Brent Crude Oil Prices: Daily (DCOILBRENTEU) – FRED St. Louis Fed

Rows: 10,174
Columns: 2

Daily Europe Brent spot crude oil price in USD per barrel. Covers from May 1987 to present. Key international crude benchmark priced at FOB (Free on Board).

AI analysis

Brent Crude Oil Price Dataset — Analytical Overview

1. Dataset Representation & Value This dataset captures the daily Europe Brent spot crude oil price in USD per barrel, sourced directly from the St. Louis Federal Reserve's FRED economic data platform (https://fred.stlouisfed.org/graph/fredgraph.csv?id=DCOILBRENTEU). The FRED provenance is significant — it signals institutional reliability, consistent methodology, and active maintenance through to the present day, making this dataset well-suited for longitudinal economic research. Spanning from May 1987 to present across roughly 10,174 trading days, it captures multiple complete economic cycles, geopolitical shocks, and supply-demand inflection points. As the premier international crude benchmark priced on a Free on Board basis, Brent is the reference price for approximately two-thirds of global oil trading, giving it outsized relevance for macroeconomic and financial correlation studies.

2. Data Quality Observations Overall data quality is strong but not perfect. The dataset contains 281 null values exclusively in the price column (DCOILBRENTEU), representing approximately 2.8% of all rows — the Date column is completely clean with zero nulls and full distinctness across all 10,174 rows. These missing price values almost certainly correspond to non-trading days (weekends, public holidays, exchange closures), which is entirely expected behavior for a daily spot price series rather than a true data quality failure. However, downstream consumers should handle these gaps explicitly — forward-filling, interpolation, or exclusion — depending on the analytical use case. No type mismatches are flagged, and the duplicate row count is pending Phase D recomputation, though the perfect 1:1 ratio of distinct dates to total rows strongly implies zero duplicate dates, which is an excellent structural integrity signal.

3. Key Column Distributions The DCOILBRENTEU price column tells a rich statistical story. The mean of $51.16/barrel sits notably above the median of $46.09, indicating a right-skewed distribution (skew = 0.563) — consistent with the asymmetric nature of oil price shocks, where upward spikes tend to be sharper and more dramatic than gradual declines. The interquartile range spans $19.54 (Q1) to $74.78 (Q3), a $55+ spread that reflects just how volatile the commodity has been across decades. The minimum of $9.10/barrel likely anchors to the mid-1990s price collapse, while the maximum of $143.95/barrel corresponds to the 2008 pre-financial-crisis peak. The standard deviation of $32.74 — roughly 64% of the mean — underscores extreme dispersion. With 5,493 distinct price values across ~9,893 non-null observations, there is very little price repetition, confirming continuous market movement rather than stale or stepped data.

4. Recommended Join Key Columns The Date column is the unambiguous join key for cross-dataset correlation. Its properties are ideal: zero nulls, 10,174 distinct values matching total row count exactly, and a consistent daily granularity. When joining to other datasets, analysts should account for the ~281 null-price days (non-trading days) and consider whether the partner dataset uses calendar days vs. business days — a mismatch here is the most likely source of join inflation or unexpected nulls. For datasets with lower frequency (monthly, quarterly), date truncation to YYYY-MM or YYYY-Q will be necessary, and an aggregation strategy (e.g., monthly average or end-of-month close) should be defined before joining.

5. Suggested Correlation Datasets This dataset has strong pairing potential across several domains. WTI Crude Oil prices (also available via FRED) would allow spread analysis between the two benchmarks — a classic refinery and logistics signal. Natural gas spot prices (Henry Hub) would test energy commodity co-movement. On the macroeconomic side, USD/EUR exchange rates are natural partners given Brent's USD denomination against a European benchmark. U.S. CPI or PPI data would support inflation transmission studies, while airline or shipping stock indices could reveal how input cost shocks propagate to energy-intensive industries. For geopolitical research, pairing with OPEC production volume data or U.S. crude inventory reports (EIA Weekly) would enable supply-side causal analysis. All of these datasets share the same daily or sub-monthly temporal grain, making the Date key directly actionable.

Columns

Date (date)
DCOILBRENTEU (decimal)