Garrett DeSimone, Head of Quantitative Research at OptionMetrics

[00:00:00] Hello, this is Dean Curnutt and welcome to the Alpha Exchange where we explore topics in financial markets associated with managing risk, generating return, and the deployment of capital in the alternative investment industry. Earning a PhD in financial economics is no small feat, and not only did Garrett D.

[00:00:25] Simone do just that, but he would unknowingly embark on his future career in the process of doing so. His dissertation from the University of Delaware involved the study of event risk premia in single stocks ahead of earnings, and to perform the analysis he engaged with option metrics,

[00:00:41] a firm specializing in implied volatility data. Now the head of quantitative research there, Garrett leads the firm's efforts to deliver carefully constructed datasets to its client base while generating original empirical studies of option pricing and trading strategies.

[00:00:57] Our discussion considers some of his work, starting with his dissertation and the finding that the earnings event risk premium for single stocks makes straddles punitive to own. We liken this to a more recent phenomenon at the index level, the inflated one-day

[00:01:11] S&P 500 implied vol levels that have occurred in days before three macro events, the CPI, the non-farm payrolls report, and FOMC meetings. We talk as well about one-day options and the risk of a blow up.

[00:01:25] At least at this point, Garrett sees flows that are reasonably mixed with no obvious risk of instability resulting from positioning. Lastly we discuss recent work he's done on implied dividends using a novel approach. Relative to years earlier, he finds that there is currently very little risk premium

[00:01:41] implied in dividends. That is, the market is charging almost nothing for bearing the risk that dividends wind up disappointing on the downside. It's interesting work and a good example of the rich information that can be extracted from derivatives markets.

[00:01:55] I hope you enjoyed this episode of the Alpha Exchange, my conversation with Garrett D. Simone. My guest today on the Alpha Exchange is Garrett D. Simone. He is the head of quantitative research at Options Metrics, a boutique here in

[00:02:10] New York City doing all kinds of interesting things on the vol and data analysis front. Garrett, it's great to reconnect. It's great to have you on the podcast. Thanks for having me on, Dean. I'm excited to be here. Looking forward to the conversation.

[00:02:22] We've known each other for a bit now and I was lucky enough to speak at your conference. I want to say it was just before COVID. So quite a different world today than back then five years ago, but you've been at

[00:02:34] Options Metrics seven years and the firm has been around for 20. So we'll have a lot to talk about in terms of the evolution of the product that Options Metrics puts out and how it serves its clients. Let's get started with your own background.

[00:02:49] Tell us about your pathway to Options Metrics. I know you've got a PhD from University of Delaware. Tell us about what sparked your fascination with markets and finance. Yeah, absolutely. Thanks for the intro, Dean. I started at the University of Delaware doing my PhD in financial economics actually

[00:03:06] using Options Metrics data for my dissertation. So it was a very natural fit. And at the time, at least in my program, you didn't have a lot of students really handling options data because of the level of sophistication, the size.

[00:03:20] But I ended up doing my dissertation on essentially these volatility risk premium clusters around big economic news days. So the whole idea without getting too deep into the details is the variance risk premium, which is basically the price of risk for the price to ensure against volatility risk.

[00:03:40] We measured that by looking at the returns on straddles on macro news days, mainly CPI and FOMC, as well as labor news. And we found that the concentration of variance risk on these days was higher as opposed to on all other days.

[00:03:57] So if you were a long straddle holder, a majority of your losses would just accumulate on these three or four days that occurred during a month. So that was kind of my intro into options data.

[00:04:09] And then it was a very natural fit coming out of school to find myself here at Option Metrics. Well, it's so interesting your PhD work around this event pricing, the macro event pricing. And a little blurb I put out this morning before this important CPI

[00:04:25] release that came out was just looking at the largest one-day percent moves in the one-day VIX, which is of course a new thing, but it's kind of an interesting way of measuring this risk premium that you speak of.

[00:04:36] And all of these big moves that are the day before either CPI, non-farm payrolls or FOMC. And then of course, what you see is when the news comes out, the air comes out of the tire, the option premium comes down.

[00:04:52] So on the day of the data release, the VIX one day consistently comes down and usually comes down by a lot. So that risk premium comes out once the news is out. It's really interesting. Did you find that the market systematically overpriced the event risk premium?

[00:05:10] What was the empirical result of your analysis? Yeah. So my result basically said it's a realized risk premium. So it's just the return of Delta neutral straddles. But what we found is if you were to form two separate strategies, one

[00:05:25] that just bought straddles on the FOMC days, on the CPI days and on the unemployment number days, you would see that your losses were in excess, far in excess of all non-event days. I forget the numbers because we're talking, you know, six or seven years

[00:05:42] ago and we've got a bunch of new data to look at, but if you had looked at the holding return of Delta neutral straddles, you would see that if you had just bought a non-event days, those losses are almost zero

[00:05:54] compared to on these event days where you experience substantial losses. This also occurred in the VIX futures too. So we had it on the SPX straddles as well as if you were long VIX futures.

[00:06:05] And in terms of the result we kind of got at, or I got it was it's really just a risk compensation idea. And it really ties to the fact that there's a lot of news that comes out

[00:06:17] that really affects the level of volatility and the level of risk conversion in the market. But the end of the day, it really just comes down to macro news really pushing the VIX up or down, whether it's structurally higher or not. Yeah, it's so interesting.

[00:06:29] You get these large one-day implied moves. You were ahead of it. The sort of macro implied one-day move has really become a thing. I would say by mid 2022, when it was very clear that the inflation dynamics were moving in a market unfriendly way, the price of

[00:06:48] optionality the day before things like CPI and FOMC, especially in 2022 really got inflated. I mean, you had, for example, in January of 2023, the one-day VIX closed at 38 the day before the CPI release. In December of 2022, it closed at 47. So some really giant levels of risk premium.

[00:07:15] And then what's interesting is the market seems to have learned its lesson along the way, which I think is some of what your research concluded, which is there's only so much you can pay whether you get

[00:07:25] the move or not, the incoming price that you pay for that straddle really matters. It makes your margin of error not that much. So you've got to really get the big move. I'm wondering, and I know you've written a fair amount on earnings.

[00:07:39] And if there's any corollaries you can speak to in terms of how the market prices vol ahead of earnings moves, what is your research showing you in that realm of one-day pricing? Yeah. So it's really a similar story to the macro finding we had.

[00:07:58] If we look at, let's say Apple earnings or big tech name earnings, almost in all cases, the implied volatility is going to be above realized vol, meaning you're going to have losses to long volatility positions.

[00:08:11] And if we look kind of across the board and we want to narrow down, it really occurs in these headline names that have a lot of interests in them, stocks that tend to have a lot of analyst disagreement and pushing up vols.

[00:08:24] So in general, I always say it could be an extremely unprofitable strategy to be long vol into earnings across the board. Whereas you look at these strategies post earnings, it's a very different picture while you still get some losses.

[00:08:37] It's not as significant as you would see if you were simply buying straddles or forming some sort of delta neutral position into the earnings news. Yeah. I always think about the clearing price of very short dated optionality, especially as it's just a moment in time when a buyer

[00:08:54] and seller come together. And if I'm the seller ahead of something that's especially uncertain, maybe I'm just demanding an especially high level of risk premium, right? Where I just, I'm so unsure. And maybe the aggregate supply of risk bearing capital, i.e.

[00:09:13] the sellers of vol, maybe there's just not enough of it such that the market clearing price is just going to be high. Right. I mean, it's just compensation for bearing really short dated uncertainty. I just wonder because it's an interesting result, right?

[00:09:28] We know that over long periods of time, vol has a couple of characteristics. One, you could lose a lot. We know the history of that, but you much more often than not are making that vol risk premium.

[00:09:40] So that's one aspect of vol, but it seems like both in the macro side of things, your PhD thesis, and then also as you discuss the earnings side, it seems like the vol risk premium is especially high into the event.

[00:09:55] Is that fair to say that that's the conclusion? Yeah, absolutely. And we should get a little more specific too. It really occurs in these very short dated options too. That's where the majority of these losses accumulate. And one of the interesting findings I had during my dissertation was

[00:10:13] that if you looked at FOMC news, you actually had positive straddle returns. So you had these backward looking measures in CPI and unemployment, which generally yielded a negative risk premium. And then you had the FOMC, which for some reason, when we

[00:10:33] point to a few in some of the papers we wrote, I wrote with Paul Lau, my dissertation advisor, some of the reasons this could be. But it was a very interesting result. It was possible that these positive returns, straddles were due to some extreme measures during the GFC.

[00:10:49] But I think it's of interest now, too, considering how important the number of cuts that the Fed is going to be looking at this year and going forward in 2025. Well, let's go back. Let's use your PhD dissertation and the data

[00:11:03] requirements of it to pursue that research and how you came across options metrics. And perhaps that's a good way of introducing the firm, what it does. And then we can talk specifically about your role in it. Just tell us a little bit about the business of option metrics.

[00:11:23] Yeah, absolutely. So option metrics, we've been around for about 25 years now. Our founder and CEO, David Haidt, started it way back then in the infancy of the options market. And initially we provided very clean, complete options data for U.S. Over the last 10, 15 years, we've expanded to other

[00:11:47] regions, Europe and Asia, as their markets have developed. But the focus in the last couple of years has really been developing tools that our clients can use that are extracted from the options market. So these are implied analytics that may

[00:12:01] provide some benefit to our clients that are not necessarily easy to compute and really just getting a sense of market positioning from dealers and also inferring risk neutral or some sort of risk premiums that are baked into the options market.

[00:12:18] So that's going to give us a lot to talk about when we talk about the concept of implied vol data. Anyone can go on the Internet and just Google Black Scholes Excel spreadsheet and something will pop up that you can use.

[00:12:33] And so we all know that closed form solution, but it's a lot more complicated than that in terms of actually deriving clean, consistent option implied vol data in the context of markets that are littered with frictions. Right. There's just a lot of complications that may not seem

[00:12:53] like a big deal, but it's a heavy lift to really develop a database that folks can rely on is analytically consistent. And I'd love for you just to give us an overview as to not to get too far in the weeds because I know that'll go far.

[00:13:07] But some of those challenges, some of what a firm like option metrics is doing in order to derive the data set that clients can really rely on. What are some of the complicating factors and the ways in which a firm like yours addresses them? Yeah, absolutely.

[00:13:24] So if we go back to the nature of pricing an option, it requires several inputs. So we've got to assume some sort of risk free rate. We've got to assume if it's a single name option, a dividend stream, we've got to have the characteristics, strike, top maturity.

[00:13:41] So we need to lay out all of these inputs first and we need to make sure these are clean inputs we're receiving. And then you mentioned Black-Scholes. That's our easiest case. Right. So if you've got an index option, we can put that into Black-Scholes.

[00:13:54] We can extract the dividend yield and price an option that way. But a majority of our database consists of single name American style options. And these options require trees to account for early exercise. And any student that's been in a graduate finance program

[00:14:10] or finance PhD program has walked through probably a tree by hand like I did. But we're calculating this for the entire US universe and providing it nightly. And ultimately, we're going to move to an intraday product. But this is actually a complicated numerical lift

[00:14:28] because we've got to make sure we have enough nodes on the tree. We've got to make sure our dividends make sense, that our corporate actions are correct. So the first step, which makes it not an easy task for, let's say, someone outside of option metrics to do

[00:14:45] is pulling in all these inputs, making sure they're clean, ensuring that the volatilities we get are reasonable. And then also, we construct some tools with our US and EU and Asia products, which provide smooth surfaces. So making sure these are error-free.

[00:15:00] So there's a lot of different steps from just receiving the input data and saying, well, you can extract the volatility out of it. But can we do that across the entire optionable universe? And can we do that very quickly?

[00:15:13] And can we do that in a way that's relatively error-free? And there are beautiful vol surfaces like the S&P, super well-defined across time and strike. And then in singles as well, you have Apple and Nvidia and Microsoft and so forth.

[00:15:30] Those have got tons of volume, again, across a lot of different strikes as well. But then you have many, many stocks for which the options market is fairly illiquid, where the trade-by appointment is probably the best description sometimes. What are some of the challenges there?

[00:15:48] How do you derive a vol surface that someone can look at and say, OK, this makes sense even in the context of something that's not trading with the incredible frequency of Nvidia or Apple? Right. So one of the ways that our clients typically look at it

[00:16:02] is fixed delta, fixed maturity surface. So we do what's called kernel smoother without getting too technical, but it generates a relatively good bubble around that delta and maturity point to give a relatively good estimate of what the volatility should be for let's say a 50 delta 30 day option.

[00:16:22] And our clients have been very happy with that. As we have developed our research capabilities, we're actually looking to build out more arbitrage-free surfaces. So our current methodology kind of takes the vols as they are. We don't make really strong assumptions regarding

[00:16:38] the price it should be and then how it puts a surface. Going forward, we're actually going to make sure it's globally arbitrage-free. There are some models out there that do this, but this is also not a straightforward lift either

[00:16:51] in the sense that we've got a bunch of equations that we're trying to minimize, trying to make sure every option chain obeys calendar R, butterfly arbitrage. But going back to the liquid options questions, for securities that have decent liquidity on it,

[00:17:08] our current methodology does a very good job. But there are some stuff that it just doesn't matter what model you're using, you're not going to get a good looking surface. Right, for sure. Well, these option markets are always changing and boy, these exchanges, they're money hungry.

[00:17:25] They're coming up with more and more new products and certainly more and more expirations. I think we're all waiting for the intraday expiration. I'm not even sure that that's a joke. Every day is not a bad description of option expirations. And so this zero DTE thing has been

[00:17:44] the subject of a lot of discussion, a lot of debate. We're always interested in whether something that explodes in terms of popularity and usage creates a risk scenario for markets. We've certainly seen instances where derivative exposures impose themselves on the underlying asset markets, right?

[00:18:05] At the tail wagging the dog scenario. And so that's been the subject of a lot of debate amongst participants. And I know you've done some work on it. So maybe just give us the big picture from your perspective of how you've approached looking at zero DTE,

[00:18:23] the things you've sought to try to answer for yourself. And perhaps some of what you've uncovered that might agree with the common narratives and then might be at odds with the common narrative. Sure. So we've actually looked at what kind of behavior,

[00:18:41] how are institutions really utilizing these tools by taking one of our products, which is the sign volume data set, to get an understanding of what the aggregate buying or selling behavior is throughout the day. And generally what we see is that the zero DTE behavior

[00:19:00] starts in the morning that we see that investors actually tend to be, or institutions tend to be selling a long zero DTE. Typically in complex strategies, we don't really see it being single leg trades that they're engaging in, but rather multi-leg trades.

[00:19:15] And by the end of the day, these books are relatively balanced. So by the time the auction market closes, we've seen that the market maker has zero net inventory of zero DTEs, which aligns with sort of what CBO has been saying that these instruments don't really have

[00:19:32] a large systemic risk component to them. I've always been of the view, and I mentioned this a few times, that zero DTEs have been around for, I guess, it's maybe a year and a half, close to two years now, that we really have not had an environment

[00:19:47] such that the liquidity has been completely sucked out or we've got a sustained period of 30 plus VIX, which is where these systemic risks tend to pop up. And the question is, a tool like this could work majority of the time, but it only takes one or two days

[00:20:04] where the market maker's book is completely imbalanced to throw markets totally out of whack. And that's sort of what we saw during Valmageddon back in February of 2018. So my view has always been like these tools work well now, but you've also got to understand will they work well

[00:20:22] when markets are not functioning properly. So that's something that I've always been interested in cautioned against. HOFFMAN Mandy Tsu, who is the head of market intelligence at the CBO and an equity derives expert was on this podcast. And we had a discussion around what her investigation had found.

[00:20:42] And I was particularly interested because I think the CBO has got as bird's eye a view as to the implementation of these trades, whether they're opening buys, sells. And I think a lot of her commentary pointed to the same conclusion, which is a lot of these are spreads

[00:21:00] and they're spreads that are held and then they're spreads that are unwound. And I always ask myself if you had, I don't know, a put option on the S&P that was traded a gazillion times a day,

[00:21:10] but closed out before the end of the day, certainly looks like a ton of risk, but I'm just not sure there is. If it's just being flipped back and forth like a ping pong ball,

[00:21:20] I'm not sure if the comparison is similar as to either a buyer or seller warehousing this risk. And I'm curious what you think. And the reason we're interested in zero DTE is because the volumes are very high, but does high volume necessarily really mean high risk?

[00:21:39] I guess is my question. Well, with these things, this is me speculating a little bit, but where I know there's some ETF providers already in the discussion of zero DTE ETFs, right? So now you've got essentially a derivative built upon a very risky derivative

[00:21:59] that relies on the liquidity essentially of this zero DTE market. And this may not pose risks for three or four or five years until these ETFs become popular or heavily traded, but it does give me the volumigant sense, right?

[00:22:17] Where you've got a short volatility linked product that essentially has to rebalance using these underlying options. But as it stands right now in this low liquidity environment, it doesn't seem like there's too much worry going around.

[00:22:32] I think with systemic risk, it's always when it'll go wrong at the worst possible time. That's when you need to be fearful, right? So when you've got two very serious events going on, let's say it's some sort of

[00:22:44] financial crisis, but then you've also got this derivative linked product that's not behaving as it did in the past. So I think that's the nature of systemic risk. When we think about the period leading into Valmageddon,

[00:22:57] it was an almost unimaginably low level of realized vol in the S&P. I think in December of 2017, we realized south of five, there was just day after day after day where you barely scratched out a 20 or 30 basis point move in the index

[00:23:15] on a close to close basis, sometimes on a high to low basis. It was really something. And you couldn't help but think, boy, that's the gamma well, right? That's the market's just got too much of this stuff. And hedging stuff from the long vol side, mutes the vol.

[00:23:31] So we always kind of point to the derivatives market sometimes, I think a little bit too often. But these days you don't have a daily resetting short fixed product. That notional is a small percentage, small fraction of what it was by the end of 2017.

[00:23:48] But you do have the growth of the income generating mutual fund, the income generating ETF. And I'm just curious to get your perspective on that. All these cycles are different. The products that get introduced are new, but you do have quite a fair amount of short vol

[00:24:06] that's in the system trying to earn some version of coupon, I guess you'd call it. It's trying to generate money by selling optionality. How do you think about all that? I think, and this might go back to the conversation around why is the VIX been

[00:24:24] so low lately and why we have these reduced risk premiums across the board, this kind of yield seeking behavior and option selling behavior to capture yields. But I think it ultimately comes back to the macro picture, which is that we've got this soft landing narrative.

[00:24:44] The markets, whether correctly or incorrectly, have believed that everything, if we look at the VIX, that everything is probably going to be okay by the end of the year. I think it really ties to the fact that this low VIX level we're seeing is just investors

[00:25:00] not believing that they're, or not having a ton of risk aversion, or that they're very confident that the Fed is going to be able to get inflation under control and unemployment to remain stable. Markets are no free lunch. Option markets are generally no free lunch.

[00:25:18] You get what you pay for. Right now you get a 12 VIX, but you also get eight realized volume S&P. It's what you're saying. The behavior of the market is one of calm and that reflects some degree of maybe consensus

[00:25:34] around a benign outcome even as this election is coming. But there's certainly a lot of interest in looking at the price of insurance and saying, boy, maybe this is a real good time as the skies are sunny. Right now, the price of the umbrella is really cheap.

[00:25:51] Perhaps I'm really supposed to take a good look at hedging my portfolio, maybe using some VIX calls or S&P puts. As you've, I'm sure run countless back tests over the years and as one of your conclusions was buying a Ventval is especially expensive.

[00:26:12] Are there any things you've found that mitigated the cost of hedging, i.e., a mousetrap that got you a little bit more bang for the buck in terms of here's what I'm paying for the insurance and then here's what I'm getting back in terms of your back tests?

[00:26:30] I can tell you what I have seen in terms of tail hedging, paying for downside insurances. If we think about the most traditional way it's thought about is buying out of the money put against your equity position.

[00:26:44] These do incredibly poorly as tail hedge risks because you end up paying for this volatility premium, it's a drag on returns and they're very difficult to monetize. So while you may time it correctly, let's say the monetization window is very small.

[00:27:01] And as it turns out, it doesn't act as the hedge that we would expect or that we've kind of been told traditionally. So I think one of the better hedges is really cash, honestly, as opposed to,

[00:27:15] as probably said many times before, as opposed to if we're looking at, let's say, downside puts or buying the VIX, you could essentially replicate the portfolio return of a tail hedged S&P position with allocations towards cash.

[00:27:31] So some equity plus cash, you could actually replicate that return without having to premium these large put premiums. So I think cash is a bit underappreciated in terms of tail hedge from all the studies

[00:27:45] we've seen with using VIX futures, long short, long volatility in the VIX is probably not going to generate excess returns for the same reasons as a long out of the money put. Timing is very difficult.

[00:27:59] You're paying this large premium that the curve tends to be in a contango. So I think if we think about it from like, if we're interested in hedging just a traditional equity position, cash seems to perform best. That's really interesting.

[00:28:14] There's no doubt that there is an option-like characteristic of cash and I certainly can't get away from a two-year note sitting here at 4.6% to 4.8%, depending on what day you look at it. So there's some positive carry element to that.

[00:28:31] You can sort of suggest that if you got a giant risk off, I feel like the odds are pretty good that that two-year note is going to rally in price and that yield is going to come down. So you're kind of getting paid to wait.

[00:28:43] It's an interesting concept. I've also been interested and I don't know if you've spent any time on things like vol contingent strategies, basically scaling in and out of the market depending on what the realized vol profile of the market looks like.

[00:28:58] So when realized vol is low as it is now, you're reasonably well allocated to the market. But some of these models effectively use a pop-in realized vol as the way of scaling down your exposure. So it's kind of raising cash as a function of realized vol coming up.

[00:29:16] The riskiness of the market rises and realized vol rises with it. And that's your signal in some sense to de-risk the portfolio and to raise cash. Some of those have some pretty good efficacy. They have option-like characteristics without actually spending premium, but certainly

[00:29:32] they're not pure insurance as a put option is. We've actually researched a few topics and you mentioned realized vol scaling. So we've done some portfolio research using implied vol scaling, but one of the interesting pieces we found and to get a little technical.

[00:29:48] But if we instead focus, if we've got a liquid stock, we've got a nice option chain that's built out with calls and puts. And we can essentially capture the downside variation of that stock.

[00:30:02] So instead of using just total vol to scale the portfolio, if we actually look at implied downside volatility, we can actually get a better result. And we've looked at this in the context of low beta and high beta portfolios.

[00:30:16] And we found that performs better than if we were to use a traditional vol scaling strategy. So that's interesting. So it was what you're saying that when some version of put skew emerges, that's got some maybe forward looking information content that can be used? Yeah, absolutely.

[00:30:33] We've done this kind of in a factor approach. So not really in the sense of just scaling, let's say the market basket, but if we've got a low volatility factor and we incorporated sort of the put skew, instead we found out

[00:30:47] performance in those as well as sort of a value factor. So it indicates to us that there's really two lines of thought regarding that. One is that the option market has kind of this information that the stock market doesn't

[00:31:00] in the sense that, well, the put skew went up, so there must be some negative sentiment around the stock, so underperforms. But the other aspect is when we focus on downside volatility, we're reducing essentially the second and third moment or rather the skewness risk in the portfolio.

[00:31:19] So we're removing those very large losses. So the skewness and kurtosis is able to be pulled out as opposed to when you just look at total vol, you're really focused on the second moment, which is just the standard deviation. All right.

[00:31:32] So you got a little wonky there, but now we're going to get even more wonky because as the industry of gamma bean counting is a thriving one, trying to map the profile of the market's exposure to gamma and try to derive some implications for how the

[00:31:48] index is going to behave. There's these second order, these Greeks as well that most people have never heard of. We've got Delta and gamma even Vega and Theta down. And with rates high, we even are talking about row, but there are these sort of

[00:32:03] second order Greeks that don't get a lot of attention, but you focused on some of it. One of them I'm going to say is called Vanna, not Vanna White, but Vanna. So why don't you talk us through what exactly some of these really fancy Greeks are, how

[00:32:20] you got interested in looking at them? And then maybe we can talk about some of your work and what you've been able to find. Yeah, absolutely. It's funny, Dean, anytime I'm doing research on that, seeing if there's academic

[00:32:32] research I'll put in Vanna and I just get 20 links to Vanna White. A real fortune. And maybe that's why it's so hard to find research on it. But yeah, the second order Greek Vanna, which you mentioned, does not receive a

[00:32:45] ton of attention, at least in academic circles to my knowledge over the last couple of years with the interest in gamma gravity and these gamma holes that we were talking about earlier. It's kind of popped up.

[00:32:59] And I think one of the reasons it hasn't gotten a lot of love is it's a very complex risk exposure. And the definition of Vanna is how the options delta changes with a change in implied volatility. So it's cross derivative. It's not a straightforward measure like delta.

[00:33:17] So in the sense that it's not really easy to understand. But one of the interesting things about Vanna is nobody really cares about Vanna in a low volatility environment. Because if we think about it, if a position has negative Vanna and the IVs go up or go

[00:33:34] down, the premium doesn't really change that much because we're in a low volatility environment. So where Vanna becomes potentially volatility exacerbating is when you've got these very high levels of volatility. Here's what I would say.

[00:33:48] I think if you are from a hedgerest standpoint, this Greek is actually very critical, right? Because you really don't know where that implied vol is headed. You know the market price at the time when you buy or sell the option, and it's going

[00:34:04] to be struck at a certain volatility. But if that volatility turns out to be quite wrong and moves around a lot, it will really move your delta quite a bit. So you'll either be under or over hedged by a lot.

[00:34:18] And I'm just remembering the GameStop saga, not this one last week, but the original one from Jan 21. You had implied vols in the 800s. And that's where I started to see people talking about this concept of the implied

[00:34:34] vol coming down and it changing the delta by just a massive amount. But keep going, walk through again, just more of what you've been able to look at and your thought process around it. Yeah, absolutely.

[00:34:47] So to give you an example why Vanna can get very wonky is let's say we've got market maker inventory that has negative Vanna. We have a decrease in IV. You would think that this would kind of be supportive of lowering systematic

[00:35:04] volatility, but as it turns out, so if you've got market makers holding a large negative Vanna position, IV decreases. So that means that if market makers are in this example, let's say they're holding a long out of the money put.

[00:35:19] So that put goes from, let's say negative 30 delta to negative 20 delta. So in this case, market makers are actually selling shares back into the market with a decrease in IV. So now we've got this kind of feedback loop that's going on.

[00:35:34] We've got a decrease in volatility, but that's accompanied with more selling, which actually increases the vol. So when Vanna gets high, we've looked at it kind of on the broad macro level in SPX, but I think this effect can be really potent around earnings for

[00:35:51] single name securities is that you've got a situation where IV decreases, but the market maker rebalancing actually raises vol. So subsequently raised that vol. Let's just say I'm the market maker. I'm short this 10 delta wingy lottery ticket, but the vol is sky high.

[00:36:11] So the thing's actually worth something, right? It's super far out of the money. It's got a low delta, but it's worth something. In fact, I was just looking at GME into the roaring kitty press conference. Boy, that was something.

[00:36:24] But at 1155 on Friday before the conference was supposed to start or the live stream, I guess it's better to call it. The stock was at 37 and the 80 strike call expiring that day, four hours later, had a 10 delta and was worth 80 cents. It's just extraordinary, right?

[00:36:46] And so just thinking about, let's just say, and you knew this was going to happen. As he spoke, you knew vol was going to start coming down because everything we were waiting for is now in front of us, right?

[00:36:58] So you take the vol down and I'm trying to remember where that vol was marked, but it was maybe in the 500s or so. So maybe you take it from, you start speaking, you take it from 500 down to 300. The stock doesn't move.

[00:37:12] And let's just assume that time hasn't really passed either, just the vol comes down. What happens to my 10 delta call when that vol comes down from 500 to 300? So this is your long this out of the money call, right? Yeah, let's just say I'm long. Yeah.

[00:37:30] Yeah, it doesn't matter. So in that case, that 10 delta call will almost go to zero, right? And the way to think about that is we've got this range of possibilities when volatility is high.

[00:37:42] So if we think about delta as almost proxy for the probability of being in the money, then that vol comes down. The chances of that deep out of the money call ending up in the money now almost evaporate to zero, right?

[00:37:55] So that drop in vol sucks almost all of the premium out, right? That's the vol crush, but it basically drops the delta down to zero. And so when you did your back tests, were these back tests on the index level

[00:38:08] or was it the single stock level or both? So we mainly looked at the index level. And the reason we did that too is when we're really focused about thinking about VANA position, we want to build up an order book.

[00:38:22] This is a very time consuming process to do for our single names. So we've also been in a period of low vol, so we don't really think the VANA impact has been that great. When we looked at it back in 2018, 2019, 2020, we saw that at the macro level,

[00:38:38] once you had these high and negative level of GEX, this negative VANA kind of took over next. So what's the outcome? Was it a case where the markets implied vol pricing came down a lot and then that impacted the delta side of things?

[00:38:54] Yeah. So our finding at the macro level is that we've got these large intraday moves that would happen in the sense that you would get a drop in implied vol and then all of a sudden you'd get this midday reversal.

[00:39:08] And that's kind of what we look for, these midday reversals as a sort of a sign of a drop in vol maybe in the morning. And then by the end of the day, you've had this selling going on

[00:39:17] and with almost no news, no real macro news or no real fundamental news out. So that was kind of an indicator that VANA was almost taking over here. And then we looked at the aggregate VANA position and we saw that

[00:39:30] the negative VANA, accumulated by market makers was incredibly high. So that was our attribution to those kind of intraday whipsaw effects. Yeah. It's so interesting. And I remember, I think you and I were talking about this risk magazine,

[00:39:44] pretty respectable outfit in terms of analytical finance took this up. And there was a day, I want to say it was in 2020, maybe it was post the very peak of the intense fire in the market of the pandemic.

[00:39:59] So it was past March, it might've been into April or May, but there was a Monday of an intraday reversal that was just so unbelievable with no real news you could point to. And they did a fair amount of work on trying to ascribe this particular Greek

[00:40:18] and the way in which the implied vol moved. And then that changed the Delta profile dramatically. And I think it led to a massive intraday reversal rally in the afternoon, the market if I'm not mistaken, but super interesting. Super interesting. I was actually quoted in that one, Deidre.

[00:40:35] Was I right? Was it an intraday rally? Yeah, I have to double check. I think it might've been an intraday rally. I tend to think these things matter the most at extremes, right? And in other words, look, we're always supposed to think about

[00:40:49] the risk profile of the market. It's hard to uncover. There's no doubt that there was alpha in thinking a lot about the period going into Valmageddon. And it didn't take someone like yourself with a PhD in finance to actually map out

[00:41:06] what it would mean if the VIX went from 12 to 18 in one day, what it would mean for that complex of VIX futures and sort of the knock-on effect. So we're supposed to do this stuff. We're supposed to do the work.

[00:41:19] I think some part of it is difficult to disentangle, but at extremes and maybe with some benefit of hindsight, it becomes a little easier. The last topic, Garrett, I wanted to explore with you is some of your work on implied dividends. Really interesting stuff.

[00:41:34] So I think it might be still a work in progress, but we've got all kinds of implied measures in markets, right? Implied vol, implied correlation, implied forwards, implied funding rates. And with the benefit of all the other inputs, we can extract an implied dividend for a single stock.

[00:41:53] And that's what some of your work has done. And you've got some interesting conclusions. So map this out for us. How'd you sort of start thinking about this and walk through the work that you've done and some of the conclusions? Garrett S. Yeah, absolutely.

[00:42:06] So we looked around and part of our goal at Option Metrics is to build out as many risk-neutral measures as possible and as many useful tools for understanding risk premium from the option market perspective. But we looked around and we had seen that there were some other calculations

[00:42:25] of implied dividends. And this is nothing new that academics have been looking at implied dividends for quite some time, but they'd always been doing it through this little bit simplified pull call parity lens. And without getting too deep into American options, pull call parity,

[00:42:40] it's an okay assumption sometimes, but you've got this early exercise nature built in, which can screw it up a bit. So we found that if we mapped option implied dividends using pull call parity, the results weren't great. There was a lot of noise to them.

[00:42:55] So we went out to develop a model based on some of the research out there that basically took a bunch of dividend paying stocks, matched some options together, and we solved for an implied vol and a dividend simultaneously using our numerical trees.

[00:43:11] So we didn't make any assumptions about whether being a European style option and obeying the arbitrage found in pull call parity. And what's interesting about these implied dividends is if you look over a longer horizon, implied dividends tend to be lower than the actual realized dividends.

[00:43:30] And this is very comparable to if you take a look at Eurex, Eurex has dividend futures on it. I think the most popular are on the Eurostox. So these are index dividend futures, but they also have a large list of single name, which aren't very liquid.

[00:43:44] So we took a look at these two things and we saw that there's an interest in hedging dividend payments or hedging the dividend cashflow from securities. And that's essentially what this implied dividend measure calculates.

[00:43:58] We generate these dividends that at its core basically tell what is the risk associated or that the options market is placing on the probability that these dividends will occur, let's say one year or two years into the future.

[00:44:12] And if we look at the history from 2018 to present, we find that this dividends risk premium. So it's the cost of insuring against changes in dividends has actually shrunk to nearly zero in the last year and a half, meaning that investors aren't really concerned about the

[00:44:32] dividend stream of these single name companies. And we've seen this over a wide variety of US patent stocks. HOFFMAN Really interesting. I'm just remembering quite a common carry trade in Europe was this dividend futures trade. Dividends were systematically cheap and they were cheap for a couple of reasons,

[00:44:50] one of which was the structured products universe that is very vast in Europe, often left the dealers in need of covering dividend exposure. So you price these five-year options and you don't have a tremendous amount of visibility

[00:45:04] as to where things are going, even on the index level, even on the Euro stocks itself. And so very common carry trade was effectively to buy DIVs that were systematically cheap from the dealer community.

[00:45:17] And you're saying that similar observation could be made through your work and on US names as well. Yeah, that's correct. And one thing this gap, this systematic cheapness has really disappeared over since 2023. And it's interesting because it falls in line with kind of this disappearance and risk

[00:45:38] aversion. And we see that on the reduction of credit spreads, we see that in lower treasury vol and lower equity vol too. So even at the dividend level, it's pretty apparent that for whatever reason, investors are not as concerned about the future of these dividend streams anymore.

[00:45:55] Give us just a sense of context in terms of that discrepancy. So if we're going back, I don't know, five years or so versus now, what might the discount have been relative to what materialized, what was priced versus what materialized and to where it is now?

[00:46:14] Just trying to get a sense of order of magnitude. Yeah, so it's interesting. So if we look at single stock names and you would expect maybe about a two to 3% difference. So the growth rate would be about

[00:46:27] two to 3% less from the implied measure, as opposed to if we looked at the forward yields, that has just closed up completely. So it was very profitable, let's say pre-2018 and especially throughout 2020 and 2021 to be long dividend futures.

[00:46:44] Now, if you look at it, there's almost no premium associated with it. So interesting. I do wonder if it's some expression of just too much capital chasing the opportunity and closing it. We didn't talk too much about implied correlation,

[00:46:58] but here's another one where the capital coming in to try to capitalize on this carry trade, effectively the dispersion trade of buying single stock fall selling index fall at increasingly low levels of implied correlation. It's working at every turn, even at very,

[00:47:15] very low levels. And so you just wonder if it's gotten overdone. I mean, it's obviously we'll have to see, but it's certainly an interesting or potential corollary to too much capital trying to close these pricing discrepancies. Well, Garrett, it's been an absolute pleasure

[00:47:33] to catch up and congrats. I think Option Metrics is a really interesting firm. I have a lot of investors on the podcast and we talk about macro and derivatives, but occasionally I get to engage with someone who's at a firm that is not necessarily investing,

[00:47:51] but is playing a really important role in helping people invest. That could be a data provider like yourself or an analytical firm like Option Metrics. So it's been really fun to catch up,

[00:48:01] learn a little bit more about your research, the work that you guys are doing, and I wish you the best. It's great to see the development of the company 25 years and on. Thanks, Ian. This has been great. Thanks for having me.

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