how an option trader extracts earnings from a vol term structure
a subject that encompasses many vol topics
Friends,
Earnings are a highly concentrated source of volatility for public companies because besides reporting results they give guidance on the future, discuss what they are seeing across business lines, as well as risks and opportunities for growth. Earnings reports are a rich source of information and in the Claude Shannon sense of the word, information is volatility.
As expected, option prices that include the earnings date command a premium implied volatility as the market expects the stocks to move on the burst of new information. The observation of a premium earnings IV leads investors and traders to important questions.
How much is the premium? In other words, how do I disentangle the amount of volatility that is “normal” vs the amount coming from the market’s expectation of how much the stock will move?
If I am a volatility trader focused on the relative value of options between names or I am a dispersion trader who cares about the relative vol levels between and index and its components how do I compare the volatility between a name with earnings (or a event specific to the name) to other names?
Our task is beckons. We must extract earnings from the vol surface.
That probably sounds like a tedious, quanty operation. But it’s not. It’s actually a pretty simple procedure once you understand the building blocks. In fact, the procedure is an implicit review of 2 main topics. Because this topic encompasses* the prior topics it acts as a test of your knowledge as well as a step forward.
Prerequisite Building Blocks
I won’t review the building blocks here but I’ll point you directly to the relevant calculators which document the procedures.
Given 2 expirations we can effectively subtract the volatility of the near dated expiry from the later dated expiry to imply a forward volatility or the amount of volatility implied in between the 2 expirations.
When the market anticipates events like a stock's earnings date, it often factors increased volatility into the affected option expirations.
Traders analyze this implied volatility by separating it into the volatility for the event day itself and the typical daily volatility.
To do this, a trader estimates an expected move size for the event.
The unintuitive impact of events
It’s worth emphasizing how important events to understanding an option surface. It’s one of those things that intuition is a poor guide to. The arithmetic is worthwhile.
Consider this situation.
A straddle has 40 business days until expiry. The name typically moves 1.5% per day. We’ll just use trader math to estimate a fair annualized volatility of 24% (1.5% x 16 because 16 is approximately √251).
However we get 2 new pieces of info.
The IV is actually 36%
Earnings occur in 35 business days.
We can estimate an earnings vol by acknowledging that term vol includes 39 “regular” days and 1 “event” day.
We presume that a regular day has 24% annualized vol. So what “event vol” makes the term vol worth 36%?
We are basically solving for what event vol reconciles these facts given that we know the average vol (the term vol) and the “regular” vol.
[Keep in mind variances are additive but not volatility. Variance is simply vol squared.]
Term variance = regular variance + event variance
.36² * 40 days = .24² * 39 days + X² * 1 day
Solve for X.
x = event vol = 171%
The event is a 171% vol event for a single day but this is in units of annualized volatility.
Convert back to daily volatility by going in reverse — divide by 16. (I’m resisting a reference to the Spaceballs vacuum scene).
171%/16 = 10.7%
Remember that’s now a daily vol (aka standard deviation). We should convert it to a straddle as a percent of the underlying because that corresponds to the what people actually talk about — “expected move size” on earnings.
Just multiply by .8 since a straddle is the same as the mean absolute deviation.
.8 * 10.7% = 8.6%
[To review, see 😈The MAD Straddle]
Let’s take inventory.
The stock moves 1.5% per day which would correspond to a 24% vol name.
However, the vol is 36% implying that on earnings it’s expected to move 8.6% on that single day.
The variance coming from all regular days is 39 * .24² ~ 2.25 (unitless, unintuitive number)
Event variance is 1 * 1.71² ~ 2.94
Despite earnings being 1/40 or 2.5% of the weight in day terms, it’s 2.94/(40 x .36²) ~ 57% of the total variance until expiry. That day has more option premium associated with it then all the other days combined. The bulk of the straddle decay occurs on that day.
This also means the theta of the preceding days is lower than you think. In practice, what happens is the vol creeps up every day offsetting some of the model theta. You can think of a glide path where as you get closer to earnings the average vol per day increases as “low vol days” peel off and the earnings day drives bulk of the straddle. This same mental image can help you understand why an event very far in the future doesn’t show up so strongly in the terms structure — its impact is diluted by the sheer quantity of regular days before it.
[These concepts underpin the trading strategy known as Renting the Straddle.]
* See educator and MathAcademy architect Justin Skycak’s explanation of encompassing vs prerequisite graphs as well as Principles of Learning Fast
Now you are convinced that this is some part important, some part interesting and you already have a taste of the most complicated math it requires (6th grade). We just need to pull it together.
Extracting earnings from a term structure of implied volatility (as opposed to a single expiry) requires using our building blocks in conjunction. The same technique can be extended to multiple earnings as well as any kind of event.
This is a good time to remind you that much of the trading is about making apples-to-apples comparisons. Normalizing data so that the comparisons are relevant is so much of the work to be done. It’s more grindy than sexy. But it also shifts the focus from what novices think investing is about to the work that actually needs to be done — measurement not prediction or “seeing the present clearly”.
As we step through an earnings extraction, I will point to real-life examples of what I mean by measurement not prediction.
A few selling points on this post:
The building blocks do the heavy lifting so this won’t take long.
The yield is insane — this is one of those topics that opens lots of mental doors.
I provide a link to a spreadsheet so you can play with the ideas yourself or extend them as desired.