Volatility, VIX products, options, ETPs, etc. and random musings about travel, wine, music, AI, sports, etc. Never investment advice. https://xitter.gq/t.co/dO8lFhL83C
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A primer on pods
In honor of some “pods blowing up” and me being traveling I will outline some concepts about pod risk management about which I do know a thing or two, as my name might suggest.
It is a common perception that pods are factor beta neutral and thus largely hedged. That is not quite true. A truer statement is that they are factor beta neutral to the extent they can measure the exposures.
Most equity risk models are derivatives of the original axioma and barra factor models. There are now many things that go into this but at their core they are a 12 (IIRC) variable linear regression. And mathematically, the unexplained remainder, the error term, is assumed to be the alpha (Upon which the team gets paid). You well recognize most of these factors: size, momentum (short and long), quality, dividend, domicile, sector, etc.
Now even the dull among you may note that there are in fact many other things that go into a stock. That’s true and that’s one reason why many people at the pods are very rich. There is though a very active and live debate to the highest level of these firms as what you “want” in the other bucket. On the one extreme, scheduling a proprietary drone flyover on the Aurora AV truck up fitting facility to measure parking lot activity vs expected timeline….that feels like pretty good alpha. On the other extreme, going long the Canadian listing rather than the U.S. listing because the Canadian listing diversifies the domicile score so you get a higher portfolio level idio score…..that doesn’t feel like great alpha. But the range is wide: paying GS a billion dollars so your team is the first meeting at the conference? That’s kind of the pod scale advantage. Albeit less so if every other pod is in the room. Going balls long large cap domestic U.S. GLP-1 manufacturers vs non GLP-1? Well that kind of is thematic alpha (depending on the work put in by analyst), but even if you’re of the opinion fuck it, let the kids rip (a reasonable view) you also know at the levered portfolio level that we aren’t measuring that risk completely. This line can really be illustrated with the concept of a “custom factor”. Maybe going long a shit load of SPACs is alpha, maybe it isn’t, but how the FUCK are you gonna build a portfolio level SPAC factor exposure loading in late 2021 when spacs didn’t functionally exist until 2021????
Anyways, interesting & active philosophical debate but there are some downstream derivatives.
Firstly, as a well known matter, 6 sigma events happen in finance all the time. There’s one every year in some corner of the market. So ex ante we know our models aren’t “great”.
Second, everyone knows the agent is an unobserved blind spot. It’s impossible to actually “know” why the analyst and PM are doing anything. You like to think they’re doing it on the back of deep fundamental quality work, but the median analyst and PM isn’t good at their job. So that assumption is known to be unreliable. The capital allocation solution to this is momentum chasing at the portfolio level: you allocate more to winning pods and you cut the losers fast. This is a brilliant approach because it means the pod business model is capital light- they identify and scale the good teams without actually having to invest in knowing which pods have “good” process. It’s worth really appreciating that point because it’s one of the true genius aspects of the model, and at its core it’s why the GPs are so fucking rich. (Deep finance readers will note that some of the original papers on factors saw that one of the earliest possible explanations for the decade-long trend nature for factor performance is implicit fund flow pressure from the momentum chasing nature of capital at the LP level)
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One for the archives, at the very least
In today's Macro Volatility Digest:
🔹 Implied volatilities declined across most asset classes last week after Democrats swept Congress and US economic data came out better than expected.
🔹Skew and convexity were bid on the sell-off, as hedging demand jumped sharply.
🔹Correlation levels remain muted, with COR1M Index increasing only modestly to a “high” of 22% on Thursday. This is highly unusual.
Get a sneak peek below, and download the full report: bit.ly/4iGzuJo
ALT A market commentary document from Cboe titled "No Contagion (Yet): Equity Vol Spikes, Macro Vol Fades" dated November 9, 2020. It includes textual analysis on asset class implied volatility, with two featured charts: "Exhibit 1: Asset Class Implied Volatility" and "Exhibit 2: Decomposition of Thursday's VIX Spike." A contact email, [email protected], is mentioned at the bottom.
ALT An analytical report titled "Macro Volatility Digest" from November 9, 2020, by Cboe. It contains multiple line graphs depicting volatility trends for various asset classes, including Equity, FX, and Credit. Each graph compares implied VS realized volatility. A summary section discusses overall market insights. Contact information for Mandy Xu, Head of Derivatives Market Intelligence, is included.
ALT The image is a financial report titled "Macro Volatility Digest" from Cboe. It contains a "Cross-Asset Correlation Matrix (1M)" table showing color-coded data, with various percentages indicating correlations. Below the table, graphs titled "Cross-Asset Correlation Analysis" display trends in equity rates, credit, and gold correlations, each over different time periods. Contact details are at the bottom left.
Bill Luby retweeted
I can believe that there is a risk premium to be short vol-of-vol, even after hedging out beta, but the question is who should be harvesting it and in what size. A benefit of being long vol or vol-of-vol is that even if the average payoff is negative, it occurs when there are dislocations and thus good trading opportunities.
Traders Look to Wall Street Quant Strategies to Hedge Next Crash, by Christian Dass
bloomberg.com/news/articles/…
'As banks offer more and more sophisticated quant strategies, their hedge fund and institutional investor client conversations are increasingly centered on a common objective: crash protection with zero carry cost.
Whether it’s improved trade timing through macro triggers or the latest volatility momentum signal using intraday data, a lot of Quantitative Investment Strategies research continues to be poured into what some refer to as targeted convexity.
One way investors may limit the cost of hedges is through strategies that sell volatility. A perusal of QIS approaches shows systematic short volatility performing very well over the long term, with the carry from selling VIX call spreads doing best of all across providers. It even outperformed various dispersion tactics that have been popular with hedge funds over the past few years, according to Premialab data.
“A daily question I have right now is clients looking for a hedging strategy with the lowest cost of carry,” said Florian Peres, executive director at QIS data provider Premialab.
“We are seeing interest in combining vol carry with protection to help offset the cost of hedging,” said Adrien Geliot, Premialab’s chief executive officer. “VIX call spread carry is a good example — its historical performance reflects the premium embedded in VIX options, supported by demand for volatility protection and the pricing of vol-of-vol risk.”'
It’s time to think about vol of vol of vol…
geek alert....
there's "vol", "vol of vol"...and dare I say it..."vol of vol of vol"... good god. In simpler terms: "the variability of implied vol on VIX options" ...that's vol of vol of vol.
and by at least one count, this has reached a record...below, the 1m realized vol of the VVIX. higher than Volmaggedon, the Covid Crash, the Aug'24 VIX event, the Tariff Tantrum.
In the last month, the $VVIX has had up moves of 18.7%, 14.2% and 49.3% (Thursday, 11/5). It also had a 15.8% down day (11/4). the surge last Thursday drives a tremendous proportion of the overall 1m realized vol. when it comes to vol, "big moves matter most."
in markets, p/l is about prices. we talk about vol only because it can be a shorthand for prices. if you move the implied vol (VVIX) on a 1m $VIX straddle up by 20%, you move the straddle price up by the same amount. if you move the implied vol input down by 20%, the straddle price declines by 20%.
Bill Luby retweeted
TLDR: Thursday could easily be an appetizer for more to come. If financial market prices are previewing a slowdown in the capex that is the machinery not just for equities and credit but for the real economy, more correlation and downside is coming.
Since 1990, there are 46 days in which the SPX has fallen by 4% or more. Almost half – 21 of them – came from the GFC. Covid adds 9. The other “crisis events” – Asian in 1997, LTCM in 1998, and the Eurozone crisis in 2011 – account for 6 more. Adding them up, 36 of the 46 down 4% days have occurred during a period that is referred to as a “crisis”.
On my podcast, Kevin Warsh told me, “if you’ve seen one financial crisis, you’ve seen ONE financial crisis.” It’s a clever way of saying that each of the episodes cited above were unique in origin and also unique in the firefighting tools utilized by policymakers to douse the financial flames. “Crisis” seems to be shorthand for “the government had to do something”.
The other 10 big ones? Among them are the day the market opened after the September 11th terrorist attacks. The telco bust in 2002. China’s FX re-peg in 2015. Volmaggedon in 2018.
You don't get a 4% down move in the SPX without both very high single stock vol and high correlation. The stocks have to move down a lot and move down together. But...amidst the carnage, 73 (14%) of the stocks in the SPX were up on 11/5.
Below, a scatter plot below that shows the 4% down days since 1990 and the number of stocks that were up on each of them. 11/5 ranks high up there in terms of number of stocks rising. The day after "Liberation Day" - 4/3/25 - saw a 4.8% decline in the SPX with 95 stocks higher.
One part of my framework for thinking about market risk is to categorize the origins as economic, monetary, financial and geopolitical. I would argue that the tumult of the past week is mostly financial with a dose of geopolitical. On the last part, the Blue Tsunami couldn't have helped the risk appetite that has been a big driver of this incredible market run.
But it's mostly the "setup" and the conditions of crowding that played a big role. When risk exposures are too similar and concentrated and leveraged, the odds of a correlated unwind increase quite a bit. If the SPX is a 15 vol asset, we just experienced a 4SD move. And as Ace Rothman said in Casino, "it could not happen, it would not happen." Who did it? The market did.
This could easily be an appetizer for more to come. If the financial market prices are previewing a slowdown in the capex that is the machinery not just for equities and credit but for the real economy, more correlation and more downside is coming.
Great piece on bubbles, probability, expected value, etc. by Jeff Yass
"Calling a bubble is a free option. If the market craters, you’re famous. If it doesn’t, you go into never-mind mode and call the next one. If the bubble callers know the true price and the market doesn’t, let’s see the track record" --Jeff Yass in WSJ opinion
wsj.com/opinion/theres-no-su…
When it comes to equities for the next week and the rest of the year are you…
35%Bullish / bullish
9%Bullish / bearish
34%Bearish / bullish
23%Bearish / bearish
313 votes • Final results 1
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A good summary of what constitutes cascading selling
اليوم قريت مقال في Bloomberg عن اللي صار أمس في السوق الأمريكي، وهذا ملخصه:
جلسة استثنائية مسحت ~3.7$ تريليون من قيمة الأسهم الأمريكية!!!
المفارقة أن الهبوط ما بدأ بأخبار سلبية عن الشركات بل بجني أرباح من الأفراد. لكن أنظمة التداول الآلي هي اللي ضخّمت الحركة وحوّلتها إلى كرة ثلج -ان صح التعبير.
وش صار بالضبط؟ (Flow Dynamics):
1- المتداولين الأفراد كانو هم الشرارة الأولى للبيع وصافي بيعهم كان ثالث أعلى مستوى حسب بيانات Vanda وما يسبقه إلا يومين وقت كورونا في مارس 2020!
2- بعدها بدأت صناديق التحوط ثم الصناديق التقليدية (Long-only) تبيع علشان تحافظ على أرباحها هالسنة قبل لا تتآكل، والمشكلة انه ما كان فيه مشترين كبار بشكل يذكر يشيلون هالمعروض ويوازنون الكفة!!
3-ووقتها بدأ البيع الآلي (Systematic Selling):
وهي صناديق تبيع تلقائياً حسب قواعد ثابتة بدون قرار بشري. وأمس اشتغلت ثلاث أنواع منها مع بعض وكل نوع كان عنده سبب مختلف يخليه يبيع:
• النوع الأول صناديق ال CTAs (اختصار لمستشاري تداول السلع)، واسمها جاي من أيام ما كان شغلهم محصور في عقود السلع والفيوتشرز- اليوم أغلبها صناديق كمية (Quant funds) تتبع الزخم- فلما كسر السوق مستويات فنية مهمة باعت عقود آجلة على المؤشرات بحوالي 13$ مليار حسب تقدير Vanda.
• النوع الثاني صناديق استهداف التذبذب (Volatility-control funds)، وهذي هدفها تحافظ على مستوى مخاطرة ثابت في محفظتها. فكل ما زاد تذبذب السوق تبيع جزء من الأسهم اللي عندها تلقائياً ومع قفزة الVIX تشير حسابات Nomura ان قواعدها فرضت عليها بيع ~61$ مليار!!
• النوع الثالث الصناديق المتداولة بالرافعة (Leveraged ETFs)، وهذي تضطر تشتري لما يرتفع السوق وتبيع آلياً لما ينزل علشان تحافظ على نسبة رافعة ثابتة بين حجم مراكزها وأصولها الفعلية، وبناءً على حجم النزول الي صار يرى Nomura ان هذا تسبب في ~47$ مليار بيع إضافي آخر اليوم!!
4- تحوط صناع السوق (Short Gamma): صناع السوق اللي باعوا عقود الخيارات يتحوطون ببيع الأسهم كل ما نزل السوق، ووفقاً لبيانات Nomura فإن كل نزول %1 كان يتسبب في ~7$ مليار بيع إضافي. يعني النزول يسبب بيع والبيع يسبب نزول وخذلك دوامة بيع قشرا!!
Bill Luby retweeted
Wow tech is now among the cheaper S&P 500 sectors GS
I mean, Real Estate has a higher P/E
Hysterical that Barron’s is quoting my “wounded hamster bounce” quip
barrons.com/livecoverage/sto…
A little more color on what we’ve already heard
Thursday, November 5th, was a day of extremes:
•US long/short Hedge Fund net leverage declined by 5% day over day, the 5th largest single-day decline since 2010. Net leverage is now in the bottom decile since 2010.^
•Long/short fund active gross flow since June has amounted to a -10.7% reduction relative to their gross market value at the end of June, the most in at least 10 years.^
•Hedge Funds are now underweight AI stocks* relative to market cap weights, but AI is still a very large percent of absolute exposures at 32%.^
•Levered ETFs had the largest amount to sell on data going back to 2007, bringing $57bn of US equity supply.
•Retail selling of US equities in the first 90 minutes, based on MS QDS estimates using public data, was the second largest going back to 2016.
•Equities are now starting to trade in a more correlated fashion, often signaling that risk reduction is broadening out.
^Sourced from MS Prime Brokerage.
*AI stocks defined as the constituents of the MSXXAI Index, made up of AI tech beneficiaries, AI power names, and key hyperscalers
Bill Luby retweeted
Data centers in Loudoun County, Virginia: “We get $1.2 billion a year in local tax revenue, about 40% of all of our tax revenue...on 3% of our land”
Buddy Rizer, the county's economic development chief, explained how “data center alley” grew:
“We would have never looked at having 55 million square feet of data center, 230-some buildings. That was not the plan. The plan was to utilize the fiber loop, the internet exchange, to utilize a reclaimed water line, and buildings that were already built. That was the plan, right? And so that made a lot of sense.”
“But, you know, fast forward, today we are largely dependent on that. We get $1.2 billion a year in local tax revenue, about 40% of all of our tax revenue. And while that looks overwhelming, data centers take only about 3% of all of our land in the county. So it’s quite a return on investment. You know, 40%, $1.2 billion, on 3% of our land.”
SpaceX’s Arizona data center, per a county supervisor: “We could be reducing property taxes across the board in Cochise County, the entire county, by 20%”
Frank Antenori, whose district includes the site, brought up the county’s tax math during a live Q&A:
“Here’s just something to just chew on. Arizona has an expenditure limit requirement that is placed on counties. If we exceed that expenditure, we have to rebate back to the taxpayers a certain amount.”
“So, if we’re receiving revenue in from SpaceX, by law, we have to offset that to the taxpayers. We have the potential to reduce, based on the projected revenue they’re creating, as much as 20%.”
“Let me just say it again. We could be reducing property taxes across the board in Cochise County, the entire county, by 20% to offset for the revenue here, because we can’t spend all that money. So, we have to give that money back to the taxpayers. The law says we have to.”
“So, you could see a potential significant impact to local property taxes. What that is, I don’t know. We’re going to have to do the math on it. But the law, if we have this large increase in property tax revenue, we have to balance those rates. So that will reduce dramatically the property tax burden of every resident in the county.”
“The construction sales tax, which also is the TPT, the transaction privilege tax, the retail tax, and the jail excise tax. So that will be part of the construction tax. We could literally use that money to pay the jail off in 2 to 3 years, as opposed to the 13 to 20 years.”
“So this could be a revenue to eliminate the half-cent sales tax in 2 to 3 years. We can get rid of that half-cent sales tax.”
Long, short or neutral, it’s always important to see what Jim Chanos is thinking. Like the rest of us, he’s wrestling with the concept of value stocks.
