> For the complete documentation index, see [llms.txt](https://klickanalytics.gitbook.io/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://klickanalytics.gitbook.io/docs/apps/single-security-analysis/volatility-analysis.md).

# Volatility Analysis

KlickAnalytics calculates a lot of derived data from market data. We just updated our volatility app to include historical standard deviations with the following periods;

* **1 Week**
* **20 Days**
* **1 Month**
* **3 Months**
* **100 Days**
* **6 Months**
* **9 Months**
* **Year-to-date**
* **1 Year**
* **3 Years**
* **and 5 Years**

<figure><img src="/files/ZxP5NRrUv2LGMeGkujrh" alt=""><figcaption></figcaption></figure>

Defining price movement in terms of standard deviations is preferable to using percentage change because using standard deviations puts all the stocks on a level playing field. There are categories of stocks that are typically more volatile and have larger percentage price changes than other stocks. For example, low-priced small-cap stocks or high-tech stocks are typically much more volatile than lower volatility stocks such as utility stocks. If we used percentage change to define price movement, then high-volatility stocks would always dominate the list and we would miss lower-volatility stocks that might have an unusually large movement on a particular day.

This will allow our users to view price movements of any global symbol in terms of historical standard deviations and much more.