Stocks & Commodities V. 33:02 (28-33, 36): Rebuilding Skews by Massimiliano Scorpio
Product Description
Rebuilding Skews by Massimiliano Scorpio
The Eagle Eye
In this second of a two-part series, find out how constantly calibrating a skew model will result in a more timely and accurate volatility curve.
Last month in part 1, I emphasized the importance of the shape of the volatility skew when trading options. I also discussed how to create a template based on historical statistical research. Here in part 2, I will discuss how you can test the process and look at skews more closely.
I grouped the database into classes (one for each number of days to expiration, starting from 60 down to 1). I did not include data from the most recent year since I wanted to use it later for calibration and testing. For each day, I calculated the implied volatility (IV) to create a delta distribution table of normalized volatilities (see part 1 for details on how to create this). In Figure 1 you see a part of the table, that is, six rows of the 60 days to expiry class.
The next step is to define and measure the slope of the skew. I evaluated this issue through the following process:
Strangle IV size: I predetermined a list of strangles built from my delta table seen in part 1 (58%–42%, 66%–34%, 74%–26%, 82%–18%, 98%–2%). A strangle is an option strategy in which you buy (for a long strangle) or sell (for a short strangle) an out-of-the-money (OTM) strike call and put with the same expiration. Strikes have, for my purpose, been replaced by delta.
The Eagle Eye
In this second of a two-part series, find out how constantly calibrating a skew model will result in a more timely and accurate volatility curve.
Last month in part 1, I emphasized the importance of the shape of the volatility skew when trading options. I also discussed how to create a template based on historical statistical research. Here in part 2, I will discuss how you can test the process and look at skews more closely.
I grouped the database into classes (one for each number of days to expiration, starting from 60 down to 1). I did not include data from the most recent year since I wanted to use it later for calibration and testing. For each day, I calculated the implied volatility (IV) to create a delta distribution table of normalized volatilities (see part 1 for details on how to create this). In Figure 1 you see a part of the table, that is, six rows of the 60 days to expiry class.
The next step is to define and measure the slope of the skew. I evaluated this issue through the following process:
Strangle IV size: I predetermined a list of strangles built from my delta table seen in part 1 (58%–42%, 66%–34%, 74%–26%, 82%–18%, 98%–2%). A strangle is an option strategy in which you buy (for a long strangle) or sell (for a short strangle) an out-of-the-money (OTM) strike call and put with the same expiration. Strikes have, for my purpose, been replaced by delta.
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