Options - Exploring Implied Volatility with Python


I will take the perspective of an investor seeking covered call opportunities – looking for and examining stocks that will pay the most premium.

To start:

- Definition: Implied volatility is a metric that captures the market's view of the likelihood of changes in a given security's price. Investors can use it to project future moves and supply and demand, and often employ it to price options contracts.

Implied volatility is not the same as historical volatility, also known as realized volatility or statistical volatility. The historical volatility figure will measure past market changes and their actual results.

Examine: Pick 5 companies all with earnings out of the way

- AAPL

o IV = 25-28%

- JNJ

o IV = 16-18%

- FB

o IV = 26-28%

- TSLA

o IV = 56%

- JPM

o IV = 22%


Order – high to low – TSLA, AAPL/FB, JPM, JNJ


To me, implied volatility doesn’t mean much by itself; however, when compared between different companies, it can give insight into call premiums one can expect to collect on a certain position. To automate this process, I wrote some python code to visualize the Percentage of Premium one can expect to capture vs Strike Price (as a percentage above the current stock price).


As expected, premiums currently priced in the market align (rank order) with what one would expect based on implied volatilities noted for the 5 stocks in scope. TSLA pulls in the highest premium and JNJ pulls in the lowest.

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