I'm blatantly copying and pasting this from Mephisto but the fact that some people haven't seen this thread, and will otherwise never be able to since the account is gone is sad:
OK, picking apart $SKEW. Why is it 'hot garbage' -h/t @ jsoloff. This is for anyone who read @ SoberLook (who of course are awesome but the problem is elsewhere)
"Have you met my bro $SKEW? He's useless but his dad owns a boat."
1/ too many
Now the thing is, as always, that the exchange are making a fortune from fees on $VIX. But it's their only baby and they'd love another one just in case something happens to this one.
2/
So, a while back, I guess in a committee meeting that should have been an email, someone suggested 'ooh ooh lets do skew next'. Of course, quants had been there already, there's a replicating portfolio for the third moment just like the second. BINGO.
3/
So, just like in the $VIX whitepaper, they take the integral over strikes, discretize it, add some cutoff rules, and da-da there's your $SKEW index. The whitepaper basically writes itself. (In fact it looks a lot like an intern did)
4/
But there's a problem. $VIX is vol which is like noise which they figured out how to sell as the 'fear gauge'. Brilliant marketing. Absolute nonsense, but the muppets applaud, CNBC gets a chart for Markets in Turmoil, 50c lifts 300k $VIX options each month, everyone's happy
5/
Ok for the 3rd moment what can we do. Committee ponders... it's the "demand for tail puts", "higher power of fear"... they're struggling and you can see the problem. Once youve successfully sold $VIX as the fear gauge you cant go to a higher power and go 'ABSOLUTE PANIC GAUGE'
6/
So they dick around with it but kind of lives and kind of dies. There's no futures on it. However, it lives on in published materials and in Bloomberg as "SKEW INDEX" which is always going to sucker some journalists, students, and tourists. (Sorry, not sorry.)
7/
So basically CBOE give up. They don't know what to do with it. But its easy to calculate, it gets some airtime, so they keep it. It's another in the long line of $VIX failsons, the Chad Laxbro of vol indices. Have you met my bro $SKEW? He's useless but his dad owns a boat.
8/
And there's another, bigger problem. It's junk. The timeseries is just a mess. take a look at that chart again. It's the same number in May 2020 and in Octobor 2017, but goes to all other numbers from 120 to 145 randomly in between.
9/
You've sold it as "fear squared" but it can collapse when the market does - Q4 2018 for example. It does nothing at all in Feb18, one of the single biggest 1-day shocks ever, and then stays low all 2019. Why? Fuck knows. You couldn't screw up an indicator harder if you tried.
10/
Ok so now lets dig a bit. The $SKEW whitepaper has the answers so I don't need to do any algebra here. Blah blah 3rd moment, cubes, expectations, lets say it's all done and ticked off. Relief all round
11/
Crack open that whitepaper go to chart 2, remembering those 120-145 multi-year ranges. Now check out that red and orange line. There's a 12+ point difference in the $SKEW valuation but the put wing is near identical. Ooops. You realise now what's going on. 12/
That the 3rd moment is anti-symmetric - its like "puts-calls" (with weights to puts), whereas $VIX is "puts+calls" (with weights to puts). But the calls still impact the results, and the - sign means the result is small so you have to scale up, scaling the noise too.
13/
The call wing clearly can, and does move $SKEW 12 points on it's own with the puts completely unchanged. That's half of your multi-year 120-145 range. You're clearly fucked.
14/
This isn't a tail risk index or a fear gauge. This is a quant number. This really is the third moment of the distribution. You've put a huge weight on teeny tiny premiums on both sides - and in the real world it isn't so pretty, it's just too noisy.
15/
More details appear in Table 12 after 15 pages of formulas. In Microsoft Word. Good God, the intern really did write this - they must have given up early. Anyway, you can see the negative weighting toward 10 cent calls, versus the positive weighting to 5c puts. Awesome. 16/
For those of you under 30 - yes the SPX was at 1105 once, and we all sold it there because it was clearly TOO HIGH. Man, I'm old.
17/
You can also see that the cutoff rule will have more impact here. CBOE stop the sum over strikes of $VIX and $SKEW when you hit 2 zero bids in a row. This is kind of OK for $VIX because the meat of the $VIX components is at tighter strikes, where zero bids don't happen.
18/
But $SKEW weights the really wide tails much more as it's a higher moment. So, say Socgen's calc engine b65df3h@efd says their exo desk is painfully long 115% strike 1 month calls and they hit every nickel bid they can find.
19/
Server 14bXfq!73 at Citadel pull the bids on 2 of the consecutive strike calls on SPX that nobody else cares about, and $SKEW moves. (Same bullshit with nickel puts too, marginally more liquidity).
20/
So that's it, that's $SKEW. It's broken- if it can be, given that it never worked. Even it's own whitepaper is lame. It was a quant dream, an intern's project, a bullet point in the minutes of a meeting that should never have been called. Please stop using it. Please.
21/
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Large quantity of unique features
Really good dimensionality reduction
Ensemble everywhere!
A word on each...
When it comes to modeling everyone always goes to their favorite NNs like LSTMs etc or LGBMs and those are great, but everyone has them, and frankly, they aren't that hard to implement! Just look at Kaggle if you want an example of DS students using them everywhere...
For real alpha, you need to focus on the three most ignored areas (there is a fourth, speed, but that's not really modeling, and a fifth which I'm not telling you because I like my alpha unleaked). That sounded super guru-like, but I promise these work and I use them.
For those wondering if they are, I'll give a few comments:
T-SNE is always something I want to apply, but can never quite figure out the right way to do it. There is certainly some benefits to be had from having a basic understanding of what this all means so you can get a better chance at visualizing your features in 2D...
Much like stochastic methods, as much as I would never make them the center of a model, there is always use as a feature or ensemble. Ensemble is truly the free lunch of alpha...
A key concept for MMs is how you manage inventory. Avellaneda and Stoikov is basically the model everyone uses for this. Then there comes the offset, basically how wide your spreads are. That's your basic model of liquidity provision...
From there we get to have some fun! If you can create multiple forecasts for different timeframes (and at a super-advanced level compute speeds) you can make spreads asymmetric and intentionally hold inventory...
Entirely unprompted here, but please check out @FadingRallies. Also @choffstein's Liquidity Cascades paper (link below). The flow between MMs, passive funds, ELS, and generally the effects of reflexive dealer hedging are key to understanding this regime!
Even if you aren't a trader (I certainly am not, although I try to keep up with it all) it is still super important to understand the regime and how it all fits in from a risk perspective. You CANNOT just take the models as your risk! Eigenportfolios decay, I would know I work
with them all the time so that isn't even the perfect metric (although I do love them). Statistical models will capture some risk but at the end of the day, you choose the parameters and the distribution you feed in is key. Knowing fat tails exist is incredibly important for this
Tweeting a question I was asked/ response regarding MM:
(me adding bonus resources):
A great example of C++ HFT MM algorithms. An improvement idea I have suggested to the author but can also be attempted by interested algotraders is that a fast model like XGBOOST (there is a C++
library) is used alongside some alphas to make spreads asymmetric before traders can trade against you and you get negative edge in those trades. A large part of market making is cheaply executing alphas by trying to get inventory on the side of your predictions and also by
getting out the way of adverse conditions by making your spreads asymmetrically wide (traders with alpha against you). github.com/hello2all/gamm…
I think you can probably classify modeling and feature engineering into a few areas: ML, Statistics, Time Series, Microstructural, some fun extras like entropy which are super weird to work with TA, data-mined alphas, and signal processing.
1/who knows lol
I'll probably speak on each of these eventually, but today I think it'd be good to get some publicity on signal processing. It's underhyped compared to ML and just as deserving.
2/
A lot of the literature is exclusive to electrical engineering and CS, but I can tell you there is lots of alpha in the area. As the story usually goes NN models like LSTMs get a bad rap performance-wise bc of their terrible application
3/