Building Blocks of Volatility

Parsing a "qualt" framework for the components of variance

Created with DALL-E

Setting the Table:

• In case you’ve been hiding under a market rock, NVDA carried the entire stock universe on its back to bring indices to all time highs. 

• Liquidity dipped a bit yesterday as markets widened into the close. We often associate bull markets with drops in volatility and tightening of spreads, but that’s more of a medium term phenomenon. A sharp repricing up or down of spot puts markets temporarily wide.

  

• We’ve been seeing the 30 day implied tango with 30 day realized, and the backward looking 30 day VRP has ticked negative. Intraday realized continues to peg much lower than either figures, so these are after hours moves.

  

• SPY curves continue to flatten, with the 10 day IV dropping puts and bidding calls relative to its recent past. Fear of missing out is the risk here, downside feels impossible. Further out of the money collars start to look cheaper because of this.

  

Identify:

• We spend a lot of time talking about different measures of volatility. Implied, historical, GARCH, Parkinsons, etc.  If you really want to go down a rabbit hole, NYU Stern has a “V-Lab” to play with many different models and their variants. These are all quantitative models for describing what is going on in the volatility space. 

• Today, I want to propose a “qualt” framework for thinking about volatility, and breaking down what’s happening under the hood. We’ll talk a bit about the VIX, how it’s different than ATM volatility, components of event vol, and other frameworks to break down what the figure is saying. 

• When seeking trading opportunities on the buy side, it’s critical to have an opinion about what might be mispriced relative to your risk tolerance. The fact that markets are generally efficient does not obviate the existence of positive expected value trades, particularly within a disciplined framework. Parsing what makes up volatility helps us do that.