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Normalizing Implied Volatility Changes Across Option Expiries

Article Quant Q&A · Author: Manish Arora
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Summary

The document describes a modeling problem: estimate the probability distribution of the next day’s change in at-the-money Black–Scholes implied volatility using current implied volatility, days to expiration, and the underlying’s percentage move. The author notes that implied volatility changes differ by expiry, with near-expiry options showing larger movements, and proposes normalizing those changes before estimating their distribution with kernel density estimation.

The only response suggests scaling implied volatility using either a cross-sectional average across strikes or a time-series average for the option over a recent period. It offers these as possible normalization references and notes that both have appeared in academic work, while expressing uncertainty about industry practice. The exchange does not specify a definitive normalization formula, compare the alternatives, or provide validation results. Researchers would need to test how the chosen scaling handles expiry, moneyness, and changing volatility regimes in their own data.

Key ideas

  • The proposed model predicts a next-day implied volatility reaction from current volatility, expiry, and the underlying move.
  • Expiry can affect the scale of observed implied volatility changes.
  • Possible scaling references include cross-sectional averages across strikes and time-series averages.
  • The discussion gives no tested formula or evidence that one normalization method is superior.

Tags

OptionsVolatilityStatisticsMachine learning
Full text
# Seeking Advice on Normalizing Implied Volatility Change for Options Modeling


# Seeking Advice on Normalizing Implied Volatility Change for Options Modeling












I'm working with a substantial dataset spanning five years of weekly options data, with records down to the second. My goal is to develop a model that can accurately predict the probability mass function (PMF) of the BSM ATM implied volatility(IV) reaction in 1 day if current IV, current time to expiration(DTE), and percentage change in underlying are given.

Here's where I'm seeking advice: Implied volatility changes vary significantly with DTE, with options nearing expiry showing larger changes compared to those further out.

To address this issue, I'm looking to derive the normalized implied volatility change value for various expiries relative to the percentage change in the underlying asset over 1 day.

After normalizing the implied volatility change, I'll use kernel density estimation (KDE) to build its distribution.

I would greatly appreciate any insights, best practices, or recommendations from experienced practitioners in the field regarding the normalization process.

Thank you all for your time and assistance.

## Answer by KaiSqDist (score 1)

https://quant.stackexchange.com/a/78376

I did abit of research on normalizing IV, where I used the cross-sectional average of IV among option IVs (with different strike prices). You may also use a time-series average of IVs - like the IV of that option for the past 1 month. Both of these averages can be used to scale/normalize your IV values.

I am not sure if this is the industry way, but there are academics that have used both of these methods that I mentioned above.

Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.