FRED – Henry Hub Natural Gas Spot Price

Rows: 7,660
Columns: 2

Daily Henry Hub natural gas spot price ($/MMBtu) from 1997 to present. Primary US natural gas benchmark.

AI analysis

FRED Henry Hub Natural Gas Spot Price – Dataset Analysis

1. Dataset Overview & Value for Correlation Studies This dataset captures the daily Henry Hub Natural Gas Spot Price ($/MMBtu) from 1997 to the present, sourced directly from the Federal Reserve Bank of St. Louis's FRED platform via its public API (https://fred.stlouisfed.org/graph/fredgraph.csv?id=DHHNGSP). As the primary US natural gas benchmark, Henry Hub prices are a foundational signal in energy markets, influencing everything from utility rates to industrial input costs. The FRED provenance ensures institutional reliability, consistent methodology, and ongoing updates, making this dataset highly suitable for time-series correlation studies across energy, macroeconomic, and climate domains. With 7,660 daily rows spanning roughly 27 years, there is sufficient temporal depth to capture multiple commodity cycles, geopolitical shocks, and seasonal patterns.

2. Data Quality Observations Overall data quality is good but not pristine. The 287 null values in the DHHNGSP price column represent approximately 3.7% of all rows — this is the only column carrying nulls, and it accounts for all 287 null cells across the entire dataset. These gaps almost certainly correspond to non-trading days (weekends, federal holidays), which is expected behavior for a spot price series rather than a true data quality failure. No duplicate rows are flagged as a concern in the current metadata, and the Date column shows 7,660 distinct values matching the total row count exactly, confirming zero duplicate dates and a clean index. The absence of type mismatches and the clean date column make this a relatively low-maintenance dataset before analysis.

3. Key Column Distributions & Statistical Highlights The DHHNGSP price column tells a rich story through its statistics. The mean of $4.08/MMBtu versus a median of $3.36/MMBtu reveals a notable right skew (skewness = 2), meaning a relatively small number of extreme price spikes pull the average well above the typical trading range. The interquartile range of $2.61 (Q1) to $5.03 (Q3) captures the "normal market" corridor, while the maximum of $30.72/MMBtu — nearly 10× the median — reflects catastrophic supply shocks such as the 2021 Texas winter freeze or the post-Ukraine 2022 European demand surge. The 284 flagged outliers (roughly 3.8% of non-null rows) are consistent with this dynamic: natural gas is well-known for asymmetric, fat-tailed price behavior driven by weather events and infrastructure constraints. The standard deviation of $2.18 against a mean of $4.08 further underscores high relative volatility compared to more stable commodities.

4. Recommended Join Key Columns The Date column is the clear and unambiguous join key — it has zero nulls, 7,660 distinct values (perfect 1:1 uniqueness), and follows a standard date format suitable for direct merging with any other time-series dataset. For correlation studies, joining on Date will work cleanly for daily-frequency datasets. For lower-frequency datasets (monthly, quarterly), the Date column can be easily aggregated via month or quarter truncation before joining. One practical note: because ~287 rows are weekend/holiday nulls, analysts should forward-fill or explicitly handle gaps before merging to avoid unintended null propagation in joined datasets.

5. Suggested Paired Datasets for Correlation Discovery Several dataset categories would pair powerfully with Henry Hub prices. Other energy commodities — WTI or Brent crude oil spot prices (also available on FRED), coal prices, and electricity spot prices (EIA data) — would allow exploration of cross-commodity energy price linkages and substitution effects. Macroeconomic indicators such as the US CPI (particularly energy sub-components), industrial production indices, and GDP growth rates could reveal how gas price shocks transmit into broader inflation. Weather and climate data — specifically US heating degree days (HDD) and cooling degree days (CDD) from NOAA — would be a natural fit given seasonality's outsized role in gas demand. Natural gas storage and production data from the EIA (weekly storage reports) would allow supply-side correlation analysis. Finally, utility and energy sector equity indices (e.g., XLU ETF price history) could reveal how spot price volatility feeds into equity market performance for energy-dependent sectors.

Columns

Date (date)
DHHNGSP (decimal)