How risky is a tanker full of oil? A VaR example

 

A recent news headline mentioned that several oil tankers were being allowed to pass through the Strait of Hormuz.

That got me thinking—not about geopolitics—but about something I care deeply about: financial risk.

 A tanker may be “just one shipment,” but financially it is a large floating position in oil.

 

A typical VLCC (Very Large Crude Carrier) carries about 2 million barrels. At today’s price of Brent crude (March 27, 2026) of $107.9 per barrel, that puts the cargo value at roughly: $216 million.

 

From shipment to risk position

Using weekly Brent prices over the last 20 years I found that:

·       Average weekly return: 0.1839%

·       Weekly volatility: 5.2495%

This means the standard deviation of the tanker’s value is about $11 million per week.

So even before doing anything fancy, we already know: A “normal” week can move the value of this cargo by about $11 million in either direction.

 

VaR under the Normal Distribution

If we assume oil returns are normally distributed, the 99% VaR uses a Z-score of 2.3263.

 

 

In other words, a one-week loss of roughly $26 million or more should happen for only 1% of the time (1 out of every 100 weeks)

 

VaR under a Student-t Distribution

But commodity returns are often not well described by the normal distribution. They tend to have fat tails. If instead we use a Student-t distribution with 5 degrees of freedom, the one-week t-critical value (the equivalent to the normal Z-score) is 3.3649, VaR_99% becomes:

 

• Same tanker.
• Same cargo.
• Same price.
• Same volatility.

• Different risk estimate.

The only thing that changed was the assumption about the distribution of returns. One of the hardest lessons for students is that risk is not just calculated, it needs to be modeled. The formula is the easy part; choosing the model is where judgment comes in.

 

Another layer: the data window matters

 

The results also depend on how far back we look.

VaR99% - Weekly Returns
	20 years	10 years	5 years	2 years	1 year
Mean Return	0.184%	0.357%	0.333%	0.371%	0.956%
Standard Deviation	5.249%	5.765%	5.274%	5.548%	6.792%
Normal VaR	$26.75 m	$29.72 m	$27.20 m	$28.66 m	$36.16 m
Student-t VaR	$38.52 m	$42.64 m	$39.02 m	$41.09 m	$51.39 m
Extra VaR	$11.77 m	$12.92 m	$11.82 m	$12.44 m	$15.22 m

 

Shorter samples (especially recent ones) produce higher volatility and higher VaR

 

But we are not done. The horizon matters too: daily vs monthly risk

So far, the analysis used weekly returns. But risk is also shaped by the time horizon over which we measure it. The relevant risk depends on who you are: a trader may care about daily VaR, a vessel owner about weekly or monthly exposure, and an insurance company about even longer horizons tied to extreme events.

 

Below are the same VaR calculations using daily and monthly returns.

 

VaR99% - Daily Returns
	20 years	10 years	5 years	2 years	1 year
Mean Return	0.045%	0.086%	0.070%	0.068%	0.182%
Standard Deviation	2.643%	3.007%	2.320%	2.264%	2.729%
Normal VaR	$13.37 m	$15.28 m	$11.80 m	$11.51 m	$14.10 m
Student-t VaR	$19.29 m	$22.02 m	$17.00 m	$16.59 m	$20.21 m
Extra VaR	$5.92 m	$6.74 m	$5.20 m	$5.07 m	$6.12 m

Even at a one-day horizon, the choice of distribution changes the estimate by millions of dollars

 

VaR99% - Monthly Returns
	20 years	10 years	5 years	2 years	1 year
Mean Return	1.026%	2.035%	1.273%	1.620%	3.915%
Standard Deviation	12.446%	14.434%	10.192%	12.568%	16.539%
Normal VaR	$64.70 m	$76.86 m	$53.92 m	$66.59 m	$91.49 m
Student-t VaR	$92.60 m	$109.21 m	$76.77 m	$94.76 m	$128.56 m
Extra VaR	$27.90 m	$32.35 m	$22.85 m	$28.17 m	$37.07 m

Over longer horizons, both risk and model sensitivity increase dramatically

 

Bottom Line

The cargo does not change. The estimated risk does. The difference comes from:

• The distribution we assume
• The data window we choose
• The time horizon that matches our exposure

 

VaR is often presented as a clean, objective number. It is not. It is a model-driven estimate, and shaped by assumptions. For large exposures like a $216 million tanker—those assumptions can shift the estimate of extreme losses by tens of millions of dollars. That is not a rounding error, it is modeling risk.

 

That is the difference between being prepared… and being surprised.

 

Note. VaR formally includes expected return (the mean). In practice, especially over short horizons (daily), the mean is very small relative to volatility and often ignored. Over longer horizons (months or more), the mean becomes more relevant.