Oct 11, 2018

VIX Spot And Financial Innovation

Since CBOE launched VIX Index, it proved to be immensely popular measure of overall stock market risk. The popularity however was somewhat tempered - there is no way to trade VIX directly. Over the years there was some progress and financial innovation.

In 1993 Michael Webber, a trader from UBS structured a variance swap contract on FTSE index. Variance swap is a contract which payoff is proportional to volatility squared.  Variance swaps became a very popular OTC instrument, but it was not quite the same as volatility, and people wanted to trade volatility like they saw it on the news, or calculated on spreadsheets. Variance was easier to structure, but did not have quite the same appeal.

In early 2000s two Goldman Sachs quants - Sandy Rattray and Devesh Shah decided to tackle the problem. They updated and re-formulated VIX formula, and coordinated with CBOE to launch futures in 2004. Futures on VIX index grew in popularity overtime and became the dominant market for volatility price discovery. I have to add, that I am not sure if reformulation of formula was necessary - or even beneficial. VIX liquidity seem to have grown quite organically, first in futures, then in options, and most recently in ETFs and ETNs ties to VIX futures.

However majority of investors and stock traders did not have quite the easy access to VIX. In the US futures accounts and stock accounts regulated by separate government agencies, typically require different account opening documents, may or may not be cross-margined, basically there is some friction. VIX was prominent in the news during the 2008 financial crisis, and this provided an opportunity for Barclays to launch the first VIX ETF in 2009.

VXX overall was quite disappointing for traders looking to hedge stock market risk. I am not going to review VXX decay, as it has been explained adequately, just point out that for many people it did not work like VIX index, and it did not work like VIX futures, and it created a lot of frustration.

Since then there were two (major) attempts to bring something like VIX to the market. Building on liquidity of weekly SPX options CBOE introduced VXST Index - 9-day VIX, and launched futures and options in 2014. Unfortunately the instruments did not attract liquidity and disappeared by the end of the year (if I remember correctly)

More interesting product came from Accushares with VXUP and VXDN ETFs. I am linking here to an excellent explanation from Vance Harwood. The (half-baked, imo) idea was to make corrective distributions
 - dividend-like payments to bring NAV in line with the index, but since no reverse process existed, pricing pretty much fell completely out of line with the VIX, and the products were shortly delisted.

Now, I would like to bring attention to a different product in a different market - perpetual swaps on BTC, that afaik were first introduced at Bitmex, and now trade on Deribit and Cryptofacilities. Perpetual swap is actually a spot-like instrument, that pays "dividend" if swap midprice is below index, and requires "dividend payment" is swap midprice is above index. These payments insure that swap midprice does not stray far off the index.

Such instrument would be perfect for spot VIX instrument, however practically this seems impossible. Current financial regulation landscape makes it very difficult to introduce new instruments, and back-office infrastructure is mostly not equipped to handle two-directional payments.

The reason why crypto-exchanges succeeded with perpetual swaps is because their infrastructure was build from ground-up not 20 or 10 years ago, but within the last few years, without legacy requirements, on modern systems, with immediate trade and settlement capabilities. Traditional exchanges simply cannot do that. On the other hand, crypto-exchanges cannot trade VIX perpetual swap because the index trademark, calculations, and data dissemination belong to S&P or licensed to CBOE or CME, and they would not be interested in some other exchange taking over their very lucrative VIX franchise.

So, I believe that VIX spot instrument is at an impasse at this time. Maybe this will change in the future, and investors will have an instrument that will be directly tied to the VIX index value.

Sep 28, 2018

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.

Sep 18, 2018

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.  

Sep 9, 2018

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.