Volatility term structure from multiple angles
part 1
Friends,
Yesterday’s post Dragonfly Eyes served as a broad preamble to our exploration today. Be like a dragonfly — look through multiple lens.
We’re going to expand our thinking about volatility term structure to see why it’s a diamond with several facets — and most interestingly — why multiple ways of looking at it are not all correlated.
We are going to consider volatility term structure in a few ways. The differences will make the value of multiple lenses self-evident. The source of the differences have highly practical ramifications for 3 tasks:
risk management
surface modeling
trade prospecting
I would be surprised if even an experienced trader didn’t walk away folding the Rubik’s cube known as vol term structure in their head. If anything, I’m sure a seasoned trader can find some interview questions embedded in the concepts to bounce off candidates.
If you are a novice trader, you will still benefit. There’s nothing more than arithmetic in here. The value of hacking ideas from several vantage points will be obvious plus you will learn some basic transformation and measures that the more experienced folk take for granted.
About this post
The post is the first of a 2-parter. It won’t all fit in the single email view plus I’ve been under the weather this week. All the background work is done but I’m running on fumes to write it all up.
It’s a semi-Socratic progression of “show don’t tell” which serves to make the lessons your own.
We will use GLD vol data for the past year which was whimsically chosen. I didn’t snoop at the data first.
We get into implications for your own procedures.*
Finally, I talk about how and why I will extend the analysis.
*The word “your” prompts a reasonable question — who’s this for? I’m imagining a trader or risk manager at a prop shop/asset manager or an extremely sophisticated retail option trader. The material comes from pragmatism and experimentation. A durable way of seeing based on lots of pain. This is the stuff of salt mines. The way traders think.
Where does this intersect with quant and formal risk management?
Everywhere.
Quants may have a different language and set of methods for computation but the concerns are the same. I’ve said it before, but the caricature of the ivory tower theoretical physics PhD without street smarts is foreign to me. All of the gigabrain quants I’ve worked with were both practical and exceptional at asking questions. Their priority was reality.
Their knowledge becomes indispensable as risk management scales and portfolios become far more complex cross multiple strategies and asset classes. I have little to add to the code-level minutiae of implementing a large-scale risk OS. I pretty much operated at the frontier of how much I could keep in my head at once but as combinations expand exponentially, well, you’re gonna need a bigger boat.
Let’s open with a “simple” question:
If you buy a 6 month/1 month straddle spread on an equity or ETF, are you long vega?
(To be linguistically clear, you are buying the 6 month straddle and shorting the 1 month)