How to Measure How Unstable a Stock's Volatility Is

Use Alphanume's Vol-of-Vol Index to quantify volatility instability across names without computing rolling statistics yourself.

Level tells you how much a stock is expected to move. Stability tells you how much that expectation itself jumps around. If you sell premium, you care about both, because an unstable volatility profile means your mark-to-market can swing even when the underlying barely moves. The Alphanume Vol-of-Vol Index endpoint turns that stability into a single number per name.

The endpoint

GET https://api.alphanume.com/v1/vol-of-vol

Authenticate with ?api_key=alp_your_key or an X-API-Key header. The filters are min_iv_vov and max_iv_vov, min_hv_vov and max_hv_vov, min_iv_vov_rank and min_hv_vov_rank (0 to 1), and only_final.

Find the most unstable names

import requests

r = requests.get(
    "https://api.alphanume.com/v1/vol-of-vol",
    params={"min_iv_vov_rank": 0.9, "only_final": "true", "api_key": "alp_your_key"},
)
for row in r.json()["data"]:
    print(row["ticker"], round(row["iv_vov"], 3))

A high iv_vov means the name's implied volatility has been swinging widely relative to its average over the last month. That is a flag for caution if you are short premium, and an opportunity if you are trading volatility directionally.

Find the calmest names

Invert the filter to find stable-volatility candidates that tend to behave predictably:

GET https://api.alphanume.com/v1/vol-of-vol?max_iv_vov=0.1&only_final=true&api_key=alp_your_key

Combine it with other volatility data

Vol-of-vol is most powerful as a filter on top of a level signal. Use IV/HV Rank to find names with elevated volatility, then use vol-of-vol to keep only the ones whose volatility is stable enough to sell with confidence. All three datasets share the same universe and cadence, so they join cleanly on date and ticker.

Access and limits

Vol-of-Vol is on the free tier as a rolling 30-day delayed window, Pro at 600 requests per minute. The first month of a new ticker is absent while the 21-observation window warms. Start with a free Alphanume API key and the API documentation, or explore it on Alphanume.

Using stability as a filter

Vol-of-vol is most powerful as a second filter on top of a level signal. The pattern is simple: find names with elevated volatility using IV rank, then keep only those whose volatility has been stable enough to sell with confidence. That two-stage screen weeds out the names where a juicy-looking premium is really just a symptom of an unstable, hard-to-manage position.

Because the index is a coefficient of variation, the numbers are comparable across the whole universe, so a single rank filter does the work. The separate iv_vov and hv_vov fields let you distinguish instability in what the market is pricing from instability in what the stock is actually realizing, which often tell different stories ahead of a catalyst.

Frequently asked questions

What endpoint provides vol-of-vol?

GET https://api.alphanume.com/v1/vol-of-vol. Authenticate with ?api_key=alp_your_key or an X-API-Key header.

Which filters can I use?

min_iv_vov and max_iv_vov, min_hv_vov and max_hv_vov, min_iv_vov_rank and min_hv_vov_rank from 0 to 1, and only_final for settled rows.

How do I find the most unstable names?

Call the endpoint with min_iv_vov_rank=0.9 and only_final=true to return the names whose implied volatility has been the most unstable across the cross section.

How do I combine it with other signals?

Use IV/HV Rank to find elevated volatility, then use vol-of-vol to keep only the names whose volatility is stable enough to sell. All three datasets join cleanly on date and ticker.

What is the difference between iv_vov and hv_vov?

iv_vov measures the instability of implied volatility, hv_vov measures the instability of realized volatility. Each has its own mean and standard deviation fields over the 21-observation window.