Using Options to Measure and Trade Earnings Volatility

Summary

The document outlines a framework from Brian Johnson’s book on earnings volatility and asks whether its Excel calculations can be recreated in Python. The described workflow estimates the earnings volatility embedded in option prices, compares historical realized and implied earnings volatility, and looks for differences that might inform trades. It also considers forecasting implied volatility before and after earnings announcements to shape volatility exposure around the event.

No formulas, Python implementation, worked examples, or trading results are provided. The author notes that Excel Solver may fail to find an optimal solution, but does not explain the source of that limitation or propose a replacement. The document is therefore an overview of a potential analysis process and a request for implementation guidance, rather than a tested strategy. Any trading use would require the underlying methods, assumptions, and performance to be assessed separately.

Key ideas

• The framework seeks to infer earnings volatility priced into options.
• It compares historical realized volatility with implied volatility around earnings.
• Differences between realized and implied volatility may inform trade ideas.
• The document proposes forecasting implied volatility before and after announcements.
• It provides no Python implementation or evidence of strategy performance.

Tags

# Python model for "Exploiting earnings volatility by Brian Johnson"


# Python model for "Exploiting earnings volatility by Brian Johnson"












Brian Johnson has written great book on Exploiting Earnings Volatility. He explains how to use his novel approach to 1) solve for the expected level of earnings volatility implicitly priced in an option matrix, 2) calculate historical levels of realized and implied earnings volatility, 3) develop strategies to exploit divergences between the two, and 4) calculate expected future levels of implied volatility before and after earnings announcements and develop trade to gain volatility to your advantage.

He has provided 2 excel sheets. Excel solver has few limitation, may not always come to optimal solution. I was wondering if anyone has converted this to Python ? Any pointers ?

Thanks

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.