What drives volatility in the crude oil market: an analysis of Reuters news from 2021–2026
I compiled and analyzed 20,000 news articles related to the oil market published on Reuters between 2021 and 2026. In this article, you will learn what drives oil prices, how markets react to unexpected events, and what this reveals about the nature of the modern crude oil market.
Source data
Approach to building the dataset:
- All news articles were sourced from Reuters.
- The data was collected over the period from November 1, 2021 to April 30, 2026.
- The search queries used included: oil prices, oil volatility, Brent, and WTI.
- Only articles in which oil and petroleum products were the main focus were retained; all other news was excluded.
- The initial dataset contained 20,449 articles. After filtering, the final number was reduced to 12,141 articles.
- The source data can be downloaded in JSON format at the end of the article.
Key drivers of the crude oil market
The main factors influencing oil markets and actively discussed in the news can be grouped into sixteen broad categories:
- Global demand
- Demand in Asia (particularly China and India)
- Demand in the United States
- Demand in Europe
- OPEC/OPEC+ supply
- Supply from the United States, Canada, and other non-cartel countries
- COVID-19 and other viruses
- Sanctions
- Military conflicts
- Military rhetoric (statements without actual action)
- General macroeconomic conditions
- Inflation expectations
- Refining and petroleum products
- Oil inventories
- Supply chain disruptions
- Supply chain recoveries
If we look at the number of days on which each driver is mentioned, the most frequently discussed topics are OPEC, demand in Asia, and oil inventories.
If we measure by the number of news articles, without removing repeated intra-day driver mentions, the most frequently covered topics are military conflicts, OPEC, and supply chain disruptions (partly driven by military conflicts). Events of this type occur less frequently than ordinary market developments, but when they do happen, they attract significant media attention.
Since most drivers are interconnected, there is no single news narrative that clearly dominates at any point in time. Throughout the entire time series, a combination of multiple drivers is always present.
At the same time, any events occurring in the crude oil market ultimately reduce to the balance between supply and demand. Drivers related to oil supply, inventories, and demand make up the bulk of the news background.
Impact of drivers on volatility levels
As a benchmark for oil prices, I used FOB Oil Prices data published by the
U.S. Energy Information Administration. This is a calculated index of spot oil deliveries that is not traded on an exchange, but is suitable for analytical purposes.
An overview of oil volatility
Below are three charts illustrating oil price volatility over the study period:
- Brent crude spot price
- Distribution of daily returns
- Distribution of weekly returns
A typical situation for commodity markets:
- A bell-shaped return distribution with fat tails (for convenience, small values in the right and left tails are grouped into a single bar on the right and left, respectively).
- Weekly volatility is roughly twice as high as daily volatility.
- The charts also show that the right tails are noticeably heavier than the left ones. However, this is likely due to the selected time period, which does not include sharp oil price declines in 2008, 2014, and 2020.
Clustering of drivers by their impact on volatility
Methodology:
- Volatility is defined as the centered standard deviation of returns over a 20-trading-day window (approximately one calendar month).
- Drivers are grouped by day, and duplicate driver mentions within the same day are removed.
- The resulting data is summarized using box plots and clustered to improve interpretability and visual clarity.
Red cluster
The most frequently discussed topics (demand in Asia, OPEC actions, and oil supply from other countries) had the smallest impact on market dynamics.
Blue cluster
Sanctions, refining activity, and regional oil demand were associated with periods of moderate volatility.
Yellow cluster
Military conflicts, the pandemic, supply chain disruptions, and inflation led to the highest levels of market volatility.
General pattern
The rarer an event is, the greater its impact on volatility tends to be.
Limitation of simple grouping
A one-off event may cause a sharp spike in volatility but never occur again. In a long-term perspective, it is more useful to track drivers that have a systemic impact on the market, even if individual events do not always lead to extreme price movements.
Integrated impact index
Formula: Impact = Mean × Count
Description: A high average effect is important, but a driver that appears 2,000 times has a stronger overall influence on the system than a driver with the same effect that appears only 100 times. By multiplying the mean effect by the number of observations, we obtain the systemic contribution of each driver to volatility.
| driver | mean | count | impact |
|---|---|---|---|
| opec | 0.023 | 602 | 13.617 |
| asia_demand | 0.024 | 552 | 13.191 |
| sanctions | 0.026 | 513 | 13.112 |
| inventories | 0.024 | 524 | 12.801 |
| supply_disruptions | 0.027 | 439 | 11.967 |
| non_opec_supply | 0.023 | 496 | 11.462 |
| military_conflict | 0.029 | 304 | 8.908 |
| global_demand | 0.023 | 305 | 6.94 |
| military_rhetoric | 0.029 | 235 | 6.706 |
| inflation | 0.031 | 201 | 6.285 |
| macro_economics | 0.025 | 234 | 5.833 |
| refining | 0.025 | 188 | 4.687 |
| us_demand | 0.021 | 167 | 3.481 |
| supply_recovery | 0.026 | 82 | 2.16 |
| europe_demand | 0.028 | 67 | 1.843 |
| pandemic | 0.03 | 60 | 1.812 |
Conclusion: The bulk of market movements over the examined period is driven by OPEC policy, demand for petroleum products in Asia, discussions around inventories, and sanctions. Military events remain an important but not dominant source of overall impact.
Stability coefficient
Formula: Stability = Mean / Standard deviation
Description: Volatility captures the absolute dispersion of values, but it is difficult to compare across drivers with different average effects. The stability coefficient addresses this issue by expressing dispersion relative to the mean, making it possible to compare variability in a normalized way.
| driver | mean | std | stability |
|---|---|---|---|
| global_demand | 0.023 | 0.009 | 2.457 |
| pandemic | 0.03 | 0.012 | 2.434 |
| us_demand | 0.021 | 0.009 | 2.338 |
| opec | 0.023 | 0.01 | 2.221 |
| macro_economics | 0.025 | 0.011 | 2.168 |
| europe_demand | 0.028 | 0.013 | 2.064 |
| non_opec_supply | 0.023 | 0.011 | 2.062 |
| sanctions | 0.026 | 0.013 | 2.04 |
| asia_demand | 0.024 | 0.012 | 2.005 |
| inventories | 0.024 | 0.013 | 1.95 |
| refining | 0.025 | 0.013 | 1.925 |
| inflation | 0.031 | 0.016 | 1.897 |
| military_rhetoric | 0.029 | 0.015 | 1.876 |
| supply_disruptions | 0.027 | 0.015 | 1.828 |
| military_conflict | 0.029 | 0.016 | 1.788 |
| supply_recovery | 0.026 | 0.015 | 1.733 |
Conclusion: Geopolitical factors and shocks related to supply chain disruptions are characterized by higher uncertainty. Fundamental drivers exhibit more stable behavior. An interesting and somewhat unexpected finding is that the COVID-19 pandemic ranks at the top of the table.
Interquartile range (IQR)
Formula: IQR = Q3 − Q1
Description: The interquartile range focuses on the central 50% of the data and shows the range within which the bulk of observations lie. The wider this range, the less stable the driver is and the more its typical values tend to vary.
| driver | q1 | q3 | iqr |
|---|---|---|---|
| military_conflict | 0.017 | 0.036 | 0.019 |
| inflation | 0.019 | 0.037 | 0.018 |
| military_rhetoric | 0.017 | 0.034 | 0.016 |
| pandemic | 0.02 | 0.035 | 0.015 |
| supply_disruptions | 0.017 | 0.032 | 0.015 |
| supply_recovery | 0.017 | 0.029 | 0.013 |
| europe_demand | 0.018 | 0.031 | 0.013 |
| inventories | 0.016 | 0.028 | 0.012 |
| sanctions | 0.017 | 0.029 | 0.012 |
| refining | 0.017 | 0.029 | 0.012 |
| asia_demand | 0.016 | 0.027 | 0.01 |
| global_demand | 0.017 | 0.027 | 0.01 |
| non_opec_supply | 0.016 | 0.027 | 0.01 |
| opec | 0.016 | 0.026 | 0.01 |
| macro_economics | 0.018 | 0.027 | 0.009 |
| us_demand | 0.015 | 0.024 | 0.009 |
Conclusion: Even after excluding outliers, military conflicts and inflation remain the most heterogeneous drivers. Some events have almost no impact on the market, while others can sharply shift market participants’ expectations.
Impact of drivers on price dynamics
Below is a set of charts, each showing the average market reaction over the first twenty days after a news release, along with a confidence interval expressed as the interquartile range.
Only those drivers that form a persistent trend have been retained. In practice, these correspond to the yellow cluster:
- military conflicts and military rhetoric
- supply chain disruptions and recoveries
- inflation
All other drivers either did not lead to any discernible trend or produced only a weak pattern.
Conclusions
- Since 2021, sixteen key drivers have been influencing the oil market.
- The largest contribution to short-term volatility comes from extraordinary events such as pandemics, military conflicts, and supply chain disruptions.
- A more gradual but systematic contribution to volatility is driven by OPEC policy and demand in Asia, particularly China as the largest oil importer.
- In the case of military conflicts, supply chain disruptions, and inflation, there is a clearly observable post-news trend. This represents a promising area for further research.