ReSolve Riffs with Logica’s Wayne Himelsein on “Skew Baby”

This is “ReSolve’s Riffs” – live on YouTube every Friday afternoon to debate the most relevant investment topics of the day.

The year that saw the fastest decline, followed by the steepest recovery in market history has left one topic top of mind for most investors – volatility. Though down from its eye-watering spike in the first quarter, the VIX (CBOE Volatility Index) has remained significantly higher than in previous years, and not without good reason. The ongoing pandemic, a bifurcated and fragile economic recovery, the most polarized US presidential election in living memory, continued geopolitical saber-rattling between China and the US, and the list goes on…

We had the pleasure of hosting one of the foremost volatility experts in the industry – Wayne Himelsein (President and CIO of Logica Advisors) – for a deeply insightful conversation that covered:

  • His background in the industry, and how he met and partnered with Mike Green
  • Why “absolute” doesn’t mean steady returns – the lumpy nature of positive convexity
  • Why Logica loves gamma and how to “scalp” it
  • Unknown unknowns, unrealized vol and protecting against events that are by definition, outside of your sample distribution
  • The difficulty in sizing a long-volatility strategy within a broader portfolio

Wayne also delved into his investment process in some detail, including how Logica measures and adapts to market phase-shifts. You should probably just stop what you’re doing and press play.

Thank you for watching and listening. See you next week.

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Wayne Himelsein

President and Chief Investment Officer, Logica

As a 20+ year veteran of the securities industry, Mr. Himelsein (a UC Berkeley alum), founded Logica Capital Advisers (“Logica”), and currently serves as its President and Chief Investment Officer. In this capacity, he heads the portfolio management team, and is a member of both the investment and risk committees.

Wayne began his career in 1995 as a proprietary trader at the Carlin Financial Group (acquired by RBC Capital Markets). Following his success there, Wayne launched his first hedge fund in 1999, and went on to manage money for private clients and major institutions such as Hyundai Securities and Millennium. In a strategy he later developed in 2005, he ran over 300 mm of private and institutional capital.

Throughout his career, Wayne has continued to develop, evolve and refine his quantitative trading and portfolio management models. Most recently, beginning in 2011, Wayne and his team conducted 4 years of R&D to solve the problems inherent in HF’s quest to produce absolute returns regardless of market regime, which serve as the basis of the Logica Fund.

Wayne is a holder of a FINRA series 65 license and currently resides in Santa Monica, CA.

TRANSCRIPT

Rodrigo:00:00:00So, when you have … it looks dungeon-y to me. Are you in the basement or is that like California Architecture where you have to have nothing but bare bones in the background?

Wayne:00:00:12Yeah, it’s actually both. It’s California Architectural and I’m in the basement. I’m satisfying both of your questions.

Adam:00:00:17California basement.

Wayne:00:00:19What’s that?

Adam:00:00:19It’s California Basement Architecture.

Wayne:00:00:22Exactly.

Adam:00:00:24I was noticing the couch in the background. I empathize because every time I get off the phone with Mike I need to go cogitate on the couch for like an hour and a half, two.

Wayne:00:00:33Yeah that couch comes in very handy. It’s great back there. It’s a necessary tool doing what it is that we do.

Adam:00:00:42That’s right. I remember last time I was on a Zoom call with you a few weeks ago you had the, I think you were in the same office but you had this outer space background going that-

Wayne:00:00:53Yeah, I tried some backgrounds, it didn’t play out well so I’m going for just basement look. It’s more genuine right? It’s where I’m sitting. I’m not actually sitting in outer space. Lo and behold.

Mike:00:01:06It’s the bunker look.

Wayne:00:01:08It’s the bunker look, right.

Mike:00:01:10Bunker chic maybe.

Wayne:00:01:11It’s the ultimate quarantine.

Adam:00:01:12Bunker chic.

Wayne:00:01:14Bunker chic I like that. California bunker chic. Or something quarantined. We have to throw that in there. It’s pandemic friendly.

Mike:00:01:22Yeah right. It’s quarantined bunker chic reporting from quarantine bunker chic Logica offices.

Wayne:00:01:30There you go.

Mike:00:01:31So, welcome Wayne. I’m going to give the typical warning for everybody that nothing that we talk about is investment advice. If you’re going to get that get that from capable people, likely not on this call. We’re going to have a wide ranging conversation that can go lots of ways and we want to keep it that way. It’s also a happy hour type of scenario so I am sipping a nice bourbon, a Basil Hayden bourbon today. I know it’s a little earlier on the West Coast. So Wayne you might not be doing it. Adam looks like he’s got the straight water going. I don’t know what’s going.

Adam:00:02:05I’m between drinks.

Rodrigo:00:02:07He’s marking the close. He’s got no excuse

Adam:00:02:09Yeah.

Wayne:00:02:09On one side it’s never too early on the West Coast but yes it is early for me.

Mike:00:02:15Agreed.

Adam:00:02:15I’m at this stage in my life where I’ve got three kids going three different directions that can come home at any time and so you got to be at least able to go and pick them up and drop them off different places and remain at least-

Wayne:00:02:26You got to be of sane mind.

Adam:00:02:28Manageably sober for…yeah exactly, to stay out of trouble.

Mike:00:02:32I remember when those excuses were just excuses back in the day. Now you sort of give them some sort of validity as if they’re reasonable anyway.

Adam:00:02:43I know, that’s true.

Mike:00:02:45Well, let’s jump into the fun stuff here. We’ve got one of the gurus of the tail trade here with us. I think something that is on the minds of a lot of folks with respect to their portfolios and stepping beyond the pale of potential sort of traditional sources of diversification. I know Adam you had some thoughts or wanted to go in a particular direction.

Adam:00:03:12No. Maybe I just think it’s good to let Wayne introduce himself and introduce Logica and maybe his role there and then we can spin out from there.

Wayne:00:03:19Sure. Thank you.

Mike:00:03:20I’d also say before we start with that spectacular rendition of Wayne’s history, I just want to remind everybody to like and share and do all the fun stuff with these things so that we can keep these going and if you like Wayne leave a review, and if you don’t like Wayne …

Adam:00:03:38That’s perfect. We lost Mike let’s move on.

Mike:00:03:42I’m backstage, I’m going back and forth backstage. Anyway Wayne we go.

Adam:00:03:47Wayne give us your spiel…

Backgrounder

Wayne:00:03:49Yeah. And keep the don’t like reviews a bit shorter than the likes and what. We’ll go to that. I’m Wayne. Wayne Himelsein. I run Logica. I’m the Chief Investment Officer. I’ve been trading since 1995. I started out as a prop trader, traded equities, traded options and learned my way through the capital markets at a prop desk and left to start my first hedge fund in what was it 1999, 2000 somewhere around there which was the end of the great bull market in 1999 in the beginning of course of the 2000 correction.  So I got the tail end of two very interesting markets and that I think not only taught me a lot as my out of the gate hedge fund launched back then but it informed my future into the uncertainties of the market, the fragility the market, the inherent negative skew in the market, all the stuff that people worry about. When your first business or launch of a hedge fund it goes from the best year on record 99 to one of the worst corrections in 2000 you take that in and absorb that and say, okay, I have to live my life or my career understanding that this is what I’m going to be prone or this is what the industry is going to be prone to or I have to prepare for this. So all of my thinking therefore was based around how do I manage in that level of uncertainty. Went on to run a fund for several years and then another fund in a very interesting inefficiency I found in the insurance pricing space also kind of risk inefficiencies, and then in 2011 launched Logica which I’ve been running since that time.

Today, our focus is long volatility which goes back to the beginnings of my career, it’s kind of my whole thesis and many of you have seen me on Twitter or heard me speak and really I love the side of the market that is…I just tweeted about this yesterday actually, non-linearity and the fact that there’s big moves to be made and they come around when least expected. The way to take advantage of that is optionality, in my mind I don’t like necessarily calling the direction of the market so I like to be agnostic to ups or downs but use optionality or convexity to take advantage of more extreme moves. I think what’s working in our favor at Logica is that these extreme moves are happening more and more over the course of time. 2018 I remember December of ’18 versus Jan of ’19 was the down 12% up 12%. Month of December, month of Jan, and so you see these incredible V’s in the market and of course come Feb March of 2020 one of the biggest fastest downs we’ve all ever seen in our lifetimes and by the way we’re at new highs and it’s only a couple of months later. Not a couple but close enough to say this is just ridiculous movement in the market and in my opinion ideal for optionality and taking advantage of those kind of moves. So that’s the hopefully a good summary for you guys and let’s go on with whatever direction you want to go.

Adam:00:06:59Great. You joined up with Mike Green when and for what reason? We’ve heard Mike’s version of how that happened but coming through your prism how did that partnership arise?

Wayne:00:07:15Funnily enough, we initially met on Twitter which is a really cool thing. I love that FinTwit space and so Mike just reached out to me. I tweet a lot of stuff that’s interesting and of course if you have the same mindset or philosophy you tend to see the people that resonate with you. So Mike saw that and he reached out and said, “Hey let’s talk, we’re both thinking the same stuff. You obviously know what you’re talking about, I know what I’m talking about. So we should have a conversation.” That was back in early 2019 something like that. So at the time I was running the tail risk product which is again long vol but more short tilted. It was against a portfolio of market neutral exposures. Market neutral has negative skew to it although it’s conceptually neutral to markets when markets collapse market neutrals tend to go with the market a little bit. So their beta is asymmetric, they can be better neutral for the moment but they get asymmetric when markets really fall. So I was handling the long vol piece of a broader portfolio offsetting to that and wanting to at the time had just spun out to say hey I want to do this not offsetting to a portfolio I’m facing but as an independent product.

So when Mike and I met I was telling him hey I’m just in the midst of…I just spun out, fortunately for me I just spun out in the end of 2018 so I was there to participate in the December sell-off and of course did nicely. So I’m telling Mike that’s this is what I’m doing and concurrently he was telling me that he had also an options portfolio where he was which is Thiel Macro. He was running their desk here in LA. I guess out of San Francisco but he was overseeing that and then I guess in that conversation the easy way to put is we just totally hit it off. So half an hour potential coffee ended up a four-hour we love each other. It’s just really neat when it goes that way. The synergy was obvious and so we said let’s keep on talking and what can we do. Some months later Mike approached me saying…not necessarily saying but recommending that if I’m going to be spinning out why leave it as a short tilt? Why have a pure tail risk product when the market is of course fragile in both directions or can move of great magnitude in both directions? Why not do an absolute return product. As soon as you said that I thought of course that’s much more interesting, it’s much more widely applicable to people, there’s probably a broader market for absolute return than there is for pure tail risk and the thinking of that was fairly easy for us to achieve because it was simply saying we already have a long and a short book so let’s just increase the weighting of our long book. We quite literally had a two to one short long so just make it two to two or for easy purposes one to one long and short and therein became an absolute return product.

As soon as I shared with Mike the results of that and what that would look like which was easy for us to do because we were already running all those models, it was just a matter of re-weighting it a little bit longer or long tilted. So as soon as I showed Mike what that outcome was he said that’s incredible, I want to do that with you. And concurrently he had been running also a straddle which was of course the same expression but he was doing it in more of a not naive but a simpler version of what we had done at Logica. We had multiple models running inside that were collectively expressing this long straddle approach in an absolute return format. So the commentary was, hey this is doing what I’m doing in philosophy and it’s exactly what I’d want to be doing and it’s quite a bit more complex and it is really well built so let’s do it together. I’m shortening a year of conversation but at the end of that it became let’s be partners, let’s do it together, we both have the same idea, literally the same portfolio and we’ll be more powerful as a team than as independents. So here we are and I think it all came together end of ’19 and then we joined formally in like January of ’20. It’s been almost a year now which is really exciting. My last comment is although it’s only been only a year we talk almost every day. The initial half-hour conversation that became four hours was completely representative of how well we continue to get along and to share insights and collaborate and we oftentimes have the same philosophy but different ways of looking at it. He’s got a deeper fundamental background, I’ve got a deeper quant background so the synergies just keep on expressing themselves and being really powerful, and we love. It’s such a good partnership and team.

Rodrigo:00:12:22Well that story sounds fairly familiar.

Adam:00:12:27We keep looking for partners that we can really connect with.

Wayne:00:12:29Yeah. I see three of you on my screen. Something’s working well.

Rodrigo:00:12:35The night I met you two, you guys were already partners but it was like we saw each other, we saw each other across the room.

Wayne:00:12:43You and I Rod, of course. We had that same effect.

Rodrigo:00:12:46I know with you, Wayne but also me and Mike and Adam. I met them in 2011 and that night we were up till 4am and we were partners the next week. And then with you there was like we argued a little bit about machine learning, philosophy and Mike and I were there in California for Sam Harris.

Wayne:00:13:05That’s right, I remember.

Rodrigo:00:13:06And in our conversation, and we talked about him because I know you know him. So yeah we definitely hit it off intellectually as well. So I’m looking forward to the conversation. Sorry Adam I interrupted your question though.

Logica and Convexity

Adam:00:13:17No. All good. I was just wondering because I’m trying to square the circle. You talk about the fact the Logica strategy tends to be focused on having convexity to more extreme movements in the market and the absolute return objective because I think of absolute return as certainly being on average uncorrelated to the market but also I think the investor expectation is steady returns. So what is the profile of a strategy that generally resembles a straddle and doesn’t really have a long or short bias and does it resonate with the general perception of what an absolute return fund should look like?

Wayne:00:14:18Sure. Multi-part question, I’ll try to take it in pieces. Absolute return of course is the idea that there should be returns absolutely not relatively. The thing is that no matter what we are in absolute terms we’re relative to something. So really it’s just saying in absolute return should use a different source of alpha than market direction. There should be alpha not beta. It’s as simple as that to absolute return. That doesn’t mean that there’s not some sensitivity to what the absolute return is trading. So whatever is your source of alpha is also going to be your source of vol or downside. So in our world we’re absolute return in volatility terms. We trade volatility and a little bit of directionality but we’re absolute return because we should have nothing to do with the ordinary direction of the market. And we’re absolute return because we should generally make money. I guess people could call that all weather or whatever you want to call it but that doesn’t mean by any standard that you don’t have sensitivity to some source of volatility. Ours ironically, our source of volatility is volatility. So being long vol means when vol goes up we do better and when vol goes down we do worse. In recent months vol has been crushing, it’s been coming off the of course the COVID highs and there’s been a steady downtrend in vol. So we’re long something that is literally declining. That makes absolute returns harder in such an environment. The thing for us given that we are that style, given how much we believe in long vol as a necessary asset class, the name of the game in that space is to manage the risk while the finger long is going down so that you can be there for when it’s going to go up. Obviously you want to make more on the ups and contain on the downs and then you become a well-traded absolute return.

Adam:00:16:20You guys have a multi-strat too, right? Like you run a long equity book with certain characteristics against your options overlay. I got that from the way that Mike sort of described the strategy.

Wayne:00:16:33Yeah. Before I jump into that and so I’ll answer the long equity book piece, there’s one more piece I want to answer on your last question which I think is very relevant. You were talking about absolute return and you used a key word that I want to hone in on. You said, “Shouldn’t absolute return be steady?” So there are different types of absolute returns, being if you’re in absolute return in pure equities then I guess the objective is steadiness maybe. At the same time for us in volatility trading I would not say the objective of our absolute return is to be steady, it’s to have more positive returns than negatives. So it’s absolute, it’s unrelated to the market but it’s not necessarily steady. Why is it not steady? Because we’re right skewed. Because we have convexity. A return profile that looks like just as an example plus minus five bips for three months and then up 7% is an absolute return over four months but it didn’t come steadily, it came in a pop. And that should be the expectation of an absolute return that trades convexity. That’s a different structure of absolute return than pure equities that don’t have the element of convexity to them. To finish off what you were first asking I want to relay that point.

Adam:00:17:50No it’s a really good point and that certainly I wasn’t implying that absolute return only should apply to funds that do deliver steady returns month in month out, they tend to be the extremely negatively skewed strategies that deliver that type of return profile but it is interesting because I think the perception if you were to talk to many institutional investors or many advisors or investors who seek absolute return, I think the expectation tends to be more of a steady profile and so there’s got to be some kind of educational curve to help people, clients to understand exactly the type of character that they’re buying and why it’s complementary to some of the other types of absolute return funds that they might also be considering.

Wayne:00:18:39I find that it’s a good point, it’s relative to what investor expectations are. I find that there are…of course we all find this because we’re in the business of asset management that there’s a tremendous spectrum of investor awareness and I guess sophistication, all of the above. And so some investors, we have investors now that said last month why were you down and then another group of investors said wow you’re outperforming your peers, you’re doing great. Of course you’re down vol is down a lot as an example. And so some put you in relative space because many let’s say multi-strats realize that so much of their portfolio is negative skew so they need some right skew long vol positioning in there and they can’t expect that to be up when the other side of their book is up because that’s why they have you there. Because you’re going to be up a lot more when the other side of their book is down and so that’s the whole point. It’s finding the right investors for what it is that you do and I guess for us I feel like that’s kind of easy because most of the landscape is seeking consistent with negative skew. Not seeking negative skew but ends results in negative skew whereas we’re saying we could be also pretty consistent and positive skew at the times everything else is cracking. That makes it of course an easier way to be absolute in a much larger absolute universe.

Rodrigo:00:20:01Well it’s an interesting absolute strategy because there was a tweet that you recently had about the metrics used in the industry to try to find good managers. Try to find uncorrelated managers, try to find managers with high Sharpe, you know. I imagine that you know every alternative fund has these issues but I imagine that your profile makes it particularly difficult to be found in certain filters for absolute type return managers. I’m curious, you care to speak a little bit about that? Your tweet itself was these beta Sharpe correlation alpha as if people talk about it as a fluent and finance lingo, they’re all based on broken assumptions of linearity which markets are not and which undermines the lot. So maybe talk a little bit about those metrics and how you guys tackle that.

Absolute Returns and Sharpe

Wayne:00:20:58Yeah sure. You started out with Sharpe. That’s the greatest place to start. Sharpe, especially for a fund like ours which is right skew. For any fund that’s right skew or long vol, Sharpe is a horrible metric. Why, because the denominator is vol. So the higher your vol, the worse you’re Sharpe. What if your vol is all upside vol? In that sense our Sharpe goes down when we do great. What’s that about? That’s literally upside down. We’re being penalized because we make more than we lose. That doesn’t make any sense. So in that perspective any fund that has a higher upside vol than downside vol is one that should never use Sharpe, literally because you’re going to be penalized by that metric. Sortino would be significantly better. So that’s one just easy answer. The second point is non-linearity in general, just which is the way markets and maybe not the S&P Index which is tends to be as close to normally distributed as we can see. Tails are a little bit fat but if you go down to any underlying, any constituent of the S&P or any many other asset classes you see lots of fat tails, lots of skew. And so in all of this environment there’s is much more non-linearity and therefore the metrics you use stopped making sense. They were, alpha beta, are designed with the assumption of Gaussian of an IID, independent identically distributed data. I mean there’s so much here that I want to keep on talking about but I have to try to condense it into one. We’re talking about time series. So it’s introducing the element of time where and we’re introducing series that are discontinuous, that have gaps in between like when the market closes at night it opens the next morning. So you summarize all that and you just look at these metrics that people are relying on for so long to say is A better than B? And I’m like I don’t know. Why are we still there? Mathematics and statistics and all these fields have advanced so much in decades. As you mentioned Rod when we started talking was about machine learning when we first met, so that’s all going on. So why are all the big institutions still just lining up your Sharpe and your beta and saying okay we like you or we don’t. It doesn’t make any sense to me.

Rodrigo:00:23:29It’s just the metrics you know, how you calculate Sharpe whether it’s on a monthly basis or on a daily basis has completely different outcome, right?

Wayne:00:23:37Of course. The original Sharpe was a single period Sharpe. It doesn’t scale with time because the numerator of course is return, the denominator is vol. So return and vol move the scale differently. Vol scales at of course root T and return is geometric. So if you move it out it’s just going to be completely different Sharpe if you look at your daily Sharpe versus your annual Sharpe. So which is your Sharpe? Which should you be looking at? So if you have to start putting metrics on a sheet and then next to that metric an asterisk explaining why this metric is good and what should be ignored with it then why use that metric? And of course we have to because that’s what everyone’s looking at and that was the point of my tweet.

Rodrigo:00:24:26It’s kind of sad you have a program that on its own may have a small Sharpe ratio and in fact Quest guys…I can’t remember the guy’s name but his, Quest is the managed futures guy, is big on, doesn’t matter that I have a low Sharpe ratio, put me together with another portfolio and I’ll show you that I can give your portfolio a higher Sharpe ratio than something-

Adam:00:24:49Well that dovetails with the question that is obviously prompted by this whole conversation which, and we obviously have some thoughts on that, but how would you if you were boss, how would you have people measure performance? If you had to condense performance into one or two or three statistics for parsimony, what would they be?

Wayne:00:25:14I think I’m going to have to challenge the question and say when you start out saying if you had to condense performance into a single summary metric and that’s where I say you can’t. That’s the problem, you can’t condense it into a metric. So for us and I know that’s not the answer you’re looking for but it doesn’t condense into a metric because they all behave differently. Go with the easiest example is if you’re just short out of the money puts, your metric is a high Sharpe, you get a high Sharpe, you get incredible alpha and then suddenly you blow up. So it all has to be in context, that’s the point.

Adam:00:25:57I agree. What about portfolio Sharpe? … portfolio if you put these funds together or if you weight these funds optimally even though the individual constituents may not have high Sharpe ratios and we may have some non-linear strategies in there, the portfolio Sharpe ends up being very high so is that a reasonable place to go? Or at least-

Wayne:00:26:23Sure. So that’s something that Rod touched on a moment ago which is that the different parts are meant to offset each other and that’s where I love that. I think you guys are there too in the way you design your portfolios that’s where I start from is the whole is greater than the sum of the parts. You could have a negative returning thing in there i.e. long puts next to your long equities and but you’re losing money on that piece of your portfolio every month. Yes and I’m really excited to lose that money because it’s going to save my equity downturn. And so that’s the point is this the whole is greater than the sum. So to that point, if you have a portfolio that you know in context is well balanced and you could kind of see that in the portfolio skew. Years ago in China when I started out sharing my background where I had built a long vol book offsetting to a market neutral book. So the market neutral book they always look good and high Sharpe until the negative skew event. Sticking a long vol book next to it you kind of ate away at let’s just say five ten bips of return every month but then that one month that market neutral was down eight it’s actually still up 60 bips. Because the long vol kicked in. So that made the total portfolio distribution more symmetric and that’s how I looked at it. I said over here I’ve got a right skew, over here I’ve got a left skew and I layered on all these distributions on top of each other and then the portfolio distribution looked as normal as could be, and that got me excited not because I believed it was truly a normal distribution, but because I knew that the parts were summarized to create something that was more reliable and where the deviations would be what we would expect because it had offsetting pieces inside. In that place you could use a Sharpe. You could use an alpha and a beta but now you want to ask who is being so thoughtful to have that kind of portfolio all the time that you could just assume that that’s been done? And so that’s the problem is why you can’t start with a metrics.

First, you got to find out is what’s inside their book and then based on that now what metrics can I use? So that goes back to my answer is there’s no one for all, there’s what do you do and how do you do it? What’s inside? How you generating your alpha? Where’s your sources of vol? Now let me decide what metrics to use. All that aside I like to see the most information possible. I like to work with the total distribution, that’s what tells me the most. Look at the shape of the distribution of what you’re doing and I can quickly tell whether you’re long vol or short vol or et cetera and therefore what metrics might be worse or better.

Adam:00:29:05One of the tricks or challenges I think with positive skew strategy is that the number of observations where the positioning in the portfolio pays off is necessarily relatively small. I mean you’ve got this sort of challenge where negatively skewed strategies tend to have a lot of…like if you observe any random two, three, four, five, six, seven, eight year period they tend to look better and then you’ve got that one year or one month or one two week period like we had in 2020 that completely flips the sign or flips the perception of those two different. How do you overcome that small sample challenge?

Wayne:00:29:52Well, we avoid 2020s. I guess the easy answer is we assume that there’s always going to be. I mean that’s why I’m a long vol investor because in my mind before 2020 I assumed there was going to be a 2020. Of course I’m not saying I predicted a pandemic that’s ridiculous but there’s actually funnily enough a Real Vision that Mike and I did together in I think it was in November of was it ’18 or ’19? I don’t remember but it was after the December 18 correction. So it was somewhere in 2019 maybe October November of 2019. Mike was saying that one of the questions was very similar to what you’re asking was that hey an event just happened which was the December event might it be many years till the next one. So why do people need you as much now or need long vols as much now and he didn’t believe that because he agrees with the idea but that was his question as the host for others to learn and my answer was because you just never know when the next thing’s going to come. There’s no excuse for not always being long vol. That’s was literally the headline of my premise in late ’19 and then comes February/March of ’20. So I’m not saying that I was right, but I was. I was because we never know when the next thing is coming and so to me when you say how do you measure? I say you always assume that that next thing is around the corner. When people sit and say oh this was the worst event in in 50 years to me it’s not that it was expected to be so bad but if it wouldn’t have been this, if it wouldn’t have been a 30% down move in two weeks, there would have been a 20 sell-off on something else and then followed by a 10.

The point is that you take those assumptions, you create a path but you simulate a path that says there will be some set of down moves over the next five years. It might be three ten percent’s, one twenty. I’m probably not assuming another two weeks 30% down. That’s probably not happening again next week but if you simulated enough of those and you ran some form of Monte Carlo but not looking at what’s happened, not taking the time series and just jumbling it but saying these are the potential downs that can occur. Then to me that’s what one should model to and so that’s the history that we always not…Sorry not the history. That’s the future that we always assume which is not our history but what anything that can. Sorry I don’t know if that fully answers what you’re asking but that’s how I think about.

Adam:00:32:43No. I was just trying to give you a chance to speak to this because I think this is a challenge of optics for allocators who are looking, even if you’re an informed allocator and you recognize that the biggest risk is the one that you haven’t anticipated, the one that’s not in your historical sample and you want to build in this type of resilience. It’s difficult sometimes to be able to pitch this to the investment committee or the board or…Most people don’t think in probability space and they don’t think it’s sort of catastrophe space and I just think it’s always an interesting optical challenge.

Rodrigo:00:33:27When you talked about your example, the market neutral fund only given up four basis points a month in order to be able to offset that eight percent. That’s the pitch kind of, if you have an elevator pitch that’s the long vol elevator pitch. But the day-to-day living that is not four basis points a month, if you’re up one percent you’re up half a percent you’re down seven or something to that depending on what you’re doing but there’s a lot of volatility. It’s not like an insurance premium they pay so I believe that even like we helped bring some products to Canada long ago which was the S&P 500 plus tail protection and the pure effect of underperforming at times or being different certain months from the S&P 500 was enough for that thing not to survive. But the idea was S&P 500 minus a couple of basis points and then you don’t get the drawdown because you’re actually not quite as correlated or sometimes not at all correlated to the S&P 500 as you wait sure. So I think that’s the biggest challenge for people being able to stick to something that is a long vol product. How do you handle that?

Mike:00:34:35I wanted a … what you are seeing as the behavioral challenges. We saw California abandoned, we saw Alberta abandoned. At the same time in another context we saw Wimbledon collect a massive insurance premium on having insurance on a pandemic for the Wimbledon open. And so how do you differentiate or how do you help the end investor whether they be institution or retail client get through the tracking error if you will? Because the tracking error can manifest in a number of different ways and what are you seeing live real time as you are operating as a purveyor of these types of products complementing other portfolios.

Educating Everyone

Wayne:00:35:18Sure. So for us it all starts with the broad education which is what we believe people should know by now. I get the best example of is ’08. There is what everybody’s running their portfolio assumptions on which is of course realized vol, and then there’s the future which is unrealized vol. So everybody lives in this land of realized vol is what I’m going to optimize to and we’re saying no you need to think about unrealized vol because that’s what always hurts. The single best example of that of course is ’08. Realized vol was the tail variance of mortgages and let alone was nowhere near unrealized for whatever chain of reasons, we don’t have to go over that. So this is an educational starting point of saying how can you depend on realized vol when so much wacky stuff happens. So there’s many ways to say that. You asked about an elevator pitch. For us it’s not as much an elevator pitch but it’s educating on this idea. We wrote a paper many years ago on the problem with Sharpe ratio and how it doesn’t take into account asymmetry in vol. It’s called The Illusion of Skill.

So, putting out that paper, having people read that and think about it and realize that yeah there is a problem in my portfolio, I’ve always been looking at Sharpe and it’s underestimating my risk. So let’s talk to the guys who wrote a paper about that. I know you guys agree with that. You’re big on educating. So we educate. Now comes down to the bigger question is as much as you educate people how do they deal with that month-to-month pain? Let’s call it that. You still, S&P’s up and you’re down, what’s wrong like what’s happened? And so our goal there was to design something that wouldn’t have that pain and that’s where Mike’s contribution into my thinking was very helpful where I was going out to the word with a tail risk product that would have that consistent bleed. It would just have really nice convexity when needed. Mike’s point was let’s make this absolute return that’s what I shared earlier. So that’s what we’ve achieved is having something that can actually make money with the market. It doesn’t every month to your point but it more reliably does and that goes back to the question Adam you asked quite a while ago is about the equity book or that piece of that portfolio. That design is there so that we make money the rest of the time while we’re waiting for the event.

We’re not just long a straddle, well we are long a straddle but the downside, the down capture the straddle is S&P downside. We always have S&P puts on and we’re trading them to try to buy them cheaper, sell them more, we’re scalping, we’re doing a bunch of stuff to try to make money while we’re holding S&P puts. So what that means is as a reference can us trading around puts and let’s say you have a portfolio of S&P puts at different strikes and on one day you sell a few of these and the next day you buy a few of those and you and so you’re literally market making or scalping, can that beat a naive straddle or a naive put? Can your trading alpha overcome a little bit of the cost of holding that put? In our case it does. So we have some alpha on the downside by gamma scalping. On the upside, we infuse a different concept to take advantage of market up capture and that is that portfolio. So go back 25 years, my first hedge fund I told you about it was a market neutral stat arb portfolio. But it was unique to the world of stat arb because typical stat arb says that things should revert, it’s mean reversionary. Meaning let’s describe stat arb in a simple framework that two names let’s say Coke and Pepsi are two standard deviations apart and they just move to three sigma’s apart, so the stat arbist says oh that should revert back down. That should collapse. So they go long the under and short the over and three sigma’s should go back to zero in its dispersion. So I at that time being a long vol thinker thought to myself well not all three sigmas go back to zero, some threes go to four. There is the stuff and this goes back to the non-linearity. There’s stuff out there that just keeps on going and you might call that trending or momentum.

My launch of my first hedge fund was a mean expansion stat arb, I tried to find the pairs that were three sigma apart that would go to five sigma apart where the anomaly would keep on going. I love the pain. I love managing the pain right. But if you think about it in every stat arb book out of 100 pairs they all have let’s say 10, 20 pairs that could go wrong. That keep on going. Those are their losers and 80% of their book are their winners that do revert. So my thought was how do I just find those losers of theirs? What is it about those pairs that keep on spreading? Keep on widening? And so I found a tool or I built a system that honed in on those. I had that model already shoot forward 20 years, I said this is perfect for what I’m doing now because it’s long vol or long convexity in equities. So it’s the same system, I’ve been around it for decades and so what it does is it scans the S&P 500 for names that are expected to continue moving dramatically. Like where three sigma goes to five sigma to simplify. It doesn’t look at standard deviation but just to simplify the idea where you’re going to ignore the Bollinger Band and say it’s meaningless. In its ability to identify names that are going to continue exploding that becomes a perfect instrument or a perfect framework to lay on a convex instrument i.e. a call option. If you have information about an equity that says or that it’s going to go up a lot, you don’t buy the equity, buy a call. That’s going to give you a bigger payoff for the information you’re armed with.

Rodrigo:00:41:35You have a view on the magnitude.

Wayne:00:41:35So in that sense…what’s that?

Rodrigo:00:41:37If you have a view on the magnitude.

Wayne:00:41:39If you have a view on the magnitude and the timing of that move. If you knew that some name was going to beat earnings in the next two weeks by 20%, then buy a call don’t buy the name. This is not earnings based but this is looking at price behavior and saying this basket has an outsized ability to outperform the S&P in a fixed time frame with a bigger magnitude. Wonderful, buy call options. So the long side of our straddle takes positions via call optionality in that basket and therefore has an ability to outperform the S&P and overcome the down vol, the Vega drag or the theta drag while the market is going up, hence we can quote make money while we wait. That’s a long-winded way of saying it but these are all, the design of the portfolio and saying or describing ourselves as absolute return was precisely to overcome what you’re talking about. I don’t want to bleed every month, what I want to do is make money with the market going up and then I have a little bit of a hurdle on my call portfolio to overcome when the market starts collapsing but that’s worth it.

Rodrigo:00:42:48So has this construction led to an absolute return portfolio that during the drawdowns you’re not thinking necessarily that positive upside but rather covering the losses of your long portfolio? Is it now as an absolute return product can still be seen as a tail protection fund for somebody else in a traditional portfolio?

Wayne:00:43:11It can. Absolutely because-

Rodrigo:00:43:15… susceptible to losses because of the asymmetry tail.

Wayne:00:43:18Exactly. Because of the asymmetry of the two sides, just go with a very simple example is you spend a dollar on a put and a dollar on a call. Market drops 20%, your dollar call is zero, delta goes to one to 0.01 and your dollar put is 20 bucks. So, yes I lost everything in my long book. I give up. I lost it all except I made 20x on my short book. That’s the beauty of a straddled approach. It is literally not blow-upable. It is anti-fragile to the greatest extent because both sides of your book only start magnifying as the extreme gets worse. So therefore when the market starts cracking on the call side if you think of it as a V all you have to…I’m trying to get it in the shape of the camera. All you have to do is the call side starts declining and the put side starts taking off and once you surpass the cost of those calls you’re off to the races, that’s how we made roughly 20%ish in February March. We still had calls on the books, they just went down quickly and by definition the other side went up even more quickly. So call that gamma, that’s what it is, that’s why we love gamma. Is you’re unbreakable. You just have to manage what is called the valley of the straddle which is the area where it doesn’t move enough to benefit you.

Mike:00:44:43So, part of that also is managing along the way. That first day happens you’re obviously not rebalancing or hedging back to the previous exposures or are you? How have you guys approached that? How do you let that run in order to capture that very positive potential tale?

The Magic of Skill

Wayne:00:45:08That’s the magic. I’m going to stop talking now.

Mike:00:45:13It’s proprietary Mike.

Wayne:00:45:15It is but I can happily share it. There’s proprietary elements. But I’d call that the scalping. What’s kind of cool in concept is what we’re doing is saying that we don’t know but we’re making a probabilistic decision. So if you take a matrix of probabilities and say this is how much vol is moved, this is how much the S&P’s moved, this is how fast it’s moved, you have a distribution for each parameter that you’re looking at or each variable that you’re looking at has a distribution. So if you take a joint probability of those events and imagine converting that into how much you want to scale out. Said simply, if puts are, markets falling your puts are going up and it was an eight percent single day, that’s a that’s a bigger than average day, that’s on the far on the left side of the single day distribution. So you look across your sets of distributions and jointly there’s a 70% probability that that’s going to reverse tomorrow. So that 70% translates to selling seven out of your 100 puts, so you sell seven or whatever number, maybe that seven’s not enough. It’s 17. The next day is another 4% down move, well that’s not 17, that’s selling only 12 but it was one more day. So then you’re selling 16. My point is you’re taking these different probability sets, you’re combining them into a single probability that then scales how much you want to sell out of your position and what ends up happening is as the market’s falling you’re taking off the table. Like you would as a trader but precisely mapped to the joint probability of that event both in magnitude, in volatility and in vol of vol if that all makes sense.

Mike:00:47:12Are you scaling on the other side as well?

Wayne:00:47:14On both sides. Both sides are scaling and so what ends up happening is you end up profiting on one side and of course building up on the other side and you’re buying cheap vol selling expensive vol with all your inputs telling you the most probable time to do that. So that to me it’s gamma scalping on steroids because you’re moving thoughtfully along the path and try to be correctly tilted as it goes and the best way you can do that is with the probabilities of everything you’re looking at. That’s how I know how to make a decision.

Mike:00:47:52This truly is a function of skill.

Wayne:00:47:56I would hope so. In what way I guess you’re asking?

Mike:00:48:00No. I mean this is an active skill like it’s not as though although you’re looking at probabilistic models and whatnot, I don’t think you’re saying you could boil this down into some sort of index or set of very simple rules or are you or?

Wayne:00:48:15No I don’t think you could. I guess you could simplify it by saying here’s a simple version. As the market’s falling every 5% down sell 10% of your puts. That’s a rule, you can create that rule but that rule has more chances of getting it wrong than honing in on all the variables that your experience understands matters and then trying to precisely put that all together in a scale that makes sense relative to the position you’re holding and relative to the rest of your portfolio. So that to me is skill. That’s taking all of your knowledge it’s quantifying it and using it as a tool to properly scale or scalp as you need to with the way the market’s moving and what’s also cool about it, this is a totally side point but what’s really interesting is you’re applying a short vol to a long vol book. So we don’t ever get short but by selling off some of our long, we’re getting more and more short in the sense that we’re mean reverting against a mean expanding underline.  So the instrument is convex, it’s pulling up and you start clipping off the top. That is going against the mean expansion, that’s going against the convexity. So we’re doing that because we’re saying this put is now too expensive, we’ve made enough money on it we have to start pulling off the table so it’s introducing a mean reversionary component on top of a mean expansionary portfolio.

Mike:00:49:47What I’m trying to do is channel the questions that are coming up. We’ve got one Corey Hoffstein where lies the gotcha after all you know zero carry is the holy grail and another question after that was how can we construct trades to carry long VIX without the bleed and I guess that what we’re digging into here. This is a skillful thing that requires the type of insights that you’re talking about and the types of constant management on both sides of the tail. So it is a function of skill that creates the opportunity to carry the long tail exposure either side with reducing the cost.

Rodrigo:00:50:31There’s no buy and hold magic here.

Adam:00:50:33I do think that this is actually really an interesting area to dig into. It’s funny we have our Friday research meetings and today we were discussing pretty well exactly the same thing. You’ve got some sort of historical distribution that gives you these conditional probabilities and so you can make some sort of…you could say there’s a 70% chance of x condition on y, et cetera and what I was wondering especially in the context of this sort of tail type strategy where definitionally you are seeking out profits from the type of outcomes that haven’t occurred in the sample distribution. That’s kind of why people buy funds like this because it’s trying to hedge against the unknown unknowns and yet you’re bringing to bear an empirical analysis that says, in my sample distribution what is the conditional expectation given what we’ve just observed. So we had the same discussion today because any time you are trying to make decisions based on the empirical sample, you’re always going to run into a situation where you have an observation that lies outside of either to the right tail or the left tail of what you’ve observed at any time in history and what should you do in that instance? Should you assume that it lies in the same bucket as other extreme events and just sort of extrapolate or should you just ignore the signal because you actually have never seen a signal like that, you have no idea what the relationship is? How do you sort of reconcile this philosophically where you’re trying to hedge against unknown unknowns but you’re managing the positions using conditional relationships from the empirical sample?

Wayne:00:52:35It’s a fantastic question. February, March is the perfect example of that. No matter what empirical view you had, whatever realized observations you looked at there was never 30% down in two weeks.

Adam:00:52:50Yeah. You’re outside of all previews.

Wayne:00:52:52You’re outside of all data that you’ve been mining. All data you’ve been processing, let’s say that better. I think that goes back to our earlier discussion on realized versus unrealized is, we understand, we know that as you guys do. So knowing that, I remember when I was a kid GI Joe knowing is half the battle. We understand the realized, we look at the realized and we say okay but what’s the potential unrealized? That’s what I was explaining earlier is we have distributions of what could happen? Or another way to think about that is, insurance companies deal with this all the time in catastrophes like could there be three hurricanes in the same state on the same two weeks. They call it extreme value theorem, EVT. So in extreme value you’re just digging deeper into this fat tail and say is there any more information inside the tail? So when you summarize all of that as a trader, I summarized all of that to say it means that there’s more variance, there’s more knowledge I don’t have than I do.

Therefore, whatever not function, but whatever the prior function I used isn’t going to be good here once you pass a certain point. We call that in our model a phase shift, when our model goes through phase shift it literally changes cadence. So all other times we’re trading daily, when we go through phase shift that says hey you’re in the tail, all bets are off, ignore all empirical data. We translate that to we’re in phase shift so what does that have us do? The easy answer is it has us move slower because we don’t know. We allow for much wider variance than we’ve ever seen, so at that point an 8% move let’s say on what was it March 17th it was like the fourth 8% day in a row which we’d all never seen four in a row, whatever. I’m getting the numbers a little off but just to make the point. So on that day had that been the third day because we were already in phase shift. Had the third eight percent down day been in empirical we would have sold all remaining puts but our cadence had changed because we’re in no man’s land. So if had we had 40 puts left we only sold 10 then it’s the fourth 8% down day now we sell them all, no slow down only sell 20% of what we have remaining not 80.  Although empirical says sell 80%.

The easy answer is we…Once again it’s a conditional probability so we add a new condition which says everything is going to be wider because we’re in no man’s land, we’re in this tail environment therefore slow everything down and our system in that way adopts or adapts to that environment and it handles the tails really well and what is the risk of that? The risk of that is you get stuck owning a few puts on that crazy recovery day on March 23rd S&P’s is up 12% in a day and so you have some crush. So instead of having zero puts left we had 10 out of 100, let’s just say. These are not the exact numbers I’m just making the point again, but having 10 we still would have been okay had there been one more down day or two more, but at that point we were willing to live with only having 10 left. I hope I’ve related that…

Mike:00:56:31I think on the other side of that as well, you slow down on the call purchasing for example

Wayne:00:56:38Exactly right. You hit the nail on the head.

Mike:00:56:41So you’ve got a little bit of a loss but you also have a lesser bleed on your call purchase protocols as you’re coming down, it snaps back and then you could have some crush or you could virtually have perfect timing all of which is fine but accomplishing the meat of the sort of the bell curve meaty part of that move for the right tail that you’re trying to capture, is what you’re really trying to make sure you nail.

Wayne:00:57:09Exactly.

Adam:00:57:10This is craftsmanship here?

Mike:00:57:13Exactly right

Adam:00:57:14Yeah. So when you’re deep in the tail into periods where you don’t have any prior analog then as you say you sort of slow it down and whatever but it ends up being there’s some craftsmanship there and you’re leaning a little bit more heavily on experience and the true objective of the fund which is if this is the big one then I want to have a little bit on still to give investors what they need against the balance of their book because the balance of their book is getting fully impacted by this, by the big one.

Wayne:00:57:51Exactly, yeah.

Adam:00:57:53Rodrigo you’re muted I think.

Mike:00:57:55Rod you’re muted, I don’t know what’s going on.

Rodrigo:00:57:57Can I put up the craftsmanship part. I missed it because my wife had a question? Did you, did you this phase shift period where you slow things down was this craftsmanship happening as the corona virus was hitting or was this pre-?

Wayne:00:58:14No. We did the exact same thing, the exact same model was in play in December of ’18 and January of ’19. December of ’18 we hit phase shift, we were up eight percent in December of ’18 and January of ’19 with S&P rallied all the way 10% we were mostly call loaded but not as many because we had slow bought and we were up three or four percent. Literally the same thing happened. It was the same model, the same trading. It wasn’t as perfect. June we made a little bit of money…Sorry on April but not as much as we did in January of ’19. So no. The model was in place, it had done it times before and it did it again and so this is what we built many years ago and this is what we’ve been doing for years. This was the idea.

The other point I want to make is, at some point you said this Mike is that…Sorry Adam you said at some point you have to infuse experience. So probabilities are probabilities but when VIX hits a hundred and the S&P is down 30% in two weeks, I’ve been in the market for 25 years, I’m like okay I understand it can go more, I understand vol can go higher but at this point vol is negative skewed and the market is positive skewed. So I’m going to take that bet and it’s not that I overrode the system it’s that the system had the design of that. In other words none of us believe the market’s going to zero. So if your basic premise is we believe that any market crash is finished at 50% off. Let’s just take that assumption. So now we know where to end that slow sell and so, had the market corrected to 52% we got no puts left, sorry guys because that was our knowledge experience end of the tale. And I’m willing to live with that because I think anything more is so ridiculous as it would be VIX going to 180. So therefore we put in, we infuse our own experience and that makes it work even better.

Adam:01:00:16If you don’t mind because we’re a bunch of quant nerds too. You’ve used the word phase shift a couple times, can you dig into the mechanics of how you observe that or identify it? And if this is like part of the proprietary stuff then we can lean away too, I’m curious.

Phase Shift Defined

Wayne:01:00:35It definitely is part of the proprietary stuff but it’s…Let me think of a way to describe. All of what we’re doing is generally probabilistic. So we’re looking at distributions and shapes of distributions. We’re looking at the broad market in general so the S&P. We’re looking at the vol of the market and IV. So realized and implied and then we’re looking at vol of vol. So if you if you take these four sets of distributions and when they all lets…I’m going to simplify it now. If each of those sets just pass two sigmas on one side then you’re in phase shift. That’s not precisely what we do but that’s the idea is everything’s within its band and we’re trading normally at our scalping cadence that is comfortable. Everything starts to look wacky and we say, we’re in phase shift. The beauty of it is not that it’s a perfect signal. It’s that more often than not when all that stuff starts, when all those red flags start going up something’s brewing and if it didn’t brew no problem. We take it back off and we go back to normal and no pain. If it did brew, if the brewing does lead to something then we’re well prepared and so that’s the beauty of…Again it’s not just having a convex instrument but being convex in philosophy. Is that getting up to bat ready to go because things are looking weird? So that philosophy aligns with optionality is you’re going to take those bets. Part of what we do in phase shift is also scale up, we could be wrong and vol crushes and we get a little bit hurt because we scaled up but if we’re right just once it’ll pay for nine other times that we scaled up that we weren’t right. So I say if things are brewing go for it.

Adam:01:02:35So it’s, just to sum it up, that’s why it’s defined by the conditional relationships between realized vol,  implied vol and vol of vol and you can sort of observe or quantify different states for those conditional distributions and identify when you’re more likely in this type of phase or this type of regime and the market behaves a certain way or has a higher probability of behaving a certain way and the optimal way of positioning the portfolio is informed by the way that the market usually behaves in that regime.

Wayne:01:03:16Yeah. You could think of it as a transitional probability. I don’t believe vol is one distribution. Vol is a low vol regime and a mid vol regime and a high vol regime and they all have different vol of vol’s. They all have different widths, if you want to think of it that way. So what is the transition probability of going from low vol regime to mid vol regime? So you’ve come up with that and so with that’s picked up now your transition, now you’re in this vol regime and so now not only are you using a different distribution because vol of vol is more. So that translates to a different scaling rate which for us is a different cadence of trading. Does that make sense?

Adam:01:04:00Absolutely, that’s great. So how should investors think…I ask this to everybody who manages a non-linear or convex type strategy? How should investors think about sizing position to your…Maybe not to your fund unless you sort of generalize it but to your type of fund, or right skewed non-linear profile type funds in general. Is it purely an empirical analysis? Is it looking at the historical distribution of the fund versus the portfolio or, seems to me that again we go back to this small sample problem where you don’t quite get the information that you need from that type of analysis. How do you think about this problem or coach investors on how to think about it?

Wayne:1:04:51Yeah. That’s one of the big questions we always get and unfortunately it’s a subjective utility function. Everybody’s pain threshold is different, that’s the problem. So my first question to people is how much are you long? Are you hedging a market neutral book or are you hedging a hundred percent long equity portfolio. That’s A&B in different landscapes so that’s I guess a major bifurcation. Now you step out of that say well irregardless of which one of those you have, what is your pain threshold? Do you allow for a 10% drawdown and that’s fine for you or not you but your investor base if you’re a fund of funds. Is that okay for you? And so we just try to go through that range of questions and then come up with…Because that’s the problem. For some people, they don’t need to be hedged up to 10% down but they don’t want to ever have a 30% drawdown like what happened six months ago. So that means you need this much but if you’re someone who is like what I did initially was you have a tight market neutral portfolio, you’re trying to make 60, 70 bips a month and steady and the one risk you have are these negative skew events and so you want that whatever size drawdown that is, you want that to be a flat month. How do I do that? That’s a very particular analysis. There, we have no choice but to be a little bit empirical and say okay if we take all market neutral events and there was the 2011 crack and go down the list of the quant quakes, what were they all roughly in magnitude and how much do we need of this to offset that and how much bleed will that be to your average month? That’s a pretty simple quant to do, that’s how we would approach it. But it’s really subjective to the party that we’re speaking to.

Rodrigo:01:06:38On that note, we were actually having this discussion earlier this morning internally about how these solutions do not apply to different tiers of investors in the same way. So for example what you’re putting together the Absolute Return Fund you’re doing. I’m sure you can’t manage in the same way with a hundred billion dollars as it can with a couple hundred million dollars. So there are certain classes of investors that can benefit tremendously from alpha strategies like ours or any guests we’ve had, you can have a really strong allocation, a combination of them and really provide some unique idiosyncratic contributions to your portfolio and so on. But as you get bigger and bigger to the mid-tier pensions, to the largest pension plans, you just talked about a bespoke mandate and what we often hear in the space of tail protection is we can minimize the bleed by doing all this fancy stuff, is that true if you get the Ontario Teacher’s Pension Plan coming to you and saying I want you to hedge out my full portfolio what could you do? I mean, is there anything that doesn’t have a bleed that you can do for them? Or are they just going to have to accept that bleed?

Wayne:01:07:50Is Ontario on the line?

Rodrigo:01:07:54Maybe.

Wayne:01:07:55Yeah maybe, who knows? If you are please listen in or perk up your ears. My answer is that what we do is number one it’s very scalable, we’re trading SPX optionality and SPX straddle and S&P constituents that are not trading daily rolling more around monthly frequencies. So we have tremendous scalability, a hundred billion is a lot of course. But I don’t see any issue with what we do up to many billions. So I don’t know where that top is for market impact but it’s the most liquid options trading is SPX front month at the money which is generally where we’re trading. We’re certainly highly scalable. That said is so you’re saying can we do what we do and not have bleed and be convex. That was Corey Hoffstein’s question earlier like if so that’s the Holy Grail and I like to say if I had a grail here I’d raise it.  I just have a mug so I’m going to raise my holy mug but that’s what I believe we’ve done. Yes that’s …

Rodrigo:01:09:04The question is whether you can do it at large scale.

Adam:01:09:06 I think just to put some context around-

Wayne:01:09:09My answer is if we’re trading scalable instruments. I’d ask you that question is like if we’re making money scalping SPX can we scalp SPX for 100 million or 5 billion? Sure. Again, I’m not selling the whole thing that day. I’m selling X%. Today I needed to buy two percent more SPX options. I’m buying at the money options on the SPX, I need 2% more of a hundred million, it’s two million. That’s nothing. So our straddle can scale and what we do the different components that I’ve talked about throughout this podcast is we believe and we’ve seen generates positive returns on average outside of outlier events. That was the design of the portfolio. I’m not saying we can make money every month, no one can. Or I guess certain people can but not us. Renaissance can perhaps, I don’t know. You know we all have our limitations. I understand our limitations, our tough environment is when vol is coming down, and that’s our hardest environment. But in an ordinary vol environment, in 2017 vol was low, we made money. There was no crack that year, there was not a single mega downside event but we just traded the vol and we were up 9, 10%. I don’t remember the exact number but it was an average year so we are built to make money in most regimes but still be convex when markets collapse. As far as can we do that with scale? Yes I believe we can.

Adam:01:10:39I think Rodrigo may not have fully communicated the context. I think our discussion was around, is there anything that Norwegian sovereign wealth fund or the Canada pension plan or CalPERS or these huge funds can do to hedge their beta risk or diversify their beta risk with active allocations of any type and I think we sort of concluded that the sheer scale of the hundreds of billions of dollars that they manage necessitates the allocation to just major cyclical beta risk. So I think we were saying, look our strategy is not useful to the Norwegian sovereign wealth fund because we can’t run 500 billion dollars trading the frequency we trade. So that’s the context.

Wayne:01:11:36Yeah. I didn’t capture that before. Yeah, at that size I don’t know if it’s worth even thinking about because also the sovereign wealths, they don’t have investors they’re answering to. For them, the market cracking is a chance to just buy more equities. So it’s a completely different mindset if they’re not answering to someone and they want to just keep on putting money to work. It’s the same as I mean there’s many big institutions that are like that. That’s not who we or all of this tail investing world is for. We are for people who are answering to investors that are managing pools of capital that have a lot of other strategies in them which typically everything is subject to negative skew, is subject to market meltdowns and so they need offsets because they have to answer and produce statements every month to their investor pool. That’s a different … of the market.

Rodrigo:01:12:29Thank you for reframing. You’re totally right and thank you. That’s what I was getting at. One of the interesting things that came away from that is that you really have these large pension plans that they can’t. When I speak to the CIOs they’re like listen we can have a massive bleed and then piss everybody off along the way to get some hopeful tail protection if we get enough, if the counterparty risk that we sold the OTC from or bought the OTC from isn’t getting boring. So we’d just rather take beta right and what happens is you have these massive pension plans that are like the guardians of the way money should be managed and you see the mid-size pension plans and multi-family offices, look to them and say well I need to be like that. Like that’s their benchmark, and what we’re trying to say to people saying, no as you get smaller you actually have tons more options. Actually can get that type of possibly the Holy Grail we were discussing or alpha strategies that are truly-

Adam:01:13:26Yeah. You should be focused in areas of the market and strategies where those large pensions definitionally cannot go either regulatorily or-

Rodrigo:01:13:37Like pensions don’t use hedge retail protection. Why should we? Because you’re not a massive multi-billion dollar pension plan. You’re smaller – you can do more.

Wayne:01:13:47Of course, so pensions don’t use them but pensions also worry about their liabilities. They don’t have that but they’ve had the benefit of a 20-year bull market. Let’s say there had been a different path and now they’re coming up against contingent liabilities that they can’t cover. Are they now thinking and being proud of their achievements as they were before? So all of this is on the heels of the empirical view that we’re all looking at that the market’s gone up for 20 years. That’s great but I don’t know that that’s happening for the next 20 years.

Adam:01:14:21No. We’re on the same page.

Wayne:01:14:23What’s that?

Adam:01:14:23100% agree. We’re all I think in violent agreement. The main point was that these big pension plans don’t really have any other option. They’re HODlers because they don’t have any opportunity to…You know CalPERS can’t hedge away all of its beta risk because if they do they will be the tail that wags the dog. They’re in it for the long haul and the typical small pension or endowment or family office, they should definitionally be looking for ways to invest that CalPERS and the CPP and Ontario Teachers and the Norwegian Sovereign wealth fund are not investing because they can’t invest and accrue any meaningful diversification from going in different directions but your typical family office has a wide variety of different opportunities to diversify and seek alpha that these big institutions don’t have and it’s this strange phenomenon that so many smaller institutions try to model themselves after these larger institutions when they should actually be going in the opposite direction.

Wayne:01:15:36It’s a different objective. Now I fully capture what you’re saying and I couldn’t agree more and I like your word. I violently agree but it’s funny, I think of like the adage from 2008 was too big to fail so the other side of that is too big to hedge. So that’s where we are, in that landscape. So we’re not talking to those people for that reason. It’s too big to hedge.

Rodrigo:01:16:04Gentlemen I have to pop, I have a dinner party that I that I promised I’d be on time for. So you guys keep going.

Adam:01:16:10Nobody wants to hear about your excuses, but that’s okay because…

Wayne:01:16:14Have a good dinner.

Rodrigo:01:16:15I got to, keep going. See you.

Adam:01:16:18Well, we covered a lot of ground. Mike I don’t know if you have any other questions or Wayne if you have any other things you wanted to hit.

Mike:01:16:24You really want a closing remarks Wayne?

Wayne:01:16:26No. Honestly this was very enjoyable and before we got on you said Adam it was just going to be free-flowing, so my only comment is that was a fantastic free flow. It was really good. I loved where the conversation went and I hope it was insightful for everyone and that’s it. Happy to be on.

Adam:01:16:48It’s a tremendous conversation, learned a lot and shared a lot and hopefully we get a chance to do this again sometime.

Mike:01:16:54Yeah. I’m going to take the hit and make sure everyone listening if you can just like, review, subscribe, make sure you get your reminder notifications in YouTube about who we’re having on. Who do we have on next week? Do you do you remember? I should be better at this.

Adam:01:17:10Before mentioning it you should probably know who it is.

Mike:01:17:12 I should probably look it up but I can look it up with a bit of…Wait, no. Next week is Thanksgiving.

Adam:01:17:18Next week’s Thanksgiving that’s why.

Mike:01:17:19We’re going to try. We might be able to wrangle something, Wednesday call with somebody but we’ll have again good reason to stay tuned and hit the subscribe button and again lots of likes and comments are very helpful for building the audience and getting on great guests like Wayne and whatnot.

Adam:01:17:40Thanks to everyone who showed up today and asked questions and participated.

Mike:01:17:43Wish you that as well.

Wayne:01:17:45Yeah. Thank you.

Adam:01:17:46All right. Thanks for watching. Have a great weekend, see you.

Wayne:01:17:49You too.

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