Can the VIX explain the Brent Premium?

One persistent, and often misunderstood feature of oil markets is the spread between Brent and WTI crude prices.

At first glance, the explanation seems straightforward:

• Brent is global
• WTI is domestic (USA)
• The spread reflects logistics and infrastructure

 

But what if part of that spread is not just physical ...

but financial?

 

The Brent Premium and Market Risk

 

Using weekly data for over two decades, I computed the Brent premium as:

Brent Premium_t=Brent Price_t−WTI Price_t

I then compared it to the VIX, a widely used proxy for global financial uncertainty. Here is how the relationship looks over the last 5 years:

The Brent premium (blue) fluctuates significantly over time, while the VIX (orange) spikes during periods of stress. See Russia invades Ukraine in February 2022, or trade disruptions around liberation day, April 2025.

 

What stands out is not a perfect co-movement—but episodes of alignment, particularly during periods of elevated uncertainty.

 

Time Varying Correlations

To move beyond visual inspection, I computed rolling correlations:

1-year (52-week) rolling correlation

5-year (260-week) rolling correlation

 

 

The results are not surprising but revealing:

The short-run (1-year) correlation is highly unstable, ranging from strongly negative to strongly positive

The long-run (5-year) correlation shows a structural shift: Negative in earlier periods. Gradually turning positive in recent years.

 

What is the mechanism?

Why would the VIX help explain an oil price spread?

We could think of the Brent–WTI spread as: “The price of connecting U.S. oil to the global market.” It takes now $10 per barrel to do so, a few weeks ago it was $5

 

Add financial stress:

1. Global risk raises Brent more than WTI, Brent reflects seaborne oil exposed to geopolitical risk. Brent embeds a global risk premium

2. WTI is more insulated. Landlocked (Cushing, Oklahoma), tied to U.S. supply-demand conditions, less sensitive to global shocks

3. Financial conditions affect arbitrage, when the VIX rises: funding costs (for trading) increase. Arbitrage capital withdraws or demands higher returns.

4. Infrastructure needs finance. Even if pipelines exist, someone needs to: finance storage, charter tankers, take price risk. In high-VIX environments, this becomes more expensive.

Regression Evidence

Beyond correlations, I ran regressions of the form:

Brent Premium_{_t}=α+β x VIX_{_t}+ε_{_t}

Full sample (2000–2026)

β = −0.095 à t-stat = −4.99

R² ≈ 1.8% - - - F - test ≈24.9

Interpretation: A 10-point increase in the VIX is associated with roughly a $0.95 decrease in the Brent premium.

 

Last 5 years (2021–2026)

β = +0.089 à t-stat = 3.76

R² ≈ 5.1%  - - -  F-test ≈ 14.1

Interpretation: A 10-point increase in the VIX is associated with roughly a $0.89 increase in the Brent premium.

The relationship between the VIX and the Brent premium is not stable—it flips sign across regimes.

 

Economic Interpretation

Long sample (2000–2020s): negative relationship. Historically: High VIX meant global stress which lowered demand expectations. Oil prices fell globally, Brent (global benchmark) fell more than WTI. Global consumption fell more than US consumption.  Demand side explanation.

Recent period (post-2020): positive relationship. In the last few years: High VIX means Geopolitical risk, such supply disruptions (Russia, Middle East). Since Brent embeds a global risk premium, Brent rises more than WTI. Supply side explanation.

 

When volatility reflects demand destruction, the Brent premium compresses.
When it reflects geopolitical risk, the Brent premium expands.

If the U.S. produces more oil than it exports, why do prices still rise here?

At first glance, it seems puzzling. The United States is now one of the largest oil producers in the world and in many periods produces more petroleum than it consumes. If that is the case, students naturally ask: shouldn't domestic prices be insulated from global shocks?

 

The short answer is no, because oil markets are fundamentally global.

 

Crude oil is a globally traded commodity. Even if the United States produces a large volume of oil, the price that producers receive—and refiners pay—is determined largely in the global benchmark markets, particularly Brent and West Texas Intermediate (WTI). If a supply disruption occurs in the Middle East, or if global demand increases in Asia or Europe, prices tend to adjust worldwide. American producers can export oil, and American refiners can import oil, so domestic prices remain tightly linked to international markets. In economic terms, oil is part of an integrated global market, not a closed domestic one.

 

Another factor students often overlook is the role of the U.S. Strategic Petroleum Reserve (SPR). The SPR is the world's largest emergency stockpile of crude oil, created after the 1970s oil embargo to help cushion supply disruptions. The reserve stores crude oil in large underground salt caverns along the Gulf Coast of Texas and Louisiana, where the geology allows enormous quantities of oil to be stored safely and relatively cheaply.

 

According to the Department of Energy, these caverns were created by dissolving salt formations deep underground and can hold hundreds of millions of barrels of crude oil. The entire system has an authorized capacity of about 714 million barrels and currently holds a little over 400 million barrels, meaning it is well below full capacity.

 

The SPR acts as a strategic buffer, not a daily supply source. Oil stored there cannot immediately offset large market movements because it can only be withdrawn at a limited rate and is intended primarily for major disruptions such as wars, natural disasters, or severe supply shocks. Even when releases occur, they mainly help stabilize markets temporarily rather than permanently change the underlying global supply-demand balance.

This also illustrates an important limitation of many risk models used in finance. Traditional Value-at-Risk (VaR) approaches often assume that price movements follow relatively stable statistical distributions. But commodity markets—especially oil—are heavily influenced by global geopolitical events, policy decisions, and sudden supply disruptions. When an unexpected event shifts expectations about global supply or demand, prices can move far more dramatically than a normal distribution would predict. In statistical terms, these markets exhibit fat tails, meaning extreme price changes occur more frequently than simple models assume. The global integration of oil markets, combined with the possibility of large supply shocks, is one reason risk managers often complement traditional VaR models with alternative approaches that better account for tail risk.

 

Markets that depend on geopolitics rarely behave like the tidy bell curves we put in our spreadsheets.

On the limits of VaR

WTI has risen 38.94% in one month. (2/6/2026 - 3/6/2026) 

To put that in perspective, I looked at monthly WTI returns from 1980–2026 and calculated: 6

• Average monthly return: 0.63% 

• Standard deviation: 10.04% 

• Z-score for a 38.94% move, 3.82 

Under a normal distribution, a move of 38.94% in one month is a 99.9932th percentile event. That sounds dramatic, and it is. In plain English, if monthly oil returns were perfectly normal, a move like this would be expected only about 1 time in 14,707 months — roughly 1,226 years. 

VaR gives us a disciplined framework for thinking about risk. But it also has limitations: 

 • It assumes returns behave “normally”. 

 • It underestimates the likelihood of extreme moves. 

 • It is built from the past, while markets enter new regimes (new normal) very quickly. 

 • It tells us a lot about what is likely in “regular” times, but nothing about what happens when the world stops behaving normally. 

 So the lesson is not that VaR is useless. The lesson is that models are tools, not truth. When a price move looks like an “1,226-year event,” the correct reaction is not blind confidence in the calculation. The correct reaction is to ask: Are markets really normal? Are extreme events more common than the model assumes? What risks live in the tails that our spreadsheet may not fully capture? 

That is where good financial modeling begins: not with worshiping the output, but with questioning the assumptions. 

I leave you with a dad joke. According to the model, this was a once-in-800-years event. According to markets, it just happened.