Trading Insights: Intraday options, levered ETFs and the retail investor

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Matthieu Boisot: One factor which is even more complex is the fact that a lot of people are trading what we call zero DTE, so zero day to expiry options. These options are really short dated, a few hours to live, and hence have a lot of gamma. So we are trying to gather all this info on a live basis. If you're able to do that you can preempt some of this flow and be able to position yourself in the right direction ahead of the close.

Eloise Goulder: Hi, I'm Eloise Goulder, head of the Data Assets and Alpha Group here at J.P. Morgan, and today I'm so pleased to be joined by Mathieu Boisot, who is head of Cross-Asset, Volatility and Intraday Product Development for our QIS business. So Mathieu, thank you so much for joining us here today.

Matthieu Boisot: Thanks a lot for having me here.

Eloise Goulder: And Mathieu, I'm really looking forward to this discussion because there have been so many developments in your space which are so relevant to markets today when it comes to the rise of the retail investor, the prominence of zero-day-to-expiry options and levered ETFs and their impact on gamma imbalance and intraday moves. And I know this is really important to your business. It's also very important to any investor who cares about intraday performance and intraday alphas. So Mathieu, you do have a particularly long title. So could you also explain exactly what your role entails?

Matthieu Boisot: So we are developing investment strategies for clients and my focus is really on the volatility side. So I'm developing strategies that are using instruments like Option, Variant Swap or Intraday Data to give access to clients with Premier or some strategies that are replicating some well-known strategy, typically edging via put-freight option buying.

Eloise Goulder: And what are the various product lines that you look at?

Matthieu Boisot: So I like to see it as a matrix on my side. To start with, we have the asset class component, covering five asset classes. Equities, credit, fixed income, commodities and FX. Then the second one will be the type of strategies that we recover. I have the carry, which is a typical implied to realize volatility carry. So the second one would be hedging, typically your long equity and you want to buy some protection via put buying or put-freight buying. A small variation of edging would be tail edging, where they would be potentially playing some different part of the implied parameters. Typically, they can pay long skew via long put ratio and so on. The fourth one would be relative value trading. So alpha generating strategies that are a bit less correlated to equity markets. One good example would be dispersion trading, where you go long volatility on single names and you sell volatility on the index. And typically, you benefit from the fact that what we call implied correlation, so the correlation, which is pricing in the volatility of the index, tend to be higher than the subsequent realized correlation. The fifth bucket will be trading strategies or pure alpha.  I think one good example here will be intraday strategies that have been quite popular over the last few years. And we have a third dimension, the client type. We cover a wide range of clients coming from private bank, asset manager, insurance company, pension fund. And more recently, we have seen as well, hedge fund coming to us and asking what kind of strategy we can offer them. So we end up with a three-dimensional matrix, asset class, type of product, and type of client.

Eloise Goulder: And I know that your business has grown enormously over the years. So can you talk to which of those pockets were perhaps the earliest to originate, and which of them have been growing more recently?

Matthieu Boisot: Sure. It evolved quite a lot over the last 15 years. We used to trade, let's say, variance swap once a month. And then we moved to daily execution of variance swap. Then we moved to replication of a variance swap. And then now we have much more intraday strategies where we can trade variance on an intraday basis. We can delta-edge on an intraday basis. So it has become more and more complex. And this was, coming from two factors, spending more money in technology, improving the execution capabilities, and clients as well being a bit more confident within the QIS product and going one extra mile in terms of complexity.

Eloise Goulder: You mentioned that an element of recent growth in this space has been a narrowing of time horizons and the growth in intraday strategies. So can we just hone in on that? I mean, even in the linear community, I'd argue that it's become impossible to ignore what's going on intraday. If you observe a consistent intraday momentum or reversal, the chances are, as far as I understand, it's a function of the gamma imbalance. And therefore, you need to understand the gamma imbalance. Matthieu, is that right? And can you describe what's going on here?

Matthieu Boisot: Yeah, it's totally right, if you look at the time horizon, it has really compressed whatever used to be more longer dated has now narrowed down to really short dated one day, even shorter than that. Let's take a simple example. You're a market maker, you have sold a call option. So as a market maker, you're delta hedging this call option. So if the market goes up, you need to buy more delta. And typically, what you will do, you will wait until the close of business day to buy this delta amount. And hence, if market has been up, you will buy a bit more exacerbating this move. This is what we call short gamma, because you have been short on option. An option is long gamma. So you're short gamma because you have sold this option, which means if the market goes up to hedge yourself, you need to buy. And if the market goes down, you need to sell. And this short gamma imbalance has created historically this intraday move. And this is not simple because market maker can sell an option or buy an option. So you need to understand where is the positioning. And this positioning can vary based on other factors. The first factor, where is the market right now? Because at the end, you will net all your position to do your delta hedging. So you need to have a live gamma map telling you, OK, if the spot is there, I'm long. If the spot is there, it's going to be short. If I'm long, it's going to create intraday middle version. If overall, this short gamma is going to create intraday momentum. So that's one of the factors you need to understand where the market is. The second factor is you need to understand what is driving this gamma positioning, long or short. And there are two factors. There is an option but there is as well levered ETF. And a levered ETF, by nature, is going to create some short gamma exposure. If you're short a 2x levered ETF, if markets go up, you need to reinvest a bit more simply because your NAV, your NAV, has increased. So you need to buy a bit more. So you create this short gamma imbalance. The second driver of short gamma or long gamma positioning will be option trading. We have seen a trend into going into even more short-dated option. Short-dated option have more gamma. We used to have Friday option for more than 10, 15 years. 2016, we started listing Monday and Wednesday option. And in 2022, in April and May, respectively, there was some listing of Tuesday and Thursday option. So now, for S&P option on any day, you will be able to trade the same day option or an option expiring over the next 1, 2, 3, 4, 5, etc etc., business days. And this has really created a shift toward really short-dated option, where the volume used to be more on the monthly option, quarterly option. Now the volume has shifted to a really short-dated option. Roughly 60% of the volume is going to be less than one week to expiry, which means a lot of options with a lot of gamma. One factor which is even more complex is the fact that a lot of people are trading what we call zero DTE, so zero day-to-expiry options, so options which are expiring on the same day. These options are really short-dated, a few hours to live, and hence have a lot of gamma. You have a lot of assumptions. How do you model an option with only a few hours to go? Is Black-Scholes the right model? So there is a lot of difficulties here to assess the gamma of this option, as well as to understand if they have been bought or sold by the investor.

Eloise Goulder: It's absolutely fascinating. So if I understand it correctly, there are really two drivers that are leading to more gamma potential within the markets. One of those is around options and the fact that options maturities have become shorter and shorter dated, catalyzed somewhat by the S&P 500 now having options expiries every single day of the week. Those S&P rules only changed in 2016 and then 2022. So this really is quite a recent phenomena. And then the other driver of the greater gamma is the fact that you now have more and more levered ETFs, two times levered ETFs, for example, which operate a little bit like a call in the sense that they are associated with higher gamma. And it's many market participants, including the retail investor who are trading those levered ETFs. So when you put it together, you've got shorter dated options and you've got more and more levered ETFs, both of which are creating greater potential for gamma to be built up on the long side or the short side on any given day. And this is important because it impacts intraday movements in markets. If market makers as a whole are net-short gamma, it is likely to create an intraday momentum signal. And if market makers as a whole are net-long gamma, it's likely to create an intraday mean reversion signal, both of which can be very significant when you're trading any sort of short term strategy. And I guess the key question from an alpha generating perspective is where can you find this information or this approximation of what the gamma is? And therefore, can you have an edge on what the market moves are likely to be intraday and how successful are those strategies?

Matthieu Boisot: Yeah, of course, you can have an edge if you're able to monitor this and to create a proper gamma map, you can use it to be able to distinguish between strong intraday momentum, no real trend and strong intraday mean reversion. I think there are two parts in estimating this kind of gamma imbalance. One is a lever DTF. This one is probably the simplest one. You can screen all the lever DTF, look at their asset under management and define what's going to happen at the close if market have moved X percent. The second part of the equation is a bit trickier because we are speaking 0DT, 1DT and so on. And what you need to be able to do is record all trades on this space, define if it was a buy or a sell. And you need to do that on a live basis simply because a lot of this gamma imbalance is coming from 0DT. And clearly, if you record with a bit of lag, this option will have expired or will potentially be unwind. So you need to be extremely efficient in your calculation to be able to draw the line between intraday momentum and mean reversion. So we are currently trying to gather all this info on a live basis. But clearly, if you're able to do that in the cleanest fashion, you can preempt some of this flow and be able to position yourself in the right direction ahead of the close. But what we have seen as well is market makers tend to anticipate this kind of effect. So they will probably not wait until the close close to rebalance. They will probably start trading tens of minutes ahead of the close to avoid this effect and they will probably try to reduce the potential impact that they could have. So it's a kind of game theory play right now. You need to anticipate, but not anticipate too much. I think what's important is all this flow driven premia. You need to make sure you understand why they're happening. What are the flow behind them? Can you observe this flow in a clean fashion? And once you have that, are you able to trade based on this signal? So it's a mindset that we have been developing on our side, trying to understand why is potential premia, how we can analyze it, understanding, and potentially harvest it.

Eloise Goulder: And it's worth noting that our research colleagues create estimates of options gamma imbalance on a daily basis. So that product is available to our investing clients as well. But Mathieu, just to clarify, by region, are we speaking most significantly about U.S. markets or other regions? And by security, are we talking specifically about U.S. indices like the S&P 500, or are we talking about single names?

Matthieu Boisot: So in terms of markets, is more U.S.-focused. In terms of the names, the levered ETF will be more on some of the single names, which are really topical, mainly tech-centric. but this has repercussion on S&P as well.

Eloise Goulder: And while to the extent that you're trading in today, it's really important to understand the gamma imbalance to understand whether you're going to see the momentum or the mean reversion. Is there a general rule of thumb that is helpful for the investing community to be aware of? Like most of the time the retail investor is buying options and therefore most of the time the market makers are selling options?

Matthieu Boisot: Historically, there was some kind of heuristic, the one our research looked at like people sell coal for income and they buy put for protection. I think the story has shifted a bit. First, we have retail investors who are now quite a big part of this market. And the direction, I would say, tend to be on the option buying. But on the other side, it's difficult to estimate how much is coming from retail, how much is coming from institutional. So it's always a big question mark on the direction. And unfortunately, this gamma can be so high for really short-listed options that if you get it wrong based on heuristic then you start having some bad surprises. And we had some events where retail came in to buy the dip where even if you are right in your direction, a buy the deep event, would have completely reverted the move as well. So exogenous things can change the direction of the trade quite quickly as well.

Eloise Goulder: And it reminds me how important it is to understand the why behind any given market move. In this case, the why there might be a persistent intraday mean reversion or momentum within markets. And perhaps it's also a warning against simplistic machine learning models where you are not aware of the why. So for example, if you note a consistent mean reversion in markets over a number of months or even years, one might assume therefore the market's always mean reverting intraday. But in fact, they may be mean reverting because of the market make a long gamma position over those particular months and years. And of course, it can reverse. And if you didn't understand the reason behind that mean reversion in the first place, i.e. what was the options gamma imbalance in the first place, you wouldn't really have a durable, sustainable, systematic strategy.

Matthieu Boisot: Yeah, exactly. I mean, this gamma map can switch quite quickly depending on where the spot is.

Eloise Goulder: So Mathieu, before we close, we always ask our guests what's next? What are you working on? What's next on the horizon?

Matthieu Boisot: I think there is quite a lot happening. As you said earlier, it's really a business which is in expansion and we are lucky we got three years ago a new market instrument in the S&P space with Tuesday and Thursday options, which offer us the possibility to start doing a lot new type of strategies. This is something which is happening in other instruments. We had this on Eurostox. Hopefully the liquidity will pick up, We started to have some listing any day as well in the fixed income space. I would like to add one dimension, I think one asset classes we are missing is cross asset volatility strategies. So typically you could think of S&P versus commodities, equities versus fixed income as a relative value, equities versus credit. So there is a lot more we can do rather than looking at it as a siloed asset classes, going back to my title that you thought was quite long. I think it's quite nice if you can have real cross asset volatility strategy where we mix asset classes within one QIS product.

Eloise Goulder: Absolutely. Well, Matthieu, it's such a complex area, but also one that is so important to understand to the extent you care about the retail investor the rise of the retail investor, market structure, and importantly, the impact of all of these flows on intraday market moves. So Matthieu, thank you for helping demystify this topic with us today. I've really enjoyed it and I hope our listeners will too.

Matthieu Boisot: Thanks a lot for having me here. I really enjoyed the conversation with you, Eloise.

Eloise Goulder: If you'd like to learn more about our QIS strategies, then please do reach out to your sales representative. Otherwise, if you have questions for our team, then please do go to our website at jpmorgan.com/market-data-intelligence. And there ou can always reach out via the contact us form. And with that, we'll close. Thank you.

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