two vol trader interview questions

Thursday’s post the dirties are down the cleans are up took the form of an extended interview question. If you are high-volume professional vol trader, the topic of vol time is fundamental but I don’t see much written about it. I hope my posts on it fill the gap.

For non-pros it probably best serves as a bicycle for the mind or a seed of inspiration but I wouldn’t stress over it. I suspect it does since tweets like this are popular even though I’m pretty sure the engagement on them isn’t coming from a bunch of practitioners in the middle of the trading day:

Most IVs you encounter use a 365-day model. To convert to a 251-day model (or any other tenor model) you multiply by the square root of the DTE ratio.

https://x.com/KrisAbdelmessih/thread/1921978763140628749

In the spirit of Thursday’s post and the tweet, I’ll pose 2 “interview-style” questions that can be answered in seconds. They require making a reasonable assumption. I’ll give the questions here, then I’ll post an assumption as a hint after the questions for those who need help forming one. The answers are at the end of the post. (Ignore cost of carry — also if you asked about that you’re way ahead of the game).

You do not need any calculators to answer these (just mental arithmetic).

❓#1: Volatility

It’s the close on Wed. Options expiring next Tuesday and next Friday have the same dirty vol (ie the same vol in your off-the-shelf 365d model). Does one of them have a higher clean vol? Explain. List any assumptions.

❓#2: Price of a straddle

It’s Friday close. Next Friday’s ATM straddle is $5. What price is the Friday ATM straddle expiring in 2 weeks to be the same clean vol? You do not need option calculator.

An assumption you could use to help:

We’ll use a calendar specification that states non-business days count as 50% of a business day for vacation time purposes.

If we continue to denominate our basic unit, a full trading day, as 1.0 and weekend days or holidays as .5 we get the following tenor:

251 x 1.0 + 114 * .5 = 308 day calendar.


Answers to the “interview” questions

#1: Tuesday has a higher clean vol than Friday.

Relative to a “dirty” year where each day is treated as equal vol, a “clean” year in which variances passes more slowly over a weekend, the Tuesday expiry has less time to expiry than Friday’s ratio of clean to dirty DTE.

If the model implies the same dirty vol for Tuesday and Friday, we can infer Tuesday must have the higher clean vol bc it has relatively less vol time vis a vis a dirty model.

#2: The second Friday straddle must be $7.07

The approximation for an ATF straddle is .8*S*σ*√t

Since there is no cost of carry we can assume ATF straddle = ATM straddle which is what the question asks about.

We don’t know S or σ but the question asserts the same dirty vol for both Fridays.

We know there’s twice as much time to expiry for the 2nd Friday and we know the earlier Friday straddle is $5 so the second Friday straddle can be computed as

$5 * √2 since there’s 2x as much time til expiry.

So the second Friday straddle = $7.07

💡This is one of those useful trader math ideas — for a given vol the price of the straddles only varies by square root of the ratio of DTE. One of those mental arithmetic things I found myself using constantly especially with short-dated options where you’re like “if the 1-week straddle is X, the 2 week is…”. This is also a clue to the degree to which I ditch the idea of “volatility” altogether in near-dated options and “think in straddles” and move sizes. This intuition is habitual but you can also see why it has theoretical support — vega p/l is a less of an influence on short-dated options. Results mostly come down to “how much did this thing move vs how it was priced”.

Returning to the question.

The $7.07 straddle is based on the same dirty vol.

But does that translate to the same clean vol the same as the first Friday straddle?

Yes — the ratio of dirty to clean DTE is the same for both expiries!

how I sold cotton at an all-time high

In the post If they ban short-selling derivatives become the underlying, I concluded with

If futures are trading at full carry and you think such a ban is possible it’s an asymmetric bet to put on conversions (ie short futures, long stock or for options short combos long stock).

Broadly speaking, the derivatives market becomes the underlying market. When the underlying market becomes encumbered do to technical frictions, derivatives are no longer just “derivatives”. The arbitrage mechanism is severed. The derivatives market becomes the home for price discovery.

I’ve seen this happen throughout my career. Just a few examples in addition to hard or impossible to borrow/short situations:

  1. HYG becoming a liquid referendum on illiquid high-yield bond market
  2. Cotton option synthetics continuing to trade even when the underlying futures are locked limit up (my trading claim to fame is selling the all-time nominal high in cotton in late 2011…it was an option synthetic around $2.25)
  3. When a stock is halted, the premium or discount to any ETFs holding that stock computed using the halted stock’s “last” price before the halt implies the price the stock will open when it resumes trading. These situations were common during the dot-com heyday when stocks would be halted intra-day on pending news announcements.

Today, we are going to cover #2. Along the way I expect several bonus “oh that’s how that works” moments. And call my shot…you will enjoy this one.

When cotton options become the underlying

Cotton futures have peculiar price limit rules. The daily price limit depends on future’s price on the prior trading day.

If a cotton future is 70 cents, its daily price limit is 3 cents. If 2 or more of the first 5 delivery months close limit bid, then the limit is expanded by 1 penny. And this effect can chain every day with the limit capping at 7 cents regardless of the futures price.

Pricing a cotton straddle that cannot necessarily be delta-hedged (ie replicated) is good, clean fun. But it’s not the subject for today.

[Aside: When I traded cotton the rules were meaningfully different but I’m a bit foggy on them and can’t find the exact old rules online. The gist of it was there was a circuit breaker once you hit the limit that halted the market for 10 minutes perhaps. When the market re-opened the market was allowed an additional limit. If it hit that, you were double limit up. If 2 contracts went double limit up the market closed for the day. I once went to work for 20 minutes. The penny expansion rules were the same.]

Important background context

1️⃣I used ChatGPT to refresh me on the cotton planting cycle.

How does this affect futures trading?

  • The old crop contracts have delivery months before August. Notably: March, May, July.
  • The new crop contract , December reflects the next year’s harvest.

A squeeze in 2011 old crop acutely affected Dec 2010 thru July 2011.

2️⃣ In general, cotton futures, like most commodity futures are correlated to each other. In addition, nearer-dated futures are more volatile than further-dated futures. Even if you never traded commodity futures you’ve seen this in VIX futures. The front future can get to 70, the 6-month future has never done that.

The academic name for this “futures get more volatile as expiry approaches” is the Samuelson effect. The post Seasonal Volatility goes hard on this stuff if you care. But the point I want to get to is that cotton futures have a diminishing beta as you go out in time relative to the front month mostly because the volatility ratio drops off. Recall that beta is volatility ratio * correlation. However, when you cross a crop year, the correlation can also significantly drop off. In fact it can invert!

This is logical. If there is a shortage of cotton this year, farmers are incentivized to plant more acres next year increasing supply. Today’s bull market literally sows the seeds of tomorrow’s bear market.

This leads to interesting behaviors in future spreads. For example, if month 1 (M1) goes up 10 cents in a week, M2 might go up 6 cents. The spread expanded by 4 cents over the week. Generally speaking, spreads themselves are positively correlated with M1. (You can think of them as having their own delta to M1).

But when you get into these squeeze cases, the spreads can really blow out as their delta becomes dominated by the volatility of M1. If M1 is $1.00 and M2 is $.80 the spread is 20 cents. If M1 is squeezed up 25 cents in a week because there’s a shortage of deliverable supply into that expiry for logistical reasons it’s possible M2 barely moves. Suppose it doesn’t. The spread therefore has blown out from 20 cents to 45 cents, which is so wide it is more than half the price of M2 itself!

If you are long an option time spread you just got carried out. You are long vol on something that didn’t move and short the thing that roofed and because it can only go 25 cents in a week by hitting limits you just got gapped.

[When I joined Parallax, I worked with the CRO to come up with all kinds of limits for not just vol exposures at the ticker level but gross exposures in individual months because we understood that the problem in commodities is not just correlations that go to 1, but -1!]

3️⃣Here’s a crazy wrinkle — when the futures are limit up, the options still trade! The futures pit would be dead as the futures brokers would run over to the option pit to quote synthetics from the option market-makers.

The option market-makers were effectively making markets in the underlying. As you can imagine, liquidity disappeared as the adverse selection problem dominates. Markets that are 2% wide with 5 lots on the bid and offer.

But this is where I thought it would be fun to point out that there was a way to price that limit-up front month future.

Suppose M1 is March 2025 and it’s $1.30 limit bid and you need to make a market.

You can imply its fair value with 2 pieces of information.

  1. Where a non-limit future is trading, say Dec 2025. Let’s say it’s market is $1.12-$1.13
  2. Where the March/Dec futures spread is trading. Let’s say it’s $.20-$.21 (remember in commodities spreads are quoted as near month minus deferred month, the opposite of how equity roll conventions).

So “on legs” the March future is $1.32 bid, offered at $1.34.

If you offered the March future synthetically at $1.35 and got lifted you could buy the Dec future at $1.13 AND the Mar/Dec spread for $.21.

You outlaid $1.34, the Dec future cancels with the Dec leg of the spread and you are left long 1 March futures which you synthetically sold in the options market at $1.35.

The series of transactions has left you with a conversion on your books — short call, long put on the same strike which assures you sell a future at $1.35 at expiry that will wash with the future you bought at $1.34, netting you a 1 cent profit.

So when a future went limit up, you need to know where all the non-limit futures were trading as well as the matrix of spreads from those futures back to the ones that were limit to triangulate the tightest market in the limit future. You could quote synthetics around that value.

This is a screenshot of the actual spreadsheet I had on a tablet computer that I held in the pit:

If you can zoom in you’ll see the RTD links to my X-Trader software, ie TT

as well as the “beep” alert which was kinda hard to hear in the pit especially because I had a headset on to talk to our upstairs trader.

Oh, there’s another little Easter egg…see BAL? That was the cotton ETF.

I’ve talked a lot about how I used to trade USO vs CL and UNG vs NG but I also traded BAL vs cotton futures…this one was neat because there were only a few people in the markets who knew in real-time where the locked limit cotton future was trading AND also jumped thru the hoops to be able to trade SEC products like ETFs. But as I explained in Battle Scars As A Call Option — The UNG Experience I had set all that up when I was at Prime so we were uniquely capable of this.

Zoom in a bit more and you can see that we were also looking at JO (coffee ETF) vs KC (arabica futures).

So going back to this post’s baity title…I sold cotton futures synthetically at ~$2.25 which is a higher price than it ever settled IIRC. In fact, I don’t think the futures ever traded that high, it only happened via options. It wasn’t some genius move — I was hedging gamma at levels that seemed reasonable based on where the futures were on legs.

Hedging gamma?

I might as well explain how I traded cotton. I went to the cotton pits specifically because the risk manager at Prime mentioned that there was a crazy squeeze happening in the cotton markets. Apparently, a large grower with cotton in the ground, who did the sensible thing — hedge the crop with short futures — was caught in a liquidity crunch. As the futures rallied in late 2010 based on the short supply, the large hedges were marking against the grower.

This was in the wake of the GFC so credit was tight. The grower couldn’t borrow to fund the margin despite having the crops. Classic example of the market smelling blood and “running in” a large exposed party.

I went to the pit to participate in these reindeer games. The option market was wild. Vol roofed. Call skew was jacked to record highs. Limit up every day.

So what did I do?

My trading partner will give me tons of credit for basically doing the thing many of the other options traders wouldn’t…I bought as many calls as my risk could let me. High vol, big riskies to the call…gobble gobble. But I did it because it was only positional trade that made sense (you could make some money market-making every day but this seemed like a chance to score on a position trade).

My reasoning:

There was no sensible path where we just grind higher. This thing gets to the uncle point as sloppy as the limits allow then crashes. We don’t know where the uncle point is but it’s up from here. By buying expensive calls with big fat deltas, I could sell lots of futures. Hard deltas. Those option deltas are soft…they will melt away as vol declines and time passes but the position I want is to be directionally short but have my upside protected.

And sure enough it played out pretty much that way, straight line up, straight line down (with the limits as speed bumps all along the way).

We played until maybe March expiry. The episode was over and I had no intention of being a cotton trader for the long haul.

Luckily, coffee was just getting busy and its pit was about 30 feet away. So I sauntered over there…for the next year.

Story time

By late 2011 I was talking to Parallax and once it was clear I was moving to SF, I started “closing only” trades with Prime. Winding down an option book is expensive. You can work out of your positions but it takes months depending on how far out your longest-dated inventory resides. Since you aren’t adding any trades with edge, it’s basically just hedging and losing money. The other choice you have is to pay another trader say a couple cents a contract to take over your book. Either way there’s an exit tax.

Once my book got very small, I was just playing Gears of War until 4am with my west coast friends every night, waking up at noon, and dabbling on my first blog, shoxland, named after my NYMEX badge — SHOX. There’s still a lot of people out there that only know me by that name and even many who know my real name who still call me that. (My XBOX live gamertag is “shox da monkey” a nickname a trader named Keith gave me in the office.)

Anyway, all the pit-hopping experience made me familiar enough with these products that when they got interesting they became part of my options book at Parallax. When they were dull, I ignored them.

Ok, ok thanks for indulging story time. It’s legit nice to write this down. For some readers out there this will be a trip down memory lane.

shortcuts to get implied vol from a straddle

I just want to say that you people are sick. This is the most viral tweet I can remember sending in recent times.

Because I happened to be helping the 3rd grader with improper fractions recently I saw 1.25 as 5/4 which is immediately recognizable as a square root of 25/16.

Sprinkle in some trader math that condemns you to see the sqrt(251) as 16 and you get an even more compact version:

Step by step:

The real masochism in that thread happens further below…

what the vol spread chart hides

People will message me with charts like this to ask my opinion of a pairs trade:

This is not a pair anyone sent me but the form is the same.

I guess-and-tested a few pairs whose daily return correlation is at least as high as some pairs I’ve been sent. In this case, I picked 2 homebuilders whose correlation over the last 500 calendar days turned out to be .90

[I used the hedge ratio tool from last Thursday to find this pair]

I respond to these messages with a few things that come to mind but I’ll flesh the responses out more in this post. Think of it as a map of data and interpretation pitfalls. There are bits in here for both novices and pros that’ll stimulate a “I never thought of that before”.

The charts above aren’t worthless but they’re very faint beeps on a metal detector during a beach stroll.

Let’s see why…

 

Thoughts as they come to me when I look at the chart…

👁️The first thing my eyeballs say to my brain when looking at a vol spread chart is “why not a ratio chart?”. Over the period, the mean vol spread of LEN to DHI is +.83 and the vol level is about 40%

If vols doubled and the vol spread had the same average I’d be surprised, so a vol ratio strips out the level-dependant assumption that is silently suggested in a spread chart.

The nature of the relationship is of practical importance because it affects how you weight a pair. Any weighting scheme has trade-offs and matching the trade-offs to the bet you are expressing is, well, advisable.

I won’t rehash weighting here but both of these links go into it:

In this case, the spread and ratio seem good enough proxies for one another. A scatterplot rather than a time series would provide a more granular look but given the 30d maturity and no earnings extractions the extra granularity would probably cause us to overemphasize extremes that would probably be smoothed by arduous surface cleaning.

🔁Possible spot/vol artifacts

If LEN price drops relative to DHI I expect its vol will expand more than DHI. To the extent that a widening vol spread simply reflects recent price action we may deem the vol aberration “more justified” or “not an opportunity”.

Whether a widening vol spread being explained by a sell-off is meaningful or not is unavoidably context-dependent. If LEN falls relatively after earnings, I’d expect its vol spread to narrow from a wider reading that we likely sampled before earnings. But this is just an example of “news flash: lazy low-resolution charts are too underpowered for a trading thesis”.

That said, I do believe the idea of spot/vol mattering in terms of how much weight we’d give an aberration even qualitatively is at least relevant enough to pivot data in a few ways to understand the shape (or lack thereof) of a relationship.

Let’s play “charts plus thoughts”:

  • We see a hint of vol/spot effects in the downward-sloping trendline. As LEN price increases relative to DHI its vol premium falls. But the r-square is trash. Weak relationship.
  • This isn’t saying that spot-vol relationship in the stocks vs their vols is weak just that the vol ratio to price ratio relationship is weak. Remember these stocks’ returns are .90 corr. When LEN goes down, it’s vol expands but DHI usually goes down when LEN goes down (and its vol will likely be expanding as well in that case).
  • My background is in commodities where the spot/vol correlations in general are higher than they are in single stocks (but not as strong as equity indices which I’ll address a bit more below). Here’s just LEN IV vs spot

    The grey trend line shows a weak relationship between spot and vol in terms of slope and a correlation that would be around .55-.60…but then I drew those red dashed lines because the story is really:

    LEN is been like a 30% vol stock give or take 5 clicks in a price range of about $165 give or take 30% (ie an annual st dev). It sold off recently with the rest of the world and vol went up to 40% or about 1/3, as the stock stepped from one economic platform down to another.

    As a matter of generalizable perspective:

    All of this feels interesting from the altitude of an investor’s vantage point, but beneath the level of signal from a trading pov. The distinction is useful for any type of money decision — am I getting trader or investor resolution? Is the option trade I’m doing matched to a trader’s dashboard or an investor’s?

     

The vol ratio vs price ratio chart showed a weak relationship. Not unexpected. LEN vols and price ratios are correlated. So instead let’s scatterplot the 1-month change in vol spread (our first chart said it’s a reasonable stand-in for ratio) vs the 1-month relative performance of LEN vs DHI directly. This is not about levels but about motion.

Honey badger, I mean the vol spread, don’t care. For a pair of 30% IV names that are 90 corr, 5%-10% relative performance is significant (can you use the risk-remaining lesson in last Thursday’s post to show that? The learning sticks if you apply it.)

And yet the scatter doesn’t validate the IV spread caring that much.

Let’s do a final chart before chatting.

We saw above that there’s a faint vol ratio to spot ratio relationship in the pair. As LEN goes up relative to DHI its vol ratio declines a touch but there’s a useless amount of noise from our distance.

Straddles are another way to look at vols and spot prices in a combined way. The approximation for a straddle price depends on vol, spot price, and DTE. DTE is constant. If spot and vol were negatively correlated with a -1 beta you’d expect straddle prices to stay constant over many price levels. If vol has a less than a -1 beta to spot then as price increases straddles increase and vice versa. Over large moves I always expect this to be the case.

For smaller moves, the constant straddle might hold as it often does in something like crude oil, a market where traders have a history of speaking in terms of straddles (or “breakevens” in case you ever wondered about Ari’s twitter name) instead of vol. In that market, discussing vol without price level is like owning one shoe.

For single stocks, this effect is going to be less pronounced. I expect straddle prices to rise with price as the spot effect in the straddle formula overwhelms the declining vol. Voila…the LEN – DHI straddle spread vs price ratio. Upward sloping af.

[Additional thought on this chart:

It likes like there are 2 channels which lovingly accommodate the trendline’s horny advance. I didn’t bother, but when you see things like that, it’s usually a clue to color code your dots by date. I don’t mean to abuse the metaphor but there are probably a couple periods of note here.]


🚧Detour on spot-vol correlation in single stocks

The spot-vol correlation in single stocks will vary by sector. The correlation looks fairly small in these homebuilders. We saw that directly in the spot/vol charts, the change in vol ratio vs change in spot ratio, the change in the vol spread vs change in relative performance, and in the strong positive relationship between straddle spread and price ratio suggesting straddles don’t stay constant but are driven by price more than vol.

One reason I could imagine homebuilders in particular having a muted spot vol relationship is they benefit demand side from lower mortgage rates. I’ll put my finger in the air and say they act like a levered 60/40 portfolio. They are diversified if stocks and bonds are anti-correlated.

[In fact, one way to potentially think of their vols is possibly from the quanto lens that one might think of pricing EWJ vol by considering the individual vols of Nikkei vs Yen. The options on EWJ relative to the legs imply a correlation between Japanese equities and FX — which is substantial in an export-led economy.]

Even though spot/vol corrs vary by sector, overall the skew and spot/vol correlations will be smaller than equity indices. Consider 2 reasons that derive from logic not vol surface data which only reflect the logic:

✔️Stock returns are positively skewed

Most companies go bankrupt. A few drive the total return of the indices. Black-scholes’ positive skew lognormal distribution is not a horrible description of companies…pump up the vol and it says, the median return is negative but there’s a long right tail. See Is There Actually An Equity Premium Puzzle?

✔️Average single stock skew must be less than index skew

An equity index is a portfolio of stocks. The vol of a portfolio depends on the vol is positively related to the vol of its constituents and their cross-correlation. If the correlation falls, all else equal, the portfolio vol falls.

Index downside skew must account for both pathways of vols increasing:

  1. The average stock in the basket’s vol increasing
  2. The cross-correlation of the stocks in the basket increasing

Most stocks share a systemic, undiversifiable risk — the possibility of the economy as a whole slowing down. This shared risk gets priced in index skew as an extra kicker over single stock skew because as correlation goes up, index vol increases faster than the increase in single stock vols.

Recast as a trading statement:

If you could sell all the single stock skew at the same level you could buy the index skew you’d be getting “correlation going to 1” for free.


 

What the vol spread chart hides

In traditional investing, we look at charts that prices travelling from the lower left to the upper right. We can’t feel the drawdowns along the green path it draws. Our eyes focus on the endpoints. Looks easy. All the suffering that you are getting paid for is eradicated, nothing but an expectation of profit for showing up remains.

This turns out to be (has been?) a salutory illusion. The chart makes it easy to buy while hiding the source of edge — a time horizon to not care about the dips. The time series of a mean-reverting vol spread does the same thing.

Let’s break down its lies.

🔮Snooping ahead

The mean and standard deviation are known for the sample period only after the sample period is over. If we transport ourselves to a place on the graph where it’s “obvious” to sell we are doing so with the benefit of hindsight. At the time of the trade, we didn’t know we were “there” on the finished chart. This insidious self-deception happens easily when we look at charts, don’t bring it with you when you move towards actually trading.

🧩Implied vol is only part of the puzzle.

The actual p/l of an option trade done to capture a vol discrepancy is a combination of:

  1. vega p/l or change in implied
  2. your realized p/l or what I call the “tug of war between your gamma and theta”. For a 1-month option the tug-of-war is going to have a large influence on your results but the chart’s thesis was IV driven.

     

If the market were strong-form efficient all the money you’d make on mean reversion of the IV spread would be lost on your realized vol p/l if the IV spreads perfectly predicted subsequent stock movements! I’m not saying that they do, I am saying that any focus purely on the IV spread only studying half the p/l drivers.

Discrepancies result from flow. Updating fair value based on flow is an inexact science, but to assume that the full amount of discrepancy is edge is the same as saying the flow has zero information.

🍦Vanillas are the real exotics

Vanilla options are messy. How?

  1. This time series is a constant maturity vol. To replicate it, you’d need to rebalance the options of the 2 expires bracketing 1-month on each trading day.
  2. As the spot prices of the pair move around, your moneyness and greeks change knocking your initial weighting off balance. Contrast this with a variance swap where holding time constant, your gamma is constant over the range of strike prices. The pricing is exotic but the experience is familiar because it ties mechanically back to a familiar calculation — the standard deviation of close-to-close returns. Vanilla pricing is familiar but the experience is exotic.
  3. You were trying to capture a vol discrepancy which means you probably want to delta-hedge to isolate the edge.

     

🩺Clinical vs practical

Maintaining a desired exposure in vanilla options requires rebalancing both the options and deltas. To do this faithfully every day as a taker would cost more than any perceived edge is paying.

This means in practice you must be willing to warehouse and tolerate noise. This means you must trade smaller for a given amount of edge if you want to maintain the same risk/reward of the clinically hedged expression.

In the real world, this type of trading is the domain of entities that can sell on the offer and buy on the bid, allowing them to leg the hard leg for edge (the liquidity of which will also be the limiting reagent on the trade).

If you are not a market-maker, trade the leg you have more conviction in or use the history to enter/exit the leg that fits into your overall portfolio more synergistically.

 

Last word

These charts should be thought of as indicators as opposed to backtests. The allure of these charts is they hint “backtest”. By peeling back their insidious, white lies, we can approach them with the right humility —they are clues. The opening statement in a trial, not the final verdict.

 

Stay groovy

☮️

the 2 vectors of volatility scaling

I fired up Corey Hoffstein’s goated Flirting With Models podcast to hear Scott Phillips discuss “ugly” edges in crypto. This episode came highly recommended in my corner of twitter. It does not disappoint.

But I want to zoom in on one part. Scott says:

If you did what I do perfectly, you’d be up well over Sharpe two, and probably with size. But the way that I do it, our four-year Sharpe is 1.7 with retail-level costs. So cross-sectional momentum is a little bit better than that, but you don’t have the really nice positive skew of trend. And cross-sectional carry is about a Sharpe 1.7 as well, and slightly orthogonal. So you blend the three of them together, and then you’re at Sharpe two easily — and without even good execution…The math holds. Returns scale with the square root of independent bets.

Scott misspeaks here (easy to do in a conversation) but what Scott means is volatility scales with the square root of independent bets (returns scale linearly). This concept underpins one of my most important posts — Understanding Edge. It is the basis of all trading businesses without exception. It’s Day 1 learning at a prop shop. Munger has that “Take a simple idea and take it seriously” advice…This is THE idea Jeff Yass took seriously.

I’m repetitive on log and compounding math for 2 reasons that extend beyond the shock factor of the “lilypads in a pond” puzzle:

a) Investing is a serially repeated game so compound returns are our primary concern

b) Option theory sits atop logreturn math which is just continuous compounding (in fact e, the Euler constant of 2.718, is your growth of $1 if you continuously compound at 100% rate for 1 unit of time. See Using Log Returns And Volatility To Normalize Strike Distances)

We’ve confronted this math many times from different angles:

Compounding is a multiplicative process that describes how returns scale across time. “Volatility drain” reminds us that volatility’s interaction with growth is embedded in that process.

We already understand how compounding scales across time as a function of volatility.

But volatility itself has scaling properties.

That’s what Scott means when he says (or meant to say) it grows by the square root of independent bets. Regular readers have seen me scale a daily return to an annual return by √251 or ~16 many times. But I don’t always remember to say that this assumes no correlation between daily vols (mapping to the “independent bets” language).

In multiple posts, we have seen that volatility is understated in the presence of autocorrelation:

Autocorrelation affects how we scale vol through time per asset.

This is only half the volatility scaling story. It’s one vector.

The second vector is how we scale vol across our portfolio.

This depends not on autocorrelation, but on pair-wise correlation.

Independent bets are simply bets that have no (ie zero) correlation with one another.

It is why you can blend several strategies and end up with a composite Sharpe greater than any of the components.

Let’s look closer.

Let’s consider an equal-weighted portfolio of ETFs:

  • IBIT (BTC)
  • GLD (gold)
  • HYG (high yield credit)
  • IEF (10-year notes)
  • QQQ (Nasdaq stocks)

Method:

  1. I used the hedge ratio tool to fetch the correlation of daily returns for each of the 10 possible pairs (5 choose 2) for the past 16 months.
  2. I chose a reasonable vol for each name and then backed out an expected return by assuming the Sharpe Ratio of each asset is .40. I’m ignoring RFR in SR — I’m just using expected return / vol.
  3. I convert the correlation table into a covariance table to do the matrix multiplication for computing portfolio vol. I explain the math in writing, video, and in a spreadsheet I’ve shared before:
    1. ✍🏽Computing portfolio vol (moontower)
    2. 🔻 Portfolio Vol Spreadsheet (download)
    3. 🎥Portfolio Vol Explainer (Moontower YouTube)

 

Here’s my work for the equal-weighted portfolio:

What to take note of:

  • If we compute the portfolio vol in the “stressed” condition where all the pair-wise correlations are 1.0, then the portfolio vol is equal to the weighted average vol of the components. In this case, the portfolio vol is 20%. There’s no diversification benefit to the portfolio vol or SR!
  • When we use the actual correlations the portfolio vol drops to 12.5%. Since the expected return is unchanged the SR improves from .4 to .64. Massive diversification benefit.
  • The ratio of the portfolio variance to average weighted stock variance is the average weighted pair-wise correlation. The average weighted stock variance is the same as the portfolio variance if all the correlations are 1.0
    • For the option fans: this math is exactly how implied correlation is backed out of the index option market. The ratio of index variance to weighted average stock in the basket’s variance is the implied correlation! The cheaper index vols are relative to stock vols, the more implied diversification benefit there is in the index. This concept sits at the heart of dispersion trading.

A portfolio that equal weights IBIT and IEF sounds a bit comical. Let’s re-do the analysis with an inverse vol-weighted portfolio:

Now IEF has 10x the weight IBIT does. If you multiply each component’s weight by its vol you’ll notice they each have equal risk contribution.

The normal portfolio has a Sharpe of .68 but if all corrs go to 1.0 the SR drops to .40 and the portfolio vol goes from 6% to 10%.

Even if the math isn’t interesting, the principle should be seared into your risk brain. Adding zero or negative correlation assets to a portfolio can improve its risk/reward even if the asset’s individual SR is inferior to the average component. An asset’s stand-alone properties are not as important as how it contributes to the risk/reward of your portfolio just as how an asset’s arithmetic return does not mean much if don’t consider its vol. After all, we care about compounded returns.

These dual concepts, timer-series volatility (influenced by auto-correlation) and cross-sectional volatility (influenced by pair-wise correlation of components) give a fuller picture of how returns AND volatility accumulate through time. You care how both the numerator (return) and denominator (risk) scale.

Application to allocators

Professional allocators recognize that much of the low-hanging fruit of long-term results is sound portfolio construction. Basic hygiene in how you stack investments by understanding their properties in various states of the world is something you have more control over because volatility and correlation are more stable than expected return predictions. Simple optimizers are incredibly sensitive to expected returns — which is a bit self-defeating when you realize this is the metric we know the least about.

To add a bit of practical color. I have a good friend — a physics academic turned investor — who was CIO for the family office of one of the partners of [insert the best quant firm you’ve heard of and it’s probably the one]. He admit something he found embarrassing — a shockingly small number of ideas underpinned the family office’s approach and none of them is a secret. The scaling property of portfolios is one of them. If they sourced 16 investments that generated a high-quality portfolio, in order to double the shape they needed 4x as many additions to that portfolio of similar quality (which was already high) to double the portfolio Sharpe. Pretty much impossible when all the investments are LP stakes in quant funds (due diligence alone is too labor intensive, never mind finding that many great managers).

[I’ll get around to toying with this tool a bit more to tinker my way to seeing how the scaling works — for example going from a 3 stock to 12 stock portfolio where I add names with equal SR but different correlations. I’m down to the wire on writing this post as it is.]

Application to traders

The last part of this video shows how the interaction of return and volatility can inform how you think about sizing risk.

If you prefer text, the heart of the material is captured here:

Understanding Daily vs. Annual Sharpe — and Sizing Risk with Intent

Let’s start with something simple but important.

🗓 Daily SPX Returns

The S&P 500 has a daily mean drift of about 3 basis points, with a standard deviation of about 100 basis points in normal conditions. That gives you a:

Daily Sharpe ≈ 0.03

(This ignores the risk-free rate, which is roughly 1.5 bps/day.)


📆 Annual SPX Returns

Now zoom out.

The S&P’s annual return is roughly 8%, with an annualized volatility of 16%. That gives:

Annual Sharpe = 8 / 16 = 0.50

Here’s the interesting part:

Annual Sharpe (~0.50) ≈ 16× Daily Sharpe (0.03)

Why? Because volatility annualizes by the square root of time:

√251 ≈ 15.8, or roughly 16


🎯 Sharpe Ratio Benchmarks

Let’s anchor a few examples to make this more intuitive:

  • SPX benchmark: 3 bps return / 100 bps vol → Sharpe 0.03
  • Sharpe 16 strategy: 1% return/day, 1% vol/day → Sharpe 1.0 daily
  • Sharpe 2 strategy (annual) =
    → Daily Sharpe ≈ 2 / 16 = 0.125

So:

Sharpe 2 strategy means your daily standard deviation is ~8× your expected return.

That 8-to-1 ratio is a helpful sanity check.


💰 Backing Into Risk Limits

Let’s say you want to size your book around a Sharpe 2 strategy. Here’s one framing:

  • Daily P&L target: $1,000
  • Daily volatility: $8,000
    → Daily Sharpe = 0.125
    → Annual Sharpe = 2.0

Now annualize:

  • Yearly expectancy: $1,000 × 251 ≈ $251,000
  • Annual volatility: $8,000 × √251 ≈ $126,744

What does this imply for annual ranges?

  • +1 SD year: $124,256 – $377,743
  • +2 SD year: ~0 – $500k
  • You’re brushing against zero at 2 SDs

📐 How to Use This for Book Sizing

You don’t just ask how much do I expect to make?

You ask:

  • What kind of daily swings are consistent with my goals and risk tolerance?
  • Can I size positions and set Greek tolerances so that my volatility lands near ~8× my expectancy (for a Sharpe 2 strategy)?
  • Do I have enough emotional and capital runway to survive the downside scenarios?

etf fair value

I replaced my IBIT shares into long calls when BTC vol got crushed this week while selling my VIX futures which held up quite well on the equity rally, a possibility I suggested when I explained that the elevated but also flat term structure of VIX suggested vol was here to stay in 2025. From vol speed round:

As I was looking at IBIT I thought I’d share a habit I picked up from distant past of ETF arbitrage — estimating the prem/discount baked into the ETF price I’m about to trade:

  1. Go to the IShares web page to grab 3 values (highlighted)
Image
  1. Enter them in my spreadsheet to compute the ratio of NAV to BTC price. The cells with the box around them are via the previous close values. I then apply the fair ratio to the live BTC price compute IBIT’s live premium or discount.
Image

hedge ratios

Net of last week’s April expiry the flurry of trading from the last few weeks has left me net long delta. That’s not accidental. I had plenty of room to be a buyer on balance in the turmoil since cutting lots of portfolio delta heading into the election.

[If interested…I rebalanced much of stock delta into t-bills bills then re-deployed into bonds, TIPs and silver in late January.]

Coming out of the April expiry last week, my position gained ES futures, VIX futures, IBIT, with my only outright option position being some May AAPL put spreads.

Back in November I published this video which show how I look at the risk of our household portfolio.

But in the flurry of trading while I’m mentally separating what I’m doing for edge and my total portfolio, I do have some delta bias under the hood to effectively dollar cost average since I de-risked in October.

But I want to keep close tabs on how much I’m adding which means summing positions in a vol or even better beta-aware fashion. NVDA is far more volatile than SPY so just as traders combine all their deltas into SPX or some other benchmark terms I like to also normalize back to SPY equivalent risk.

This is a doorway into hedge ratios and beta-weighting. Beta-weighting is not just vol weighting but includes correlation.

I’ve written extensively about how to do this in:

🔗From CAPM To Hedging (17 min read)

That post steps through derivations and the basics of correlation math but in my personal portfolio spreadsheet I needed to make a little widget where I could just put 2 symbols and get a hedge ratio.

We actually have a calculator in the app but you need to supply the inputs.

However I realized with Excel’s built-in =stockhistory I could not only get the inputs, but easily plot time series and scatterplots to understand the shape of the “idio” risk and variation in the beta over time.

🚀We are going to add this functionality to the moontower.ai app. We already have the data, just need to extend the UI.

In the recent month, I wanted to understand how many SPX delta equivalent I had in VIX futures. I used VXX as a proxy put it into Excel and voila got some idea of how much “less” long SPX I am with the VIX futures incorporated.

So….

I made a video walking through the Excel tool. It’s some of the most practical traders-use-this-everyday-type knowledge. We skip the derivations and jump to how do I actually use this data to size hedge ratios or estimate my book’s beta.

I hope you find it as useful as I think it is. (If not, I’ve got more work to do on the explanation side so let me know!)

Paid subs get the spreadsheet. If you have the stockhistory function in Excel this will work seamlessly for you. It’s addicting to toggle thru pairs so fair warning!

The spreadsheet link below has 2 bonus items not shown in the video:

1) A widget for spitting out the hedge ratio not just running data

2) A little position radar template on a separate tab to keep your gameplan organized


Before getting to the sheet, tomorrow night is Ricki Heicklen’s Trade Gala party in SF. I mean yesterday we learned that Jane Street made $20B in 2024 which doubled what they made in 2023 which shattered their record from the prior year. I think they are the most profitable firm in the world per employee (they have about 3k employees).

I think it was Morgan Housel who once wrote that very few orgs in the world could say their edge is just flat out “we’re smarter than you”. There’s usually some other sauce. He gave the example of RenTec. Jane Street is another that can boast the same. This is a cool opportunity to see how that crowd thinks.

This is a special moontower invite that Ricki sent me. She’s repeatedly acknowledged that anyone she meets that comes out of this community is a breath of fresh air which she poses as a compliment. I’m not one to insult a compliment but it’s a testament to y’all. I don’t control how you act.

Anyway the party starts at 8pm and runs thru 6am…well I’ll let you click on this yourself. This is actually a custom invite for Moontower readers:

Trade Gala Party

A quick description..Trade Gala is a limited-access costume party with markets, a puzzlehunt, and demo-ing a new trading game.

If you want to attend the trading BootCamp the rest of the weekend sign up below. It’s truly ridiculous. If you are a novice it will open your mind to what trading looks like thru the eyes of sharps. It is hard to come by this in the level of detail you’ll see here.

If you are a pro, well, you might want to know you might notice some things that you are up against. There’s a lot to learn for everyone.

trading.camp

It’s view only but you can save a copy for yourself and do what you like from there.

  1. When you click the link it the link will open in your browser.
  2. Download a Copy. (see pic)
For the spreadsheet continue to moontower.substack

If they ban short-selling derivatives become the underlying

A reminder in the spirit of being attuned to seemingly far-fetched risks:

If short selling were restricted in any way, the value of puts relative to calls on the same strike increases in a put-call parity framework.

Another way to say this is being long stock is more valuable since only long sellers can sell. If puts increase relative to calls on the same strike, as they do when borrow costs increase, that is like a synthetic future on the stock trading at a discount to the stock price.

Similarly, being short futures vs being long index or SPY expresses the same bet. If futures start trading at a sustained discount to the cash index value it could also reflect an implied probability of short-selling restrictions being imposed.

If futures are trading at full carry and you think such a ban is possible it’s an asymmetric bet to put on conversions (ie short futures, long stock or for options short combos long stock).

Broadly speaking, the derivatives market becomes the underlying market. When the underlying market becomes encumbered do to technical frictions, derivatives are no longer just “derivatives”. The arbitrage mechanism is severed. The derivatives market becomes the home for price discovery.

I’ve seen this happen throughout my career. Just a few examples in addition to hard or impossible to borrow/short situations:

  1. HYG becoming a liquid referendum on illiquid high-yield bond market
  2. Cotton option synthetics continuing to trade even when the underlying futures are locked limit up (my trading claim to fame is selling the all-time nominal high in cotton in late 2011…it was an option synthetic around $2.25)
  3. When a stock is halted, the premium or discount to any ETFs holding that stock computed using the halted stock’s “last” price before the halt implies the price the stock will open when it resumes trading. These situations were common during the dot-com heydey when stocks would be halted intra-day on pending news announcements.

🔗Related

📽️Teaching Options Basics With Live Data (Moontower YouTube)

📔Synthetics: Alternate Realities (Market Jiujitisu)

BTC Autopsy

In Sunday’s surfing volatility, I recapped my IBIT (the spot BTC ETF) options trading for the 10 days preceding the March 14th expiry.

That post steps through the conceptual steps and flow of trading “surfing” volatility. I mentioned that trade went about as well as it could have since the stock pinned my short. But that’s reporting results. Evaluating the trade requires decomposing the “vol p/l” because that was the intent of the trade.

Just to recap, Sunday I wrote:

On March 4th, I initially paid 61 vol for the April 38 puts and sold them at 63 at the Mar14 expiry, while the Mar14 48 puts I had sold at 64.5 vol saw their IV get crushed as they pinned near my short strike. The stock was $48.50 when I put the trades on and was trading $48.14 at expiry.

The ratio trade collected $0.67 [I wrote .65 in the last email but as I did the autopsy it was actually $.67] on the small leg upfront. As the fronts expired worthless, I closed out the larger short leg at $0.40 per contract, yielding a total profit of $1.47.

When I break down the attribution, I see a small win on IV (although the vega isn’t as meaningful as the gamma/theta battle on options of these tenor), a giant win on realized, a small loss on delta, and then the positive luck on path which is the short expiring at the strike.

I promised to publish the actual attribution.

From that point of view you will see how I got lucky. But you’ll also see inherent noise in evaluation.

There’s big lessons in this.

We will work through this in 3 steps.

  1. Actual performance
  2. Benchmarked performance
  3. Why they differ and what that reveals about vol trading

Onwards…

Actual Performance

This is the most straightforward part of the analysis. It’s simple. But it also teaches us nothing. Unfortunately, this is where most people stop so learning takes longer (if it ever happens at all).

On March 4th, with IBIT at $48.50, I traded a calendar ratio spread:

  • sold 1 March12 48 put @1.79
  • bought 2 April 38 puts @.56

I net collected $.67 per 1 lot “on the small side” (language for ratios trades can be weird).

On March14th expiry, IBIT closed $48.14:

  • The March puts expire worthless
  • I liquidate the April puts at .40 (they have expanded in vol by about 2 points since I purchased them but the vega was small)

P/L per 1×2:

+$1.79 on the March put

– $.16 x 2 April puts = -$.32

Total profit = $1.47 per 1 lot on the small side or $147

[If I traded 10 lots on the small side, ie a 10×20, the profit is $1,470. A 100×200 lot ratio would be $14,700 and so on.]

Summarizing*:

Hooray, right?

Not so fast. This is just “resulting”. Trading is too noisy to learn from that.

Let’s go deeper.

 

*I actually did another trade which made the performance even better but adding another trade at a different date complicates the coming analysis which I want to keep approachable. I’ll mention it at the end. It’s more interesting as a matter of “vol surfing” rather than direct attribution but it also highlights just how dynamic option trading is.

Benchmarked performance

This table shows where the stock was when I first did the trade, the change in stock prices during the holding period, and there the stock was at expiration when the trades were closed.

To annualize a daily move into a volatility you can multiply by 16. Notice how large the daily moves have been since I sold the options. 4 out of 8 days the moves were greater than 1 st dev with 1 exceeding 2 st devs (st devs as implied by the March IV).

Once we have 5 returns we compute a 5-day realized vol. The closing IV is always less than the RV (although intraday the IVs did whip around a lot). Negative VRP!

This table shows the total greeks as if the position is short 100 March puts and long 200 April puts:

Things to note:

  • The position is short gamma. Even though its long 2x as many April options as it is short March options. That’s because the shorts are closer to expiry and closer to the stock price than the April 38 puts.
  • The closer the stock is to $48 and the less time to expiry there is, the larger the short gamma position, and the larger the theta collection.
  • At expiration, the stock expires above the strike. Going into the expiration I’m short gamma so above the strike my position becomes short as my March puts “go away”. My greeks at the end of the day, reflect my April puts — now I’m short delta, long gamma and vega, paying theta.

Estimating p/l attributable to greeks

✔️Realized p/l = gamma p/l + theta p/l

where gamma p/l = .5 x gamma x (change in stock price)²

✔️Vega p/l = vega x change in IV

Over the life of the trade, the position wins to April vega p/l but those gains are swamped by the sheer size of the gamma/theta tug-of-war.

If I hedge daily…

I’m losing that battle.

The negative gamma p/l dominates the daily decay because the moves are larger than what’s implied in the vol (which determines how much theta I collect — in this case, I’m “not collecting enough” to compensate for the short gamma).

To evaluate a vol trade you need a benchmark just like if you buy a stock you might benchmark the decision in comparison to how the SPX or QQQ did over the holding period. Since this is a relatively short-dated trade, benchmarking to “how did my position behave assuming I hedged my deltas daily” is a solid idea.

Here’s how that looks:

The trade won small to vega, lost big to realized, but the attribution decomposition overestimates the loss because the gamma profile wasn’t as short on the large down move because of vanna.

💡Vanna is a second-order greek we are not attributing. It can intuitively be understood in words: “when the stock was at the bottom it was far away from the March shorts and closer to the April longs and therefore lost a lot of short gamma on the move”. The gamma p/l estimate assumed constant gamma across the move. You can imagine how if we computed gamma p/l over every $.50 interval it would progressively decline as the stock kept approaching our long options. The assumption of constant gamma therefore overestimated the loss in this case and that overestimate is accounted for as positive “unexplained p/l”.

Back to the attribution. Here’s a visual of the hedged p/l vs the stock price (left panel) and p/l vs move (right panel).

The 100×200 lot ratio spread, delta hedged daily (assuming no slippage) lost about $1,000 or a dime in option terms.

$1,000 = $.10 x 100 contracts x 100 multiplier

If this trade was done with 1×2 contracts only it would have lost $10 (ie a dime on a 1 lot).

In sum, benchmarking the decision to do the trade to “what if I hedged it daily” the trade is a small loser even though the realized vol over the holding period was much higher than the IV sold.

The 2 primary forces that kept the theoretical loss in check were:

  1. When the stock was most volatile (on the down move) my gamma became much less short. This is why I wanted the downside. I expected BTC vol to outperform the skew as I expected the down move to be “destabilizing” (just as it has been in other risk assets recently…I would expect the opposite for something like gold).
  2. The stock expired at the short strike. I’ve discussed this before but it’s the roulette aspect of these dirty vanilla options. See Short Where She Lands, Long Where She Ain’t

Actual vs Benchmarked Performance: Why they differ and what we can learn from that

Recall, my actual performance was making $147 per one lot vs a “hedge daily” benchmark of losing $10.

What the hell is going on here?

Remember last week’s post a misconception about harvesting volatility?

I wrote:

By hedging your delta at various time intervals or as your position size breaches a threshold, you are first and foremost reducing market exposure risk. You do this because you don’t want directional p/l variance to swamp the vol-driven reason for doing the trade. A byproduct of this is your hedges “sample the vol”. If you hedge on the close every day and the market always comes back to unchanged after having large intraday ranges, you will sample a zero volatility. If you hedged intra-day you will sample a much higher volatility.

There’s no escaping the reality — every option trader experiences their own realized vol regardless of what the close-to-close volatility says unless they hedge close-to-close. If you benchmark realized volatility as close-to-close, you could think of your sampling as ‘volatility tracking error’ even though there is no “single volatility”.

Your hedges might sample the vol, but the intent is to cut risk, ie manage position size. You can appreciate this by considering the opposite extreme — you do option trades for volatility driven reasons but you never hedge.

What happens?

You are still trading vol. The expirations are the moments when you “sample” vol. The realized vol you experience is point-to-point volatility over longer stretches of time. It’s just hedging on a long interval.

I didn’t plan it this way. That post came out Thursday morning. I didn’t know how my Friday expiry position was gonna turn out. But the difference between my actual p/l and the theoretical p/l if I hedged daily is a perfect example of that quote.

I did not do any delta-hedging. I was prepared to own the shares if IBIT closed below the strike and sized the trade, which was driven by a vol axe, to be ok with an unhedged outcome. If you recall this video, I showed exactly how I studied what a disastrous result could look like:

Still, why was my actual performance much better than the delta-hedged counterfactual?

By not hedging I only sampled the vol in 2 places. I sold the near-dated 48 strike puts with 10 DTE when the stock was $48.50.

At expiry, the stock was $48.14.

Despite all the whipsawing, the 10-day point-to-point return was a measly -.75% and I was short 65% vol. I won to being an ostrich.

Of course, this is luck and not a strategy. If I bought the options and didn’t hedge I wouldn’t have “sampled the high volatility” and would have gotten slammed.

You can’t know the path.

The benchmarked “delta-hedged” performance of the position is a better measuring stick of a vol trade than looking at what happens if you do nothing since part of the reason for trading the vol was a comparison of the implied vol to the daily realized vol. You can’t pretend the daily realized vol doesn’t matter when you do an autopsy.

You may not agree that “daily delta hedged theoretical performance” is the right benchmark for what you are doing, but just “resulting” is active self-deception. If you trade over longer time scales the vega attribution will gain relative importance. You might even benchmark realized vol using weekly sampling. If you trade dailies, you can probably just compare straddle prices to dollar moves and ignore IV measures altogether. Whether it’s report cards or stopwatches, students and athletes don’t get better without measuring. The same is true for traders.

A humble bit of advice: to get better, construct benchmarks in light of the metrics that drive your trading decisions.

Wrapping up

My actual performance was much better than a more platonic version of how I should have done. I’ll take it. The opposite happens too. Vanilla options are filthy. If you love being fooled by randomness and like to brightside your results, options will give you all the rope your heart desires.

I recommend assassinating your ego and looking at the chalk outline. The IBIT autopsy report:

  • Sold an IV that turned out to be too low (bad)
  • Owned a downside put ratio (good risk management and judgment about how vol would react on a large risk-off)
  • Got lucky on pinning the short

Final note

I mentioned that I did another trade in the fracas that I didn’t include in the attribution because it was done on a different day.

Let’s talk about that one.

Near the depths of the sell-off with vol ripping, I launched another clip of puts. This time the March14 43 strike at $.77 with the stock around $45 (they were about 85% IV) or about 20 vols richer than my April puts.

Here’s the scenario. I’m getting worked because I’m long delta as my short $48 puts are now $3 ITM but I’m long 2x as many April puts which are picking up greeks as my March puts are losing vega and gamma.

In other words, I got bullets. This is not an accident. This is in the DNA of how I trade (the readers who’ve spent a lot of time trading beside me are laughing right now because they know exactly how I’m thinking…pit trader to the f’n bone).

Now I can sell when they’re really coming for it. Those teeny puts’ value don’t come from some actuarial place. It’s from the dynamic understanding of how things trade when shtf. I didn’t buy the ratio to go to the grave with it but so I can paint. IBIT is down nearly 10% and vol is singing. So I monetize some of these extra options that have greeks. But instead of selling the April puts I sell the March 43 puts because that’s what the market is paying up for.

To be clear, this is risky. I’m taking calendar spread risk. I’m also not hedging. My risk is capped of course, if IBIT goes to zero I ride the long shares down to $38 but then the pain is over.

However, if IBIT rips back up to $55 all I’ve done is collect premiums. The p/l will be positive but the autopsy will say “this man left a lot on the table by being short vols that turned out to be a buy”. Let’s not sugarcoat short gamma.

As it turned out, those $.77 options expired worthless so the entire series of trades amount to $224 per contract of profit ($147 for the first package + $77 for the additional puts) per 1 lot.

[Again, just basic option accounting — if you sold 100×200 ratio then sold an additional 100 puts then you are net flat option units. The p/l at expiration is $22,400.]

All of that said — the second batch of sales also fail an honest attribution lens. The 5-day rv that prevailed after selling 85% vol was 100.6%.

[I’m reminded of the wild Oct 2009 nat gas options. There was no IV level that ever traded that was above the realized vol. Vol got over 100% and it wasn’t enough. But that lasted much longer than this boondoggle.]

On a point-to-point basis, it is a 7.2% move in 4 days or ~57% vol move.

[7.2% * sqrt(251/4) = 57.3% ]

Note that the up and down is worse than if the stock went up 2% per day for 4 days. Even though that move would have been further, in a delta-hedged strategy it’s more benign. The whippiness is like a treacherous course through the mountains that would otherwise seem short if you measure by “how the crow flies”.

Options are fascinating because they must balance both path and destination. 2 investors can trade with each other, one based on path and one based on destination and both win. At the macro level, the participant who gave liquidity to the delta-hedger lost. Just imagine buying the 43 call for 50% vol and the stock goes up 2% per day for 4 days. The call seller who hedges daily and the call buyer who goes on vacation both win. The trader who sold the hedger shares all the way up holds the bag. Of course, we’re anthropomorphizing a system here but you get the idea.]

To repeat what I said earlier, this second trade is more interesting as a matter of “vol surfing” rather than direct attribution. It highlights just how dynamic option trading is. It’s a 3D boardgame that slides along a time dimension. I’ve alluded to a lot of this conceptually in my writing over the years but with the moontower.ai visor on I’m getting my sea legs back bit by bit which means I can get more concrete.

If you are reading this as an options novice with a process for using options already, I’m not advising or encouraging you to invite this kind of brain damage into your life. The tools are useful for much simpler options applications because they are, say it with me, always about vol (with an exception for vertical spreads but our Payoff Visualizer has you covered for that anyway).

Let’s leave it there.