Max Pain - Two For Two

Ten days ago I wrote a post about max pain theory, and wrote down two ranges - first for immediate "weekly" expiration on Sep 21, and for the following expiration, Sep 28.

These ranges - 6500-6750 and 6500-7000 appear reasonable given recent market movement, but I would not read too much into them. We'll wait and see what the market actually does.  

Well, the results are in - when I made the prediction, on Sep 18th, the index was trading at around 6250 level, with both forecasts pointing (about 0.8 std) higher. Sep 21 futures settled at 6618.85, very close to middle of the forecast of 6625; Sep 28 futures settled at 6764.52, again very close to middle of the forecast of 6725.

I am intrigued with the results, and will keep monitoring the open interest on Deribit options.

Maximum Pain Theory

I think if you're reading this blog, you're probably already a knowledgeable options trader, and have heard of maximum pain theory - an idea that market moves in a path that hurts ( causes losses ) most amount of market participants. In options it is typically stated that simulating options expiration losses by open interest at different strikes will help you to divine its expiration value. The idea is closely related to options expiration pinning - another idea that hedging pressure causes options to drift toward strikes with highest open interest.

All ideas above have been researched, and found some academic support - this paper by Avellaneda and Lipkin is an update of their seminal 2003 paper, another on pinning that contrasted optionable with non-optionable stocks, or this theoretical research into market feedbacks. However the empirical consensus is that on any particular stock this effect is rather small, and signal is too weak to be a stand-alone strategy.

I have been following options market on Deribit already for some time. Because options expire into an index, calculated from BTC/USD rate of 5 other exchanges, the hedging feedback mechanism would have a lot of friction, and probably does not exist. However, open interest could reflect market information in another way.

Here is a small spreadsheet where I calculated max pain strike for front (21st) and second (28th) expirations. Front is weekly, while second has been listed for 5 months now and has a much larger set of strikes. I trimmed the simulations in the second expiration but it does not effect the calculations.

Google sheet

The max pain strike for the front is 6750, followed closely by 6500, about 250 above where the market is now. For the second expiration, max pain strike is at 7000, followed by 6500. These ranges - 6500-6750 and 6500-7000 appear reasonable given recent market movement, but I would not read too much into them. We'll wait and see what the market actually does.  

Jonathan Kinlay on Volatility Modelling

Few weeks ago Dr Jonathan Kinlay from Quantitative Research and Trading blog published a series of excellent articles on volatility. I wanted to review and comment on the notes.

Forecasting Volatility in the S&P500 Index
Modeling Asset Volatility
Long Memory and Regime Shifts in Asset Volatility
Range-Based EGARCH Option Pricing Models

There are four main articles that discuss practical volatility forecasting topics. The material is not new; it was published around fifteen years ago by Dr Kinlay's previous funds, Caissa Capital, Investment Analytics, and later Proteom Capital.

As I understand Kinlay was the idea generator behind volatility trading, and Proteom Capital had some excellent years in 2003 and 2004, and that was pretty much it. Managers typically don't close funds because of great performance, but I don't know what actually happened; if you have any information, please send me an email.

Regardless, the published research is important, and I believe worthwhile to pay attention to. I will also comment on some more recent updates.

Forecasting Volatility in the S&P500 Index
tl;dr : Arfima-Garch, straddles trading.
my comment: While we know that autocorrelation of volatility decays very slowly, Arfima-Garch is actually pretty bad at predicting volatility. Midas and HAR(X) type models using realized volatility (and jumps, or other factors) have been demonstrated to be perform much better. The weakness of Garch seems to come from both the form and MLE estimation 'issues' but that's a separate topic.

Modeling Asset Volatility
tl;dr : volatility exhibits complex dynamics - long memory, momentum, and mean reversion
my comment: The most interesting part is the last paragraph - "Dispersion"  This is not dispersion in the index vs names sense, at least not exactly. The idea (if I understand correctly) that both first and second moment relationships can be used to construct cointegrated baskets of options. Not sure how it is supposed to work in practice - do you hedge gammas or not?

Long Memory and Regime Shifts in Asset Volatility
tl;dr : remember all that stuff about long memory? actually, it could be structural breaks, and no long memory
my comment: structural breaks can confound models, luckily there are break test available. This is an import point, don't skip this one.

Range-Based EGARCH Option Pricing Models
tl;dr :2 factor Egarch, but on ranges instead of squared returns.
my comment: 20 years ago having ability to store and process tick data was uncommon, and calculating realized volatility was not possible. Therefore models were developed based on 'newspaper data' - daily open, high, low, close. Range Egarch has several features that make it better than let's say garch - 
1, range being far better vol estimator than squared returns,
2 - log vol is much better behaved, and makes estimation more robust,
3 - two components in the model, fast and slow vol. 
Range Egarch was probably one of the best way to create decent vol models back then, but of course better methods exists today. Now all production models feature these three elements ( robust estimate of vol, power transformation / generalized error, multiple scales ) in one way or another.