# Difference between IV Rank & IV Percentile

In the OptionSamurai Scanner, we use IV Percentile to compare the IV of different stocks and call it “IV rank” as we think it is more intuitive to understand. We know that other market players (such as Thinkorswim or tastytrade) use those two terms differently. To avoid confusion, this post will explain the **difference** between the two.

IV Rank and IV Percentile

**IV percentile** is a measure of implied volatility vs. its past values. It measures how many of the past IV values are lower than the current IV value. __An example best explains this__: If IBM IV percentile is 34% – It means that the current IV value is higher than 34% of previous values (and, of course, lower than 66% of them).

**IV Rank** is also a measure of implied volatility vs its past values, but it looks only at the highest and lowest values. The formula is:

[IV – Lowest_IV]

[Highest IV – Lowest IV]

If IV values are not too extreme and the look-back period is long enough (about a year), then values will be very similar. However,** we feel that IV percentile is a better indicator and is more robust** (albeit harder to calculate).

**This simplified example will show the difference between the calculations:**

In the above example, we see four past values. Now we have a new value – 15, and we will calculate both the IV percentile and IV Rank:

- When calculating the
**IV rank**, we can see that 15 is exactly the middle between the highest and lowest IV values (10&20), and Thus the rank will be 50%. - When we calculate the
**IV percentile,**We can see that 15 is larger than three of past values out of the four values in our set so that the rank will be 75%.

We feel that the IV percentile is a better indicator and we use it, even if we call it “IV rank” which we feel is the more intuitive way to understand it.

## How to scan for IV rank in OptionSamurai:

- We have a complete volatility suite in Option Samruai. Read the knowledgebase here.
- Read our Implied volatility research here

(Originally published on Jan 29th, 2015, and updated since)

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