Last week, in embedding spot-vol correlation in option deltas, I showed how vol paths use anticipated changes in implied vol as the spot moves around to estimate more accurate deltas. It’s a maneuver that respects delta fully as a hedge ratio rather than its narrow textbook sensitivity of “change option price per change in underlying price, all else equal”. We are sure (enough) that all else ain’t gonna be equal, so we can use the knowledge to improve the hedge ratio.

The post explains how a “vol path” takes a slope parameter that dictates how ATM vol changes as spot moves. For example, a slope of -3.0 means a 1% rally drops ATM vol by 3% (ie from 20% to 19.4%, not 3 clicks such as 20% to 17%). It’s like a “vol beta”. We can even see -3.0 slope by looking at a 1-year beta between SPY and VXX based on daily samples.

A stylized view of how this works:

The vol path only affects the ATM vol. If the smile remains the same shape along the path, the vols of all the options also change. That’s not all. The skew, measured as normalized skew or the percent premium/discount of OTM strike vs the ATM strike, is also changing if the shape stays the same but ATM changes.

While vol paths describe the ATM vol, there are skew models that describe how all the options for a given expiry will change. Just like the vol path concept, the goal is to make better predictions of how a portfolio of options will react to stock movements without pretending that vols don’t change. This is an opportune moment to remind you that the presence of a smile in the first place is a patch to the faulty Black-Scholes assumption that vol is constant. If the strike vols don’t change as the spot moves, the ATM vol still does since you have “moved along the smile”.

The entire branch of quant devoted to modeling option surfaces stems from the knowledge that vols change as the underlying moves and that there is value in trying to forecast those changes rather than accept a null prediction of “vols won’t change”.

There’s no controversy about whether there is value in modeling the dynamics of option surfaces. Better models improve:

1. Risk measures. VAR needs assumptions about how the surface reprices when spot moves. Your hedge ratios are direct outputs from portfolio scenario shocks and their assumptions.
2. Market-making. Sound models mean the ability to recognize abnormal kinks within a name or cross-sectional divergences between names. A model gives you a baseline from which to judge “how strange is this surface change”? If the skew rips by X, do I expect that to pop back into line or is this within the realm of normal, given the market’s movements?
3. Option pricing on illiquid names. How do I estimate option values in a name with sparse quotes? A good model fills in the blanks.

My goal with this post, like all of my posts, is not to give it an academic treatment but the non-quant practitioner’s perspective. To offer an intuitive angle to either better organize your understanding, whether this is new to you or if you come from a similar vantage point OR complement the textbook rigor that some readers possess.

As the title suggests, we are going to reduce the topic to 2 basic types of skew modeling approaches — “sticky strike” versus “floating”. The fact that there are 2 is a hint that neither is fully “correct”. Just like skew itself is a kluge, the entire domain of surface modeling is basically a kluge. Beyond hard arbitrage boundaries, the relationships of options to one another is a collection of informed guesses mediating a constantly evolving conversation between models and behavior.

The typical George Boxism “all models are wrong, some are useful” applies. Models are toys by necessity — if they were actual simulations of reality then the reality is simple enough to not need a model. Nothing about the future of a security price satisfies that requirement.

Our procedure here is to assert the model, see what they would predict if they were true, and then watch them break by hypothesizing the trading strategy that would profit from the models NOT breaking (which of course is a blueprint for why they must break).

The nice part is that this is mostly a visual exercise so don’t be discouraged by the post being too long to fit in the email…it’s a lot of pictures.

Floating Skew

A floating skew model says that the percent skew by delta* stays constant. The 25-delta put is always, say, 25% above ATM vol.

Note: Floating skew models are also referred to as “sticky delta” keeping consistent nomenclature with their counterpart “sticky strike”. I always found this similarity in names to be confusing but YMMV.

*Delta is a stand-in for any normalized measure of moneyness. 

Log or percent moneyness itself (ie “a 5% OTM put”) is not normalized for volatility. A 5% OTM option on TSLA is a lot “closer” to ATM then 5% OTM on SPY because TSLA is so much more volatile. 

Delta is a vol-aware unit of distance, but it has the problem of being recursive. We need a volatility to measure distance to measure the vol premium on a strike BUT we delta depends on the very volatility we are looking to parameterize. 

You can use standard deviation based on ATM or .50 delta volatility to measure distance as I do here. I admit this might be cope as I’m just drinking the Heisenberg poison I’ve built immunity to. 

The stylized demos in this post are using the base smile from last week’s post from a SPY snapshot.

Spot = $695
ATM vol = 12.4%
DTE = 31

We also maintain the -3.0 vol path (ie a 1% rally drops ATM vol ~3% and vice versa). This model says the percent premium/discount of a strike’s given delta is preserved.

Looks sensible if we plot vols by delta for green (stock up ~1% to $702) and red (stock down ~1% to $688):

Let’s plot vol by strike:

Hmm.

Let’s look closer. Again, the purple curve is the base curve. The green curve represents the smile if SPY jumps from $695 to $702, or ~1%, and the red curve represents the smile with an ATM strike of $688.

I’ll narrate observations, but it’s best to pre-load your own observations if you’re trying to learn (you’re enabling the technique of ‘hypercorrection’ or ‘surprise learning’).

Observations and notes on breaking

• The down move where vol increases due to vol path, actually leaves us with a lower ATM and downside vols! It’s because the vol path itself is not tangent to the slope of the actual skew of the purple line. In this model, unless the vol path is tangent, vol will underperform on the downside while the OTM calls will outperform. As the stock rallies, vols across the board outperform because the vol path is not as steep as the implied skew.
• If there were no vol path at all (ie vol slope = 0) then these under- and outperformances would be even more egregious. In fact, if that’s how surfaces behaved you would simply sell the slightly OTM puts and buy the OTM calls knowing that whenever the spot moved, the IV spread you had on would profit since the vol would always underperform on the way to your short and vice versa. It’s true that you’d still be exposed to changes in realized vol, but you’d have a giant IV tailwind as compensation.
• If the vol path was as steep as the skew, you’d be much closer to a sticky strike model to be discussed below, along with its own caveats, of course.
• A floating model is incoherent in the extremes. If ATM vol doubles/halves, all strike vols must double/half. Leading the witness a bit, but tails are sticky…which means skew flattens when vols get extremely high, and steepens when it gets its extremely cheap. The floor on a .10d put vol is proportionally higher than the realistic floor of an ATM IV. At the extremes, a single penny can be several vol points as prices get sticky (especially since transaction costs are fixed dollar amounts — think of the fee to sell an option at a “cabinet”.)

By asserting the same percent skew premiums/discounts across the curve, the strike vols themselves are left to vary as our chart shows. This view shows how the strike vols change from the base curve depending on whether the stock went up or down:

 

Sticky Strike

A sticky strike model asserts that vols do not change as the spot moves. The $680 put trades at the same vol whether SPY is $695 or $702.

If we fix the strike vol, what happens to skew?

If strike vols are fixed but spot rallies 1%, your 25-delta put is now a 20-delta put. Same vol. Different delta.

This chart is percent skew by call delta for the base curve. For the shape rotators in the audience, go ahead and guess what happens to the skew at the .75 delta when it becomes a higher delta call after a stock rally.

As a wordcel myself, I’m just going to display the answer.

Zooming in on the actual skew changes:

Put skew flattens (ie gets smaller) on sell-offs while call skew gets trashed. On rallies, put skew firms* and call skew flattens (becomes much less discounted).

*A 2% shift in normalized skew is “small”. If skew is 20% premium and ATM vol is 30%, that’s 6 points of premium. A 20% to 22% move in the degree of premium is 0.6 vol points. Matters to a market-maker but it’s noise to most participants.

Picture of SPY 1m .25d skew for the past year:

Zoomed in, you can see how it flattened during the late Feb to late March sell-off and bottoming ahead of Liberation Day before spiking!

I’m not making stories, but pointing out that it collapsed again on the second leg-down, marking the bottom for the remainder of the year. All hindsight stuff, but overall you can see the range for .25d put skew was about 15% for the year(from about 14 to 29% premium to ATM vol).

In case you need a reminder for why I don’t like trading skew for vol reasons:

a sense of proportion around skew

Reality

Sticky strike predicts flat strike vols.

Floating strike predicts unchanged skew, which describes how strike vols change.

Let’s pause for a second. I’ve done something subtle in how I’ve framed this discussion which might be lost on more novice readers (although I’m not sure just how novice anyone who has gotten this far might be).

Without saying so directly, I am putting a lot of emphasis on what happens to strike vols. For traders as opposed to onlookers who just talk about what vol or VIX is doing, strike vols are the closest thing to what matters — option premium. Strike vols influence option prices directly and prices of contracts determine p/l. “Vol went up today” means nothing if strike vols were unchanged and the stock is simply lower. Telling me that ATM vol is higher doesn’t tell me if a floating model just slid down the curve. “Vol” is an abstraction of strike vol is an abstraction of option premium.

With that out of the way, relating these models to reality starts with observation of strike vols. In fact, this is how such models are generated in the first place. Noticing, then fitting.

You could go crazy with examples, but I will do just a couple to give you enough fodder for your own consideration.

This was a SPY snapshot on 1/20/26 with shares down ~2%

Strike vols are up across the board.

Notice:

• Sticky strike wasn’t true. Strike vols moved.
• Floating skew didn’t hold either. If the strike vols were all up in an approximately even fashion in clicks (ie all vols up 1.5 points give or take .3 for near the money) then skew flattened (think of it this way…higher IV options were up a similar amount to lower IV options).
• You could describe the change as a parallel shift in sticky strike vols. A sticky strike type movement means the skew changes. In this case, the put skew flattened and the immediate call skew became less negative.

A 2% move in SPY is 2 standard deviations. I’m not surprised the surface didn’t adhere neatly to a model. Even vol paths are extremely local (a vol path of slope -3.0 would predict that a 2% sell off would lead to a 6% increase in vol and IV on the original ATM went up double that amount from 13% to 14.5%).

Let’s look at IBIT March expiry on Monday’s selloff. IBIT fell ~6%, about a 2 standard deviation move as well.

Here’s the change in strike vols.

In the belly of the curve, sticky strike was a great description of what happened. Strike vols barely budged, while the signature of the strike vol changes for options that are now OTM calls and puts looks like what a down move with a floating strike model would predict. Call vols up and puts vols down. Sticky strike in the belly, floating skew for OTM.

And while the SPY move looks like it rattled the market as the surface shifted higher, the BTC move had little effect on its surface despite both moves being ~ 2 standard deviations. The SPY move seemed unstable, while the BTC move was stable.

What do you do with this?

If all of this sounds confusing, it’s because it is! This is good news for vol traders. If it weren’t, the market would just be more efficient. In this example, BTC vols underperformed SPY for the same exact type of move. Inverting, that means there’s an opportunity for discernment, as you had 2 assets which had highly correlated underlying behavior but mismatched volatility behavior.

A question to consider given the vol moves…if you buy the now at-the-money BTC vols that haven’t budged or even the OTM puts which actually declined in vol to sell upside SPX or BTC calls, is this an opportunity? This is what vol traders think about for a living. You have desks that see the flow in everything and combine that info with the relative strength and weakness across parts of the surface (it’s the whole idea behind the vol scanner tool).

If you’re a market-maker, you don’t have the luxury of just scanning the markets to cherry-pick. You are deeply embedded in the price formation process since you must post a market. In illiquid names, you must do this with limited flow information. Having a vol surface model to generate fair values to quote around is not optional.

In commodity options, I toggled between sticky strike, floating models with vol paths, and hybrids (basically a floating model with a vol path and a skew correlation that allowed you to rotate or tilt the shape of the curve forward and backward).

Just like a vol slope parameter, these models affect your deltas.

I remember a particularly brutal period where vol was so heavily offered on up moves that when I eventually gave in and ran a much steeper negative vol slope, the change flipped my delta from being flat to short $20mm of oil. And of course, if you are long vol as it’s getting pummeled on the rally, that means your model is now saying you are short on the highs. After all, that’s the problem. The market is rallying, your calls are massively underperforming their delta and you are short futures against them!

But this sensitivity means you can’t be toggling your models all the time because how you model affects your risk. The goal is to model reality, but if you keep changing models like your name is DiCaprio, you’re going to put your risk in a blender.

This is a good place for judgment. You build an understanding of how the surface changes for various types of moves while acknowledging that this depends on the market context and open interest. If investors are well-hedged to the downside like they were in 2022 (the market sell-off and rise in interest rates were extremely well telegraphed), then you might expect put skew to underperform on the way down. You certainly don’t want to run a fixed strike model in that case.

You let the market’s surface changes act as a tell. If the market acts differently on a small sell-off and a big-selloff that’s expected. You don’t really gain information. There’s no null to reject. But if both types of sell-offs have muted reactions, that’s interesting. This is an orderly, expected, and perhaps even stabilizing event.

On the other hand, a stock up-vol up surface move is unexpected. That should inform the model you run. There’s an art to this. How long or how persistent should a behavior be before you can classify which model you should run? There’s no simple answer (again, thankfully!). Open interest and expectations are convolutions that direct whether something is a surprise or not. Surfaces react to surprise. Remember they already know that vol is not constant — it’s the delta in expectations about how volatile the volatility itself is that substantiates new surface behavior. Surfaces anticipate a band of random behavior without reacting because randomness is embedded in volatility.

Sometimes interest rates rise because of growth expectations. But stagflation will do that too. The vol surface will likely care about the difference. Oil might be rallying steadily because China is booming and the global economy looks rosy. It can also rally because there is no peace in the Middle East. The vol surfaces will distinguish between the 2 types of rallies. Your deltas will be vastly different for the same nominal options position depending on the backdrop.

I’ll leave you with this summary that captures what I generally, but loosely, expect when I see the stock market up or down and whether I think the move is stabilizing vs destabilizing:

 

Before we get to the heart of today’s education, this is a video follow up to yesterday’s HOOD: A Case Study in “Renting the Straddle”.

I talk about oil volatility as well and how it shows up in the Trade Ideas tool.

This concept of “spot-vol correlation” gets a lot of airtime from different angles even when it’s not explicit. The mass financial media doesn’t use the exact words but they know enough to call the VIX the “fear gauge”. VIX is a complicated formula that aggregates values representing annualized standard deviations from a strip of inverted Black-Scholes numerical searches with quadratic weights. But all of this gets translated to:

“when stock go down, that number go up”

That travels a lot faster. Even your cat knows that market volatility has an inverse relationship with stock returns.

The more your paycheck depends on option greeks, the more you will need to zoom in on this concept. Mostly because the relationship between vol and prices changes your actual risk. The Black-Scholes world assumes vol is constant, but we know better. The sensitivity of options to various market inputs (greeks are measures of risk) is naive without adjusting for behavior that is predictable enough for your cat make a better guess than random about what will happen to vol when stocks move.

How can use this cat knowledge to estimate better deltas so when our model says we are long $50mm worth of SPY, we aren’t suprised when it seems to act like we are only long $40mm worth?

There isn’t a single way to do this but I’m going to show you how I did it as a calculus-challenged orangutan.

Before we get to numbers and pictures, I want to mention one last thing.

There’s a riddle in the world’s best trading book Financial Hacking. An imaginary bank trader calls a meeting with management and says he’s found “greatest trade in the world”. He sits them down for a presentation and says he can buy calls for 20 vol and sell puts at 40 vol, delta hedge until expiry, and make a 20 point “arb”.

What’s the problem?

There are several, perhaps many, option traders reading this right now who have thought about the holy grail of long gamma, collecting theta. Look you can go do this right now.

1. Sell a strangle on 1-month oil futures and buy a ratio’d amount of 12-month CL straddles.
2. Buy a ratio time spread in a name that has a major event coming up
3. Trade SPY risk reversals

All of these trades will give you the “desired greeks”. But these are illusions. In order:

1. The lower vol on the deferred future makes the gamma of those options look higher than it is, but you need to weight the gamma by the lower beta those futures have to spot oil prices
2. The decay you think you collect on the near-dated short is unadjusted for the “shadow theta” or glide path of IV increasing as the upcoming event is a greater proportion of the variance remaining as each second elapses
3. Spot-vol correlation means that theta number is not just the cost of gamma but vanna. The owner of the put is getting more than gamma.

Ok, time for less words and more F9.

I grabbed a SPY IV curve from earlier this week. 31 DTE.

The spot price was $695.27 at the snapshot time but we are just going to keep things simple by ignoring any cost of carry and saying that spot is $695. I just wanted a sensible IV curve for demonstration purposes.

The ATM IV on the $695 strike is 12.40%

Fetching the strike vols and using a vanilla Black-Scholes calculator with 0% cost of carry and 31 DTE, we get this self-explanatory table:

The Naive POV

Those deltas answer the question:

“How much will the cValue change if the stock goes up $1?”

But those deltas don’t know what our cat knows? Vol will fall if the market goes up. It’s not a certainty, but I’m happy to lay you even odds on the proposition if you think it’s random.

If vol falls, the option is going to underperform roughly by the change in vol on the strike * option vega.

Greeks are useful insofar as they describe our actual risk. If my cat-instincts know that the call will underperform if the market goes up, then I probably don’t want to sell quite as many shares against it to be neutral on a naive delta.

Likewise, if I sell those calls and the market falls, the increase in vol will mean I won’t make as much money on my call short as the naive delta predicted.

Notice that whether I buy or sell the call, I am better off having hedged it with less delta than the naive model predicts.

We are just trying to incorporate what the cat already knows to dial in better hedge quantities. We are folding expected vega p/l into delta because the empirical relationship between spot and vol changes is strong enough to bet on it.

We need some parameter, some concept of beta, that describes the strength and sign of the relationship between spot change and vol change. In SPY, the sign is negative because of the inverse relationship. In silver, the sign is positive. It is a “spot up, vol up market”.

An important note. We are speaking in generalities — any market has a general spot-vol signature, but it can flip for periods of time and the strength of the relationship also bounces around. These empirical relationships reflect flows. The supply and demand of options as the spot moves around. God doesn’t assign them. Academics will talk in terms of capital structure and how when a company falls, it’s more levered, and therefore mechanically more volatile, equity is a call option on the highest and best use of the company’s assets, yadda yadda. There’s truth to this, but its not the most useful lens for thinking about option surfaces which are tangible projections of an order book of shares across price and time.

A detour with a purpose

I started in commodity options just before the listing of electronic options markets. When I first stepped into the trading ring, many market-makers were still using paper sheets. We had spreadsheets on a tablet computer, but heard of a fledgling software called Whentech. Its founder, Dave Wender, was an options trader who saw the opportunity. I demo’d the product, and despite it being a glorified spreadsheet, it centralized a lot of busy work. It had an extensive library of option models and it was integrated with the exchange’s security master so its “sheets” were customized to the asset you wanted to trade.

I started using it right away. Since it was a small company, I was able to have lots of access to Dave with whom I’ve remained friends. I even helped with some of their calculations (weighted gamma was my most important contribution). I was a customer up until I left full-time trading. [Dave sold the company to the ICE in the early 2010s. It’s been called ICE Option Analytics or IOA for over a decade.]

The product evolved closely with the markets themselves. Its nomenclature even became the lingua franca of the floor. Everyone would refer to the daily implied move as a “breakeven” or the amount you needed the futures to move to breakeven on your gamma (most market-makers were long gamma). Breakeven was a field in the option model. Ari Pine’s twitter name is a callback to those days. Commodity traders didn’t even speak in terms of vols. They spoke of breakevens expanding and contracting.

What does this history have to do with a spot-vol correlation parameter?

This period of time, mid-aughts, was special in the oil markets. It was the decade of China’s hypergrowth. The commodity super-cycle. Exxon becoming the largest company in the world. (Today, energy’s share of the SPY is a tiny fraction of what it was 20 years ago.)

Oil options were booming along with open interest in “paper barrels” as Goldman carried on about commodities as an asset class. But what comes with financialization and passive investing?

Option selling. Especially calls.

Absent any political turmoil, resting call offers piled on the order books, vol coming in on every uptick as the futures climbed higher throughout the decade.

A little option theory goes a long way. Holding time and vol constant, what determines the price of an ATM straddle?

The underlying price itself: S

straddle = .8 * S *σ√T

If the market rallies 1%, you expect the straddle price at the new ATM strike to be 1% higher than the ATM straddle when the futures were lower. Since the “breakeven” is just the straddle / 16, you expect the breakeven to also expand by 1%.

But that’s not what was happening.

The breakevens would stay roughly the same as the market moved up and down.

If the breakevens stay the same, that means if the futures go up 1%, then the vol must be falling by 1% (ie 30 vol falling to 29.7 vol)

It dawned us. Our deltas are wrong.

If we are long vol, we need to be net long delta to actually be flat.

When your risk manager says why are you long delta and you explain “I need to lean long” to actually be flat, you can imagine the next question:

“Ok then, how many futures do you need to be extra long for this fudge factor?”

We need to bake this directly into the model because it’s getting hard to keep track of. Every asset and even every expiry within each asset seems to have different sensitivities between vol and spot. The risk report can’t be covered in asterisks detailing thumb-in-the-air trader leans.

Whentech listened.

Vol paths

Whentech introduced a new skew model that allowed traders to specify a slope parameter that dictated the path of ATM IV. Their approach was simple and numerical. It was some version of this:

ATM Vol path = ATM IV × (100% + vol slope × moneyness)

Let’s say I set my vol slope parameter to -1.0

SPY ATM vol is 12.4%

If SPY goes up 1%, what’s the new ATM IV?

New ATM Vol = 12.4% x (1+ -1*1%)
New ATM Vol = 12.4% x (99%)
New ATM Vol = 12.28%

A -1.0 vol slope corresponds to a “constant breakeven” regime. If the stock is up 1%, vol falls 1%.

This is a table of vol paths for different vol slope parameters:

Keep in mind that the vol path is only for ATM vol. You can think of the ATM region of a smile sliding up and down a ramp of slope -1.0, -3.0, and so forth.

💡Notice that all of these ATM vol paths suggest a lower vol ATM vol at say the $675 strike than the actual smile implies. That is really a separate discussion, since skew is not really a “predictor” of vol anymore than a back-month future is a predictor of spot price. It is just a value that clears the market so it has risk-premiums embedded. It’s just another example of “real-world probabilities do not equal risk-neutral probabilities”. Even if that’s not satisfying, you could think of the skew as needing to average any number of price paths approaching a strike. If we drop $40 overnight, ATM IV is going to be higher than what the current $40 OTM put vol. If it takes 2 weeks, maybe not.

SPY skew is quite steep compared to most assets. A vol path that is tangent to the skew curve (-9.0 parameter) would be a very aggressive spot-vol correlation, especially considering that -1.0 is constant breakeven. Anything more negative means, as you rally, the value of an ATM straddle shrinks. That’s a strong clue that this slope idea is highly localized. If SPY doubles, the new ATM straddle isn’t going to be worth less than the current one, nevermind 0.

Zooming in on the strikes that are $5 around the ATM $695 strike:

How vol paths affect your delta

Once we’ve chosen a vol slope, we can compute the vol path, which in turn alters our model deltas. We can do this numerically, instead of deriving new formulas for greeks.

We are going to make a simplification, which is to assume that for a small spot move, changes in vol affect all the strikes by the same proportion. You are invited to think of what that would mean for implied skew. I plan to tackle that in a later article, but we’re building up in steps.

Let’s zoom in on the 695 call in the case when SPY goes up $1.

In the naive model, the 695 call goes up by its delta or $.507

But based on the different vol slopes, we know IV is going to fall from 12.4% to anything from 12.38% (-1.0 slope) to 12.24% (-9.0 slope). When we reprice the option with the lower vol, we see our profit is less than $.507. The difference, which is mechanically due to negative vega p/l, is being used to convey an “effective delta”.

If the market behaves as if the vol slope is -5.0, then instead of hedging the ATM call on a .507 delta, you should have used .44 delta.

[This is the topic I’m talking about at minute 37 in the context of estimating dealer hedging flows]

I show the vega p/l just to make the decomposition tie out between the recomputing of the option vs what it’s worth if IV was unchanged.

Vol beta

We’ll close by tying this dynamic back to hedge ratios in “delta one” vol products like VIX futures and ETPs.

VIX depends on a strip of options, not just ATM. But let’s stick with our simplification that IV changes proportionally across strikes such that if ATM vol decreases 10%, VIX falls 10% (not 10 percentage points but 10%…like 20 vol going to 18).

This is our IV projections according to different vol slopes for SPY shares up 1%:

The vol slope parameter can be thought of as a vol beta. As in, what’s the beta of VXX shares to SPY?

[ I wrote about this last year during Liberation Day because on the sell-off, I bought both ES futures and VX futures but I needed to estimate the right ratio to buy them in.]

Running the regression for the past year in moontower.ai shows a VXX/SPY beta of -3.25:

The rolling one-month beta is more volatile and would correspond to vol slopes between -1.5 to -5

Related video:

📺VXX Beta explained via Moontower Hedge Ratio Tool

I bought June/Feb13 put calendar in SLV a few weeks ago when the vol spread inversion went nuclear.

That was a disaster.

SLV dumped 30% 2 days later.

The Feb puts I’m short are of course 100 delta, so the effective position is long a June OTM call synthetically.

💡If a stock is $80 and you own the 100 put for $25 and 100 deltas worth of the stock, then you are synthetically long the 100 call for $5. If you don’t believe me, look at your p/l payoff for the portfolio of long puts and stock at expiry for stock prices of $90, $103, and $120 vs what it would be if you just owned the 100 call.

We understand the position and the risk. But we don’t talk about taxes much here so I’ll use this example to introduce the complexity of the real-world.

Let’s say I roll my June puts.

Consider the tax implications.

I will realize a gain on the appreciated puts.

The puts I’m short that are now the risk equivalent of being long shares because they are so far ITM. I have a mark-to-market loss on these puts, but it’s not realized. This is a problem. The entire trade has been a loser, but if I roll my June put,s I crystallize a short-term tax gain. Ideally, I need to crystallize the short-term loss on the puts I’m short by buying them back.

If I don’t buy them back and get assigned, I don’t realize the loss. Instead, I acquire shares with a basis of the strike price minus the premium I collected when I sold them. If I sold the 100 put at $5, my cost basis is $95. The shares are $70, but my loss is still unrealized until I sell the shares.

The problem might not be immediately obvious, so let me break it down.

• If I roll my June puts instead of closing the entire position out, I have a trade that has been a loser, but the tax accounting shows a short-term gain + an unrealized loss.
• To crystallize the loss, I must buy my put back or sell the shares once I’m assigned. But, both of these trades sell lots of SLV delta. If my intention is to maintain a synthetic long call position (long stock + long ITM puts) I’m stuck with an accounting gain.

⛔Because of the wash sale rule I cannot sell my SLV shares then immediately buy them back.

• You can envision a scenario where SLV rallies up again, my synthetic call position recovers the economic loss but I have a taxable gain on the rally. My p/l on all the activity is a wash BUT I have loads of short-term taxable income!

Not picking up your matched short-term loss is leaving a dead soldier behind.

(Ok, that was dramatic. I’m sorry enough to say so, but not enough to delete it. I want to imprint it.)

There are a few choices whereby you can roll the puts, achieve the desired risk exposure but I’m not an accountant and this is not advice. There’s no wink here. Talk to an accountant.

Goal: crystallize short-term loss without getting rid of your long silver delta

Possible solutions

1. Once you are assigned, sell your SLV shares and replace the long with a highly correlated silver proxy such as other ETFs or silver futures. From an IRS interpretation of the wash sale rule, the futures are probably safer since COMEX is NY silver and SLV is London deliverable. But again, not an accountant.
2. Replace your length with assets highly correlated to silver, like miner stocks. The basis risk is obvious.
3. Close your puts and buy the stock at the same time, effectively buying a worthless synthetic call.

Let’s talk about #3 a bit more.

If the stock is $70 and the 100 put is only worth intrinsic (ie there’s no time value left in the 100 call), then that package is worth $100. The stock price plus the $30 put. Now you wouldn’t expect a market-maker to fill you at fair value.

I figured a market-maker might fill me for a penny of edge. When I was looking at the quote montage, the 99 strike call was offered at a penny so by arbitrage the 100 call should be offered at $.01

I tried to pay $100.01 for the package.

No dice. Nobody wanted the free money. I didn’t raise my bid, figuring I would try again on expiration day since perhaps a seller didn’t want to bother with the inventory. If they traded it on expiration day, the whole position would offset at settlement, and they would collect their easy penny.

Well, what happened?

My short put got exercised early! I got stuck with the shares and now have to sell the shares to crystallize the loss.

The interesting thing to point out is that paying up a penny to lock in a short-term accounting loss is a type of trade that’s win-win. The market maker sells a worthless synthetic option, I get my tax situation aligned.

This is a screenshare constructing a synthetic call in IB’s strategy builder, then adding it to the quote panel so you can see the bid/ask for the structure.

In the spirit of spaced repetition, I published The Gamma of Levered ETFs as an article on X. Seemed relevant given silver’s 30% selloff on Friday.

Here’s the short version of the math of levered ETFs. To maintain the mandated exposure the amount of $$ worth of reference asset they need to trade at the close of the business day is

x(x - 1) * percent change in the reference asset * prior day AUM

where x = leverage factor

examples of x:
x=2 double long 
x=-1 inverse ETF
x= 3 triple long
x= -2 double inverse

Applying this to silver:

AGQ, the ProShares Ultra Silver ETF, is 2x long. It had ~$4.5B in assets at the close on Thursday.

For the underlying swap to maintain the mandated exposure, at the close of Friday (assuming no redemptions) the swap provider must trade silver. How much of it?

2(2-1) * -30% * $4.5B

or -60% of $4.5B.

-$2.7B worth of silver in forced flows. Negative = sell.

There’s an UltraShort 2x ETF, ZSL, that had about $300mm of AUM going into Friday.

Rebalance trade:

-2(-2-1) * -30% * $300mm = –$540mm

Assuming no redemptions, these levered ETFs needed to sell ~$3.25B worth of silver into the close.

In a typical environment, silver volumes are mostly split between London’s spot market (LBMA) and COMEX futures (NY deliverable) with Shanghai (SHFE), India (MCX) and SLV (London deliverable, US traded ETF) combining for less than 10% of total volumes.

At the NY close, SLV and COMEX represent all the liquidity that’s open.

COMEX futures traded nearly $150B of volume Friday and SLV traded ~$50B which is on the order of 10x the dollar volumes silver used to trade a year ago at lower prices. Still, those forced sales, if they are happening in the few hours of trading may represent something like 5-10% of the liquidity.

I’m guessing readers who are actually on metals desks have a better guess.

Silver futures margins, after being raised again this week, are about 15% of the contract value (although your broker may ask for more. IB asks for twice that, which was prescient!)

If Shanghai futures, which were closed, have a similar requirement, that means the exchange doesn’t have enough collateral to cover the 30% move if Shanghai futures match the COMEX move.

I don’t know how that exchange works (many exchanges have an insurance pool where some of the losses are socialized across clearing members), but one thing that would be interesting is if Shanghai exchange officials have the authority, balance sheet, and ability to have sold COMEX futures as a hedge. I doubt that, it’s just a speculative musing, but if such a thing did happen, their Sunday evening unwind trade would be to buy back COMEX futures as they liquidated Shanghai holders. Again, this is just a ridiculous musing, but I look forward to seeing how it all shakes out.

In any case, I think a useful takeaway from all this could be to add expected levered rebalancing flows to your dashboards (of course, this is a recursive problem because the price at any point in time reflects some people’s knowledge of these flows. Pre-positioning always opens the door to backfiring if enough arbs think the same way).