | 87.92900085449219 | -36.849151611328125 | — | — | 0 | — | — | — | 0 | 0 | 20.882400512695312 |
| 1985-05-01 | World | WLD | GPR | Geopolitical Risk index (benchmark, monthly) | 103.26284790039062 | 15.333847045898438 | — | — | 0 | — | — | — | 0 | 0 | 18.883056640625 |
Download sample data
Download the full sample snapshot for this dataset (sample rows, not the complete dataset).
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/gpr_signals/gpr_geopolitical_risk_signals" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/gpr_signals/gpr_geopolitical_risk_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/gpr_signals/gpr_geopolitical_risk_signals
Tip: fetch /llms.txt for the full machine-readable catalog.