This is a follow-up to Derivative “Income” Bumhunting where I shared skeptical takes on derivative income ETFs from:

• quants (Roni Israelov, David Nze Ndong, the Alpha Architect)
• a former options market-maker who runs an RIA that uses options (Mark Phillips)
• a relative value vol manager (QVR)
• a tax specialist (Brent Sullivan)

I also gave the moontower take which was more of an eye test that rhymed with Mark’s take — an alpha approach to vol is simply not going to be rules-based. Option alpha is built on quicksand because the direction, size, and persistence of opportunity depends on the opponent (the market’s bias). When fear is dominant, you get paid for selling volatility but when the adversary is complacency the job to be done is to pay theta and warehouse options. You get paid for taking what the market gives you.

An ETF with an options mandate is a point guard than can only go right. So the onus is on the investor to know when to put that point guard in the game. It’s not a set-it-and-forget-it choice — you have to be the coach.

No asset gatherer is going to tell you that. This isn’t index investing where you get paid for time and tolerating swings.

Let me revise that.

Part of it is — you are effectively selling in-the-money puts so you are getting some exposure to equity risk premium and then some exposure to a risk premium that is sometimes consciously taking away your right hand dribble. Algebraically you are paying that point guard a lot in fees and tax treatment considering the pure equity risk premia piece can be had for zero.

You can make a valid argument that options can be used to change the shape of your payoff instead of alpha. But this works because options are surgical tools priced for specificity. The logic of passive call-selling is anything but that. Even if I granted such logic, you still need to show why you aren’t just better off buying less shares to match the delta (the ETF’s are less volatile because selling calls means they aren’t 100 delta like SPY) of the ETFs you’re considering hence saving on explicit and hidden costs for a payoff shape that does significantly better in every case except where the market sits relatively still.

[Personal bias warning — the utility preference to outperform when nothing is happening is bizarre. I want to outperform on the downside because I get the double whammy of my wealth holding up better PLUS all the things I want to buy getting cheaper. If you think of your future purchases as a liabilities, ie maybe you want to buy a condo in Miami, your assets are not only holding up but your liabilities are shrinking on the downside. Likewise — there some component of the upside distribution that is an inflationary, blow-off top. This is a disaster to underperform in — or more directly, the stupidest call option you ever sold. I’m being dramatic to mention this in the admittedly narrow context of covered call ETFs but I’m just sharing why I think shifting payoffs from large moves to small moves has never really appealed to me. And just to get ahead of the “the real distribution has more small moves than the Gaussian assumption” — yea I know. So do the vol markets which use surfaces to “correct” the bell-curve. If you want to parse the price beyond that you are now in the club of “one’s life’s work”. That speakeasy’s password changes monthly and won’t be found in the VRP article you read one time in 2011.]

So far, this is all high-level take based on why alpha can exist, how options work, and their adversarial zero-sum nature. Let’s look closer shall we. I grabbed returns for the largest derivative income ETFs to do my own up/down capture analysis. Up/down capture is the metric QVR used — it’s a solid choice. I’m familiar with it from my days at Parallax. You’ll see why it lends itself well to hedged funds.

Learn:

• how I did the analysis (including normalizations)
• why I think the up/down capture formula you find all over the internet deserves adjusting
• how to interpret the results

Draw inferences about:

• the actual performance
• what performance depends on

I’ll focus on JEPI because the findings are interesting and as you’ll see — there’s more to the story than QVR’s picture:

Onwards…

What is up/down capture?

The up/down capture ratio measures how an investment performs relative to a benchmark index during periods of market gains (up capture) and market losses (down capture).

An excerpt and example from YCharts:

An upside/downside ratio of 100 means that the investment typically performs the same as the benchmark regardless of if it is rising or falling. If the benchmark increases by 10%, the investment increases by 10%. If the benchmark decreases by 5%, the investment decreases by 5%.

Investments usually don’t have upside/downside ratios of 100. Sometimes, an investment may rise 15% when their benchmark rises by 10% but falls 12% when the market falls 10%.

In this case, we calculate the upside/downside capture ratio by dividing the investment’s upside return and dividing by the downside return:

(.15/.10)/(.12/.10) = 1.25

Multiplying this by 100 gives us an upside/downside capture ratio of 125 for this investment.

Formula

Upside/Downside Capture Ratio = (Investment’s Upside / Benchmark’s Upside) / (Investment’s Downside / Benchmark’s Downside) *100

YCharts uses monthly returns data to determine Upside/Downside Capture Ratio

QVR contends that up/down capture is indicative of manager skill…up/down capture is important not to only produce superior performance but to have any potential of outperforming relevant benchmarks. Or said differently, knowing your manager is not destroying value.

I’d simply put it as — knowing your manager is better than levered beta.

If your manager is up 15% when the market is up 10% and down 15% when the market is down 10% the ratio is:

150% up capture / 150% down capture = 1

Like any single measure, up/down capture could never be a final word. T-bills have low up capture but amazing down capture. If SPY is down 5% and T-bills return 5%, is that negative -100% down capture? The whole up/down capture measure breaks (it’s also a dumb example to benchmark T-bills to SPY).

Investopedia also uses the YCharts method.

When you google up/down capture the first 3 results are Investopedia, Morningstar (there method is ambiguous despite publishing the stat for its funds), and YCharts. In fact most results on Google’s first page use the Investopedia/YCharts method.

Eh.

We already saw one problem with that computation — when the sign of the benchmark and fund return differ the ratio is negative.

The second problem is familiar to anyone who looks at ratios frequently — dividing by small numbers. If the benchmark return .05% and the fund returns .50% you get an up ratio of 10 for the period. It sticks out like a pimple on a chart even though it’s not an especially notable data point.

There are 2 superior methods to computing up/down capture.

1. Up/down spread

   If you scroll down to the BTS Funds doc you learn that’s what Morningstar actually does. They simply take the difference between the fund and benchmark returns. This is also QVR’s method.

The logreturn method

This is what I use:

[Apparently some place on the google search results called Longs Peak Advisory does a geometric return method which is basically the same]

JEPI

etfdb.com describes JEPI:

The JPMorgan Equity Premium Income ETF (JEPI) is an actively managed fund that generates income by selling options on U.S. large cap stocks. The fund invests in S&P 500 stocks that exhibit low-volatility and value characteristics, and sells options on those stocks to generate additional income. JEPI was launched in May 2020.

At $35b, JEPI is the largest derivative income fund.

When we compare the performance to SPY since July 2020 (I picked July because it lined up with the lookback windows of the volatility calcs), we see that it underperforms, but doesn’t drawdown as hard as SPY in 2022 and doesn’t recover as quickly in the past 18 months.

Which is really just an indication of what we know — it’s less volatile than SPY. This is by constitution. It sells calls against a long position so JEPI is simply long less net delta. The vol ratio bounces around a range of 30-80% but the average is 61%. Said otherwise SPY is 2/3 more volatile than JEPI on average.

But we want a better look at the risk/reward of JEPI relative to SPY despite the vol difference. Sharpe ratio is a conventional choice. This is rolling 1 year Sharpe (I don’t subtract the RFR from the numerator though) computed from daily logreturns. The chart starts in summer 2021 because the lookback is 1-year.

Not that JEPI performs better for the first couple years before giving up ground and finally being overtaken about 1 year ago.

Let’s look at the up/down capture. Instead of using monthly data as its commonly reported, we will use more granular weekly returns with a rolling 1-year lookback.

The individual legs of the ratio (up and down respectively) are far less than 1.0. That makes sense. JEPI is less volatile so it’s not going to go up as much as SPY or down as much. But the up/down ratio remains coherent since the lower volatility fact cancels out. You can see that the ratio starts off amazing, far greater than 1.0 but it starts to degrade and fall below 1.0 in July of 2022, about a year before the rolling Sharpe ratio succumbed to SPY.

Let’s look even closer.

We’ll scale the JEPI returns up to SPY returns by the inverse of the trailing vol ratio (so if JEPI is half the vol of SPY we’ll double its return to mimic a equal-vol weighted allocation).

That healthy up/down capture is being driven by outperformance:

1. during small moves which we expect since JEPI is short options
2. on the downside even for large moves despite being short options!

Make no mistake, JEPI was working quite well.

What does it look like as it starts to degrade?

The underperformance on the upside looks similar. If you squint maybe the underperformance seems to be a bit worse for the +4% weeks but it’s not hitting you over the head and the frequency of those weeks is still small.

However, the outperformance on the downside moves is a much thinner green envelope potentially indicating that the call premiums being collecting are not buffering the downside as much as they used to.

A Tale of 2 Halves

JEPI has been around for 4 years. Like the Falcons in Super Bowl LI, it got off to a strong start. I remember leaving the party at halftime and by the time I got home the Patriots were on the verge of finalizing their historic comeback. The JEPI underperformance in its past 2 years isn’t the same as Atlanta’s defensive collapse but an up/down capture of .80 isn’t gonna fill the seats.

Or is it?

This was a lot of AUM growth in one year (red box):

That’s a lot of new, easy to anticipate option premium for sale. This is not football. A big fanbase is not an advantage.

2024 was the first time any months saw an outflow and they were gnat-sized.

Wrapping up

This was a high level analysis. It demonstrates the value of up/down capture as another way to slice up performance. We examined what happened when a popular options strategy gained steam.

I don’t think this resolution makes crowding effects a conclusive cause of the performance degradation but my bias is definitely in that direction.

If you want to dig further, I left a lot uncovered:

• I didn’t even try to relate implied vols or skew data. (If you do this at the index level, you get noise from implied correlation readings.)
• You can reconstruct the holdings and do p/l attribution on options they sold if that’s available (or inferable).

I will run the same high level analysis for a few more derivative income ETFs: DIVO, JEPQ, NVDY, RYLD and XYLD. If their performance also followed the same gradient descent I’ll see how it lines up with their own respective AUM trajectories. Confirmation or divergence could both be interesting.

For now, I leave you with 2 final bits:

Covered Call ETFs: Shitburger With Extra Tax (9 min read)Brent Sullivan

The Tax Alpha Insider gets into the weeds of tax treatment of all kinds of investments. He spoke at CBOE’s RMC on the topic of taxes and derivative income ETFs.

Just to add a personal point. I have a rough heuristic of individuals being either markets people or structuring people. Traders vs lawyers. Contemporary folks like Matt Levine and Wes Gray are striking examples of people who understand both deeply and are actively sharing knowledge today. Henry Singleton would be on the Mount Rushmore for this breed of ambi-turner.

My brain isn’t on the same plane as these people but even on the rung I’m at I can’t build Ikea furniture or rotate a shape. I’d rather watch your 2-year-old than read a structured-product flowchart with the words “payor” and “receiver”. I hate thinking about taxes, entities, even real estate deals. Some people live for the words “generation-skipping trust”. My eyes glaze over.

I don’t use an FA, but accountants and lawyers are necessary balms. It’s good to see an account like Brent pop up since maybe I can at least get a glimpse of how much I don’t know.

Up/down capture that’s worth a lot

I made up the numbers below, but it’s not that different from what a well-run relative value vol strat will look like using monthly data. Before fees at least.

But they are expensive because who wouldn’t want a positive carry put option. That green outperformance region buys a lot of fallen assets at depressed prices. Your assets are up, while liabilities are down. Double win.

I recently built this cockpit view to see what’s going on in markets. I’ll be iterating on it as well as creating a page to incorporate my portfolio so I can do some high level bucketing by asset class, vol weights, and portfolio correlation. It won’t take much to get it to a suitable template for the personal account.

I’ll probably add proxy benchmarks to mimic private fund holdings that hold public securities. However, there won’t be accounting for angel investments. I hold them at cost on the spreadsheet and at 0 in my brain regardless of their “valuation”. If anything hits, I figure my kids will thank me one day. If not, and I trained them well, they’ll drag me over the foregone beta return. They own a lookback option on our sense of guilt. That goes beyond finances I’m sure.

We’ll talk about the cockpit view briefly before using it to describe Q3.

1) Columns Worth Clarifying

1. Change: Logreturn (%) from the previous close (current data).
2. Closing Vol Snapshot: Implied volatility as of T-1, includes:
   • 30d IV: Implied volatility for the next 30 days.
   • 90d IV: Implied volatility for the next 90 days.
   • 30d VRP: (Forward-looking 30d implied volatility) / (1 month realized vol) – 1
3. Return Z-Score: Normalized asset return based on implied volatility from a week, month, or quarter ago.
4. 30d IV Change: Change in 1-month constant maturity IV over the past week, month, quarter
5. Returns (weekly, monthly, quarterly): Asset returns for the specified periods,
6. RV (Realized Volatility): Annualized historical volatility calculated using daily log returns. Monthly RV is computed using 21 days of data for example.
7. Lagged VRP: Ratio of realized volatility that transpired compared to implied volatility from a month or quarter ago for example.
8. Correlation to SPY: return correlation for specified lookback
9. Beta to SPY: computed as realized vol ratio x correlation for lookback

2) Some insights from the cockpit view

• 30d IV is up across the board on weekly, monthly, and quarterly time frames despite asset return also being up everywhere except oil.

  I bought oil and sold stocks back on 9/11/24 when it seemed like a nice rebalance entry. I was wrong on the oil vol sale, but right on direction…I synthetically sold puts in oil vs selling railroad shares (CP)…see commodity kamikaze

• Short vol did well last week (see lagged VRP) in US index and sector index. It got WRECKED in China ETFs with weekly return Z-scores greater than 3 standard deviations (Return Z-Score column).
• Quarterly returns in SPY and IWM were close to 1 standard deviation (return / implied vol from 3 months ago). QQQ flattish as gains in TSLA, AAPL, META were washed by flat or losing results in semis, MSFT, AMZN.
• Bonds unsurprisingly had a strong quarter with rates falling but the lagged VRP showed vol was overpriced.
• The euro, yen, and gold all rallied strongly against the dollar this past quarter. BTC was flat.
• XLU (utilities) were the strongest SPX sector but utilities have bond-like properties so they seemed to inherit both the stock and bond rally. Their trailing correlation is low being pulled in opposite directions by SPY and TLT.
• The trailing 3 month realized vol performed well compared to how implieds were priced in late June. The August 5th chaos made its mark.

While I got the USO vol trade wrong, the GLD short vol idea carried well.

I threw this on Twitter yesterday:

The discomfort of short vol (this might have something to do with why it often pays):

On 8/16, I wrote about the GLD Nov 232 ATM call looking fat over 17% IV

The call was ~$9.40

Shorts have made nearly $2 on delta-hedged basis

But notice what’s uncomfortable…

GLD is up nearly 5% since then (as of 9/30)

Strike vol did come in hard and stay subdued for about a month after I wrote the post.

It’s recovered recently but the strike vega is much smaller with over a month elapsing & the call being .81d (not ATM anymore)

3) Using Excel to pull stock data

A 1-minute video:

Lots of fields:

There’s even a stockhistory() function.

I hope the cockpit view inspires your own ideas. We will be bringing a very similar version to moontower.ai

If you use options already, definitely check it out. It’s option trader goggles. Vol traders already understand the lens but if you are a directional or fundamental trader who is even curious about options the Primer and Mission Plan docs offer surgical ways to expressing your views. And if the cockpit is any indication, option metrics alone are useful for thinking about risk and opportunity even if you don’t trade options.

That’s the premise of Option Analytics For All.

Some headings from that post:

Option Surfaces As A Unique Source of Market Intel

• The typical use of option analytics
• Intelligence from options markets
• A Brief Detour into the World of Indicators
• Option analytics as indicators for non-option users

Hypocrisy is overrated as a vice.

Don’t get me wrong, a homophobe politician caught in a bathroom stall with his “friend” can go crawl in traffic.

But the cost of being rigidly consistent in your worldview is much higher because it’s dried in a coat of virtue upon which edifices of horror are constructed. It’s the idealogue who twists into a moral pretzel. Better to protect the ego than consider more plausible models of the world I guess.

Hypocrisy comes with built-in birth control. It’s easier to resist because it’s harder to rationalize. Its ability to recruit is smaller because the dissonance is out in the open. It comes off as self-serving which eventually tires the audience out. Ideology, on the other hand, is seductive. It makes sense once you accept the assumptions.

We are all shaped by experience. A career in trading has been a large influence on my belief that the need for coherence drives us mad. I was a far more dogmatic thinker when I was young. It’s not that being cognitively flexible enabled a career in trading so much as the requirement to survive ripped my trust in lazily examined assumptions right out of me.

[A nice example of why trading demands curiosity, suspicion, and honesty about assumptions comes from Risk of Ruin’s critique of Taleb’s so-called “ludic fallacy” which he convincingly argues is a strawman.]

The universal quality of fear — you must respond to it. But you can choose to respond honestly at the cost of ego. The flexibility that trading teaches flows from paranoia. The fear of making a faulty assumption load-bearing.

The need for coherence also comes from fear but animates a different response. Digging your heels into some playhouse fantasy that is continuously reinforced by overfitting every action or utterance as “evidence”.

[As a matter of spotting talent in trading, you can’t rule someone in based on cognitive flexibility (or any other single quality) but clinging to ego is a red flag. Some people are institutionalized by their fear. The signs can be subtle but defensiveness is a dead giveaway.]

You can train yourself to be more flexible with a scout’s mindset. This sits at the heart of trading. Hacking together charts or tables to find abnormalities. Testing the ideas under live fire while having the right sense of proportion around what the experiments can teach you (said otherwise — neither backtesting nor “resulting” is sufficient. One of these non-dogmatisms that some find repellent. I’d urge those people to consider other jobs. On second thought, come right in, the water’s fine.)

I’m currently reading a draft of a friend’s upcoming option book. There’s an outstanding section on what I’d call prospecting. Where’s the fertile ground to look for trade ideas? It’s an underdiscussed meta topic. There’s no shortage of strategies to be pitched but how many of them originate from a game-theoretic perspective of “why am I even able to find this?”

The section echoes in my head because it’s ultimately about how to see things differently. While that isn’t a guarantee to discover, failing to do this is a guarantee you will not discover.

When transitioning from equities to commodities in the 2000s, I had to unlearn lots of principles. Some were simply mechanical — there are different arbitrage bounds in equity vs futures options. Some related to the zero sum nature of commodities vs the perpetual nature of a stock. Futures are derivative underlyings themselves in ways that an equity is not. Equity is an entry on a cap table. A commodity is both an input and an output to be stored, exhausted, mined, or grown. Measuring vols and correlations will help you relate to an asset you are learning about. But thinking by analogy has its limits. The edge cases demand first principles context. The fine print so-to-speak.

In my chat with Corey Hoffstein, I hit one of these points in reference to shifting gears from market-making to position-taking:

You mentioned to me that early in your transition into Parallax, you had a mentor who said to you, quote, do you want to optimize your p&l on a daily basis or something else? And that this question really unlocked something for you. What did this question mean to you? And how did it ultimately impact your behavior in your new seat?

This was a sort of a profound one for me. It was something I needed to hear. So the context here — I was in my first or second year at Parallax and I was trying to dial in my spot-vol correlation parameter in oil.

Without getting too far into the weeds, your spot-vol correlation parameter will have a large impact on your model deltas. So, for example, if you run a spot ball correlation parameter of say 1% or 100 or negative 100, what you’re saying is every time spot goes up 1% I think vol comes in 1%, which is what I would say is that’s a constant straddle regime. Like saying “When the futures move, I believe that the straddle is always around the same price [net of theta ofc]”

So stocks up is 1%, vol is down 1% — constant straddle. Now, obviously, that’s a slope. And that’s a very local slope. If the stock doubles, I don’t think vol halves. So it’s clear that that’s not a constant parameter.

But I was being very locally minded. And the reason for that was I very concerned with my daily p&l, which is a bad habit I picked up from spending those couple of years as an indie market maker.

You don’t want to be too dogmatic about your spot-vol correlation, because it does change. And so what the mentor was doing as he was encouraging me to zoom out and think about the expected value of the spot-vol correlation rather than overweighting it to like the recent observations.

There’s an additional lesson in this that is presumes an assumption that lives in oil that is far less coherent for stocks — the absolute price level matters. Because oil is an input that is refined it is both a raw material and source of revenue depending on its end user. That means its price is represented as a margin or cost on some CFO’s spreadsheet. Its absolute price has many dependency arrows in that spreadsheet. Dependencies that are managed by flipping switches or placing hedges. Sure if GME stock price goes up enough you can expect a shelf — but the degree of freedom here is much wider than what is managed in oil.

Option order flows are therefore more conditional on absolute prices in oil than in a stock. In fact, the price of a stock doesn’t mean much without considering its ratio to earnings.*

In practice, this means I care about underlying price levels in commodities when considering spot-vol correlation or skew in ways that I would simply ignore in equities. This calls for additional tools to look at the market from other angles.

In tomorrow’s paid post, we’re going to expand our thinking about volatility term structure to see why it’s a diamond with several facets — and most interestingly — why multiple ways of looking at it are not all correlated.

The title of this post is a tribute to Phil Tetlock’s phrase in Superforecasting (my notes):

Dragonflies have compound eyes with thousands of lenses “synthesized into vision so superb that the dragonfly can see in almost every direction simultaneously, with the clarity and precision it needs to pick off flying insects at high speed.”

Stay Groovy

☮️

*This idea harkens back to my post Markets Will Permanently Reset Higher (My Sacrifice to the Delta Gods) about the option contracts people should really want:

While being long the index outright is a blunt hedge, call options, for all their extra hassle, are still not a surgically precise hedge. The right tail we are concerned with is risk premiums shrinking. This can still happen if earnings fall while multiples expand. Imagine earnings falling by 20% and the index only dropping 10%. Multiples will have actually expanded by 12.5%. I admit this sounds unlikely. But we are talking about this as a right tail event. In that context, the forces which are driving the price of capital lower may even accelerate in a recession. The financial option you actually want to buy needs to be struck on the index multiple, not the index level.

So unless a liquid market develops for the SPX 10yr 40 P/E Strike Call, I don’t see a simple financial options hedge.

In JEPI and the…Atlanta Falcons? I promised to follow up on a few other derivative income ETFs. In that post you get a dose of evergreen education on methodology. Today I’ll just present the charts with quick observations.

XYLD

This is the Global X SP500 covered call ETF. While JEPI manages $35B and has been around close to 5 years, XYLD was born in 2013 and manages $2.8B. It’s one of the largest derivative ETFs which shows just how big JEPI is!

Like JEPI, it’s perfomance degraded by mid July 2022. The second chart shows XYLD weekly returns on the y-axis vs SPY returns.

In this chart I note 2 things:

• A general observation: the rolling 1 year SPY sharpe has gotten over 2.0 4x in the past 6 years It spent all of 2021 up there and it touched 3.5 in late 2017!
• XYLD sharpe has been underperforming recently and massively underperformed during COVID. I do wonder to what extent it’s outperformance captured in the lookback thru early 2022 might have been due to call skew being strongly bid (overbid?) in 2021 — the year of mania, SPACs, NFTs, etc.

JEPQ

JEPQ is JP Morgan’s ETF that overwrites Nasdaq calls. It currently manages over $16B. It has a shorter history than JEPI and it’s performed quite well. But I would pause before inferring this to being Nasdaq specific before reading below.

QYLD

This is the Global X version of a Nasdaq call-overwrite ETF. It manages $8B.

Growth of $1 is not great relative to QQQ, but QYLD is also lower vol. So we look at the rolling sharpe. This has a longer history than JEPQ.

The sharp is often in line with QQQ but again has this skewed left tail to it where it can fall apart relatively. Zooming in on that Covid period, you see an extreme dip in the up/down capture driven by the up capture getting shelled plus the down capture spiking on that first Covid sell-off.

The behavior of the second chart is a bit strange. There is steeper underperformance than I’d expect on the downside (after all, the delta should roughly go to 1 and simply mimic the index) and the put performance is a stronger on some of the larger up moves than I’d expect…heck, I’d expect underperformance.

I have a subtle hypothesis for why we observe this. Perhaps the call skew and vols are sticker on the downside — the market drops but the call vols blow out so much that their realized delta is small. So it feels like you are riding stocks down with little offset from the calls depreciating.

On the up moves the calls which might have been too high to begin with massively underperform causing the ETF to do quite well. In other words, Nasdaq call deltas are much lower than you think!

This is just a guess and it’s a muddy one at that since we don’t see the same behavior in JEPQ and overall JEPQ has performed much better than QYLD and quite well on absolutely.

Could just come down to a skill issue? Hard to know without getting more into the details of the strategies for a finer attribution.

I use the following example all the time because it makes it makes the distinction between premium and income plain.

You’re long a $100 stock.

• It’s fairly priced because it’s 90% to be 0 and 10% to be $1000.
• You overwrite by selling the 500 strike call at $45.

Did you earn income?

A courageous response to my question on Twitter:

There is no problem here. You take your $45 and move on with your life. If you get called away you make 5x, and if your stock goes to $0 you came out with only a 55% loss.

Umm, incinerating money when you think you are investing is actually what I would call a “problem”.

You make $445 10% of the time and lose $55 90% of the time. You are literally better off betting on roulette.

If you overwrite a call that’s actually worth $1 at a price of $.95 because call markets are faded low for sellers, you are stuck with roulette odds. Factor in your brokerage costs (implicitly or explicitly) and effort.

I’d rather get a free hotel room.

As a market-maker when you sell a call you might book the difference between the trade price and what you think the option is worth as “theo p/l”. And even in that case marking-to-model is only going to show a few cents of edge.

Derivative “income” ETFs treat the entire option premium as yield. Guessing disclosure rules prohibit them from selling ITM calls and labeling the intrinsic as yield. Is the distinction that a hard arbitrage bound can’t be marketed as income but a soft one can be? After all, if you buy a .25d call in a random stock for 0, you’re like 99% to make an arbitrage profit with a series of delta hedges. If I sold the call for 1 cent, I can call that yield? Ok. Well, I call that “semantic arbitrage”.

Looking at the AUM growth of these funds, their cheerleading has worked. They don’t need any boosting. Turns out I’ve been collecting the critical takes on these ETFs for a few months. Are they biased? Of course. But I’ll excerpt and weave the arguments so you can form an impression to weigh against what “income” marketers claim.

You will hear the arguments from:

• quants
• a former options market-maker who runs an RIA that uses options
• a very familiar vol manager (who allowed me to re-publish their firm’s take which is nothing short of violence)
• a tax specialist
• a bit from me (and more next week, as I plan to get my hands dirty with some data)

Onwards…

The quants

Roni Israelov and David Nze Ndong’s paper A Devil’s Bargain: When Generating Income Undermines Investment Returns was published in the Spring 2024 issue of The Journal of Alternative Investments.

🔗free version of the copy that preceded it on SSRN

These are the key points remixed between me and ChatGPT (emphasis mine):

Passive Income Strategies and Covered Calls

The paper notes that many investors, particularly in the retail sector, are attracted to strategies that generate income, such as covered calls. These strategies have gained popularity due to their ability to provide income through derivative overlays, often being presented as ‘income-generating’ strategies.

Negative Relationship Between Derivative Income and Total Returns:

The authors demonstrate a strong negative mechanical relationship between the expected total return and derivative income for covered call strategies. Empirical evidence from a nearly 25-year analysis of S&P 500 Index covered call strategies supports this finding. Essentially, the higher the derivative income generated by these strategies, the greater the losses. This outcome contradicts the common assumption that high derivative income leads to higher total returns.

[Kris: This is framed as surprising, but moontower readers know better. Remember Distributional Edge vs Carry?]

Impact of High-Yielding Strategies:

High-yielding call selling strategies, by design, have larger short equity exposure, leading to worse returns. This is a mechanical relationship and is highly predictable. Additionally, selling call options introduces short volatility exposure, which can be profitable but typically does not offset the losses from short equity exposure.

[Kris: Hard to benchmark because the beta is less than the broader index. I’m exploring an adjustment as I want to run my own comparisons. More on that next week inshallah.]

Misconceptions About Covered Calls and “Income”

The authors challenge the idea of treating the initial inflow from selling a call option as income, as this overlooks the associated liability and potential for loss upon settlement of the option. They argue that viewing the initial cash inflow from selling an index call option as income, while ignoring the expected outflow at settlement, is misleading.

[Kris: Been on that train so long I’m slumped over a rocks glass in the bar car right now. And I don’t even drink. See what you made me do. By “you” I mean the committee who defined SEC yield and the asset managers who gave ’em the reach around.]

The quants over at Alpha Architect boosted the paper as well, putting a bow on the argument by comparing the sleight of hand to how dividend chasers are hunted:

Investor Takeaways

Israelov and Ndong’s findings demonstrated that at least some investors might be attracted to covered calls for the wrong reason, seeking income rather than equity and volatility risk premia. That attraction can lead to a misallocation to these risk premia versus a best-fit allocation when analyzed appropriately. They also demonstrated that these strategies might lead investors to have overly optimistic return assumptions guided by their derivative yield. Considering the yield as income could also lead investors to make misinformed choices in terms of spending.

The live returns of the two most popular covered call writing ETFs should cause investors to question the prudence of these strategies, which are also less tax-efficient than traditional long-only strategies.

The most important takeaway is that the call premium is not income. This is the same type of mistake investors make about dividends, leading many to overvalue them. By definition, income increases wealth. Dividends do not do that; when a dividend is paid, their investment is now worth less (by the amount of the dividend). In other words, a dividend is just a forced divestment of some of your investment—you are receiving cash but now have a lower equity allocation. It’s also not income, except for tax purposes, which makes dividend payments an inefficient way to return capital to shareholders. In both cases, the failure to understand this can lead to overallocation to “income” strategies.

Thoughts from former option market maker Mark Phillips

His post is a 5 min read and worth every second but here’s what I want to highlight from Show Me Your Edge (emphasis mine):

The edge [in passive stock index investing] is time. Patience is an expensive virtue – but it pays handsomely in the long run. $1000 a month in an 80/20 stock/bond portfolio for a 40-year career returns about 9.7% a year and turns into $5.3 million. And that’s only half of the monthly 401(k) max limit.

Equity risk premium pays you for your patience. That 9.7% a year is several percentage points higher than risk-free treasury bills because of volatility. Don’t stop buying the few times a decade when stocks drop by more than 20%.

The most naive investor can capture the equity risk premium. It’s available in any brokerage account and with roughly 15 clicks the entire process can be automated.

With equity options, there is no set it and forget it.

There are rapidly increasing number of ETFs that offer options structures in a simple wrapper. But this is grocery store sushi. I’m not so much concerned about them suppressing volatility or creating some sort of derivatives tinder box, as I am with what an undifferentiated approach does for returns.

It takes 15 clicks to set up a lifetime of ERP.

It takes 15 trades to collect a month’s worth of VRP. 

The net of the implied vol and historical vol is a very abstract number. Other than over-the-counter agreements in the tens of millions of dollars, that’s not a tradeable concept.

Covered calls are a very simple way to capture part of this. You’re long the equity realized path, and short some implied volatility. But positive VRP could still be a losing trade. Stock going down, even at a lower than expected rate is still negative PnL.

If you can’t trade variance swaps, you can still try and manage a short premium capture strategy with condors, straddles or strangles. Moving these around to stay short the right amount of volatility while stocks move and strikes go in and out of the money is introducing a lot of friction into a trading strategy.

The rebalancing for an index strategy only happens quarterly, and stock execution is cheaper and finer-grained than options execution. This weighting adjustment works very much in the favor of the investor. Outperforming companies are added to, while laggards slowly melt away. Options greeks not only evolve on a daily basis, but the friction and frequency of adjustments are a major performance drag.

Consistently applying the same trade, like a QQQY that sells ATM NDX puts every day, is also going to deliver sub-optimal outcomes. The directional aspect might be a tailwind, but there’s nothing particularly systematic about why that option should be a consistent sale.

Adjusting trades based on delta (i.e. implied volatility levels) is a useful adaptation, but options overlay strategies are more about shifts along the utility curve than adding alpha. Selling calls to buy put spreads transforms the risk, it doesn’t deliver performance.

There are plenty of edges in options, VRP is real…But passive can’t be an edge. Dumping your money in equity markets and closing your eyes will have you beating most active managers. Blind selling or buying options will almost certainly be a performance drag.

Vol manager QVR pulls no punches

As a vol manager themselves, they are biased. And to be clear, so am I. Most of my opinions here overlap (not shocking — with some LinkedIn sleuthing you can spot some personnel merry-go-round between my past firm and QVR although I had no involvement).

Excerpts from their latest letter, DERIVATIVE INCOME…? DEFENSIVE EQUITY…? HEDGED EQUITY…? ARE YOU BEING MISLED?:

The marketing departments at banks and asset managers have conjured up some savvy descriptors for options strategies over the years. Portfolio Insurance, Option Overlay, Hedged Equity, Overwriting, Derivative Income, Defensive Equity, Structured Outcome, Enhanced Equity, and Buffer. As sales departments had success selling these types of products, the assets under management (“AUM”) grew.

Strategies using options are now commonplace in portfolios. Investors will always look for ways to cheapen hedges, but unless very closely understood, persistently capping the upside on your equity allocation is not the answer. Historical techniques such as collars and put-spread collars employed by some of the largest equity providers have become so overcrowded, they will likely lead to nothing but extreme relative underperformance, simply value destruction. This statement is arguably also true for strategies now referred to as “Derivative Income” (aka call overwriting, defensive equity, covered call, etc.), which are all simply selling short calls against equity.

Historically, the go-to benchmark here has been the Cboe BXM Index. An accurate description of benchmarks such as the Cboe BXM or similarly PUT indexes historically would be “Equity-like returns with lower volatility,” but today should be replaced with the statement, “Equity-like risk with lower returns.” These volatility selling strategies cap upside. An option selling strategy is not inherently risk-reducing: a covered call or a cash-secured put selling strategy has effectively the same exposure as outright equities in a sharp market selloff.

Various competitors in Hedged Equity such as JP Morgan, SWAN, Gateway, Calamous, Parametric, and Innovator have had difficulty in recent years delivering their stated objectives. We believe their underperformance is the lack of price sensitivity on the options they use to deliver their stated investment mandate. These strategies have caused a combination of a simple imbalance of near-dated volatility supply and long-dated put demand. Over time, this has led to their products’ underperformance in both market rises and declines. Having simply overpaid for put exposure on the one side and having sold calls too cheap on average on the other side.

So how do investors improve risk-adjusted performance in hedged equity? The answer is: Combine an alpha strategy, that has negative correlation to broader equity. Moving alpha and porting that atop beta has been called portable alpha. Capturing positive returns in upside equity markets (positive up capture) and capturing positive performance or at least flattening downside beta returns in equity downside (flat to negative downside capture) is ideal.

The vast majority of managers are upside down on up-down capture. Hedge funds and various alternative strategies with a negative up-down capture spread, rely on persistent positive equity upside for returns. In other words, this is typically achieved by underperforming funds through a combination of participating in less of the upside (bad) and more of the downside (bad) equating to a negative up-down capture.

The term “outcome-oriented investing” started to be used many years ago, to allow managers to explain away poor performance relative to simple, suitable benchmarks and strategy substitutes. In Figure 1 we show how a simple strategy substitute of 50% S&P 500 TR + 50% Generic 1M T-Bill Return has recently outperformed notable JP Morgan strategies, tickers JHEQX, JP Morgan Hedged Equity and JEPI, JP Morgan Equity Premium Income ETF. Note the dashed blue and green lines underperforming the 50/50 strategy substitute

The market through option pricing is punishing investors of these products and giving opportunity for more skilled traders to achieve true alpha for investment programs. As a reminder, in decades past these types of strategies had the potential to outperform generic equity benchmarks. Today, however, not even a reduced benchmark hurdle of a 50/50 benchmark seems to be easily outperformed. The massive trade flow, supporting hundreds of billions in AUM growth without a doubt, is contributing to a large and still growing structural dislocation in options markets.

As with all risk premiums, the attractiveness ebbs and flows.

Pre-GFC, short-term options tended to be expensive. As a result, harvesting a volatility risk premium and hedged equity strategies were attractive and backtested well pre-Global Financial Crisis (“GFC”). Post-GFC, both retail and institutional demand for option-selling strategies and hedged equity started to grow rapidly. Large groups of investors are now viewing both as an “evergreen” asset class for income generation (“Derivative Income”) or a liquid alternative (“Hedged Equity”). Or at least that’s what we see marketed everywhere. Table 1 shows the degradation of the up-down capture for the HFRX Equity Hedge Index, +31.6% (excellent) ITD through GFC versus -8.7% Post-GFC (bad)

Call and put write strategies have roughly the same downside risk (down capture) as underlying equity market risk, with much less positive return in up markets. In a portfolio context, adding negative convexity strategies with low returns to the upside and high correlation to risk assets to the downside is not additive to portfolio performance. An investor should always think about the portfolio context and the opportunity cost of capital, the value potential.

Up-down capture is important, not only to produce superior performance but to have any potential of outperforming relevant benchmarks. Or said differently, knowing your manager is not destroying value potential.

So are you being misled, with all the marketing spin? Maybe. This is a market after all and we do encourage different points of view. So here is ours:

• COVERED CALLS ARE NOT A FIXED INCOME ALTERNATIVE
• COVRED CALL AND PUT WRITE STRATEGIES ARE NOT DEFENSIVE EQUITY OR HEDGED EQUITY
• THE VOLATILITY RISK PREMIUM IS NOT PERSISTANTLY POSITIVE OR ATTRACTIVE
• BEWARE OF THE TERM “DERIVATIVE INCOME”, NEW MARKETING SPIN

Opportunity for skilled traders? Unequivocally, yes. And that is the point of this analysis, creating better Hedged Equity and S&P 500 outperformance.

ALPHA MATTERS.

We are undeterred that, finding repeatable sources of alpha, attractive risk adjusted return is best found by providing liquidity to price insensitive end users of derivatives. This is a core belief of any skilled trader and a good starting point for any long-term investment strategy, so should come as no surprise.

A tax specialist’s perspective

Brent Sullivan raises concerns about the return of capital tax hack derivative income ETFs may employ to distribute tax-free income (LinkedIn)

A smattering of moontower thoughts

Options are always about vol. I co-sign both Mark and QVR’s message. No opinion on vol? Then you are just shifting around utility curves. Maybe that’s worth the fees, short-term tax treatment, and execution slippage*.

But “income” is a dead giveaway that someone’s bumhunting.

If the managers want to say they’re in the alpha game and not some poorly-defined risk premia sport then I agree with QVR we should see it in the up/down capture (and that’s what I want to explore more myself).

The broader asset-management and retail world lags the alpha world by a generation. Perhaps in 20 years, any fund focused on options would have to disclose how their vol p/l looks benchmarked to some naive beta-esque expressions of vol trading. Right now you can imagine an option relative value trader in a pod being forced to benchmark their vol buys and sells compared to if they had just bought or sold SPX vol or a basket of liquid singles’ vols in an attempt to measure their vol “idio” or skill. Then you can really study whether they are good at timing, sizing, or selection.

Further reading that drives home the “options are always about vol” message:

• What Part Of Selling Calls Is “Income”?
• Selling Calls: It Might Be Passive, But It Ain’t Income
• A Winner That’s Really a Loser
• Dynamic Hedging & Option P/L Decomposition

*Some option traders have mentioned that often the large predictable option fund rebalances will trade mid-market and not even disturb the surface. Well, let’s just say that 20 years ago the market would get leaned the day the makers expected the execution. Today, the table gets set earlier and earlier. The periodicity of maturity cycles and flows makes option traders feel like they’re riding a longboard during the dull times. Slide down a bit, flatten, slope back up the wave, re-catch the energy, make your move, snap back down.

That mid-market execution probably has a few days of slippage baked into it. Harder to detect. But nobody’s job is to stand there and slurp risk for free. Trading firms’ results are coming from someone (actually everyone, but some more than others).