In this episode I speak with Gerald Rushton, senior member of the QIS Structuring team at Macquarie Bank.
Our conversation largely revolves around commodity strategies, including thoughts on trend following, commodity carry, commodity congestion, and commodity volatility carry. Gerald argues that the latter three are particularly well suited to be paired with equity hedging strategies, and we spend quite a bit of time discussing the major design levers behind each strategy.
Gerald also provides some insight as to how QIS desks have evolved over the past decade, why he believes QIS desks can provide unique edge, and the many ways in which they can customize mandates for clients.
Please enjoy this conversation with Gerald Rushton.
Transcript
Corey Hoffstein 00:00
Okay, you ready? Go? Yes. All right. 321 Let’s go.
Corey Hoffstein 00:09
Hello and welcome everyone. I’m Corey Hoffstein. And this is flirting with models, the podcast that pulls back the curtain to discover the human factor behind the quantitative strategy.
Narrator 00:21
Corey Hoffstein Is the co founder and chief investment officer of new found research due to industry regulations, he will not discuss any of new found researches funds on this podcast all opinions expressed by podcast participants are solely their own opinion and do not reflect the opinion of newfound research. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of newfound research may maintain positions in securities discussed in this podcast for more information is it think newfound.com.
Corey Hoffstein 00:52
In this episode, I speak with Gerald Rushton, senior member of the QE is structuring team at Macquarie Bank. Our conversation largely revolves around commodity strategies, including thoughts on trend following commodity carry commodity congestion and commodity volatility carry. Gerald argues that the latter three are particularly well suited to be paired with equity hedging strategies, and we spend quite a bit of time discussing the major design levers behind each approach. Gerald also provide some insight as to how QE is desks have evolved over the last decade. Why he believes QE is desks can provide unique edge and the many ways in which they can customize mandates for clients. Please enjoy this conversation with Gerald Rushton.
Corey Hoffstein 01:39
Gerald, welcome to the show. Thank you for joining me excited to get what I think is going to be a pretty differentiated perspective in this episode.
Corey Hoffstein 01:48
Thanks for having me, Cory, excited to be here.
Corey Hoffstein 01:50
Well, I would love to just begin with your background for the audience who maybe doesn’t know who you are, or doesn’t have maybe a lot of context for Aquarius a bank, particularly us listeners, maybe you can do a little bit of table setting for us.
Gerald Rushton 02:04
So my name is Gerald Rushton. I started my career at Lehman Brothers working in subprime mortgages. And you guys may have heard, that ended pretty badly for everybody. So at that point, I needed to find a new career, I’d studied computer science, pretty quiet guy. So I found out about systematic investing. I’ve been doing that ever since. So at Macquarie, which is a Australian investment bank, I work as part of the QA s structuring team. So quantitative investment strategies. And what we do is we research, design and implement systematic trading strategies across all asset classes. What does that mean? Well, like how the s&p 500 has a weighting scheme and rules to determine the level, the rule being market cap weighted, we build indices that have maybe different weighting schemes and different analyzers, trying to implement things like trend carrying value across different asset classes. And then cons can access those products. In a way you can access any index product and swap an option, a fund an ETF, you name it, you can get as exotic and funky as you like, we can do it.
Corey Hoffstein 03:13
So I’m going to come out of the gate swinging a little bit here, maybe address the elephant in the room, which is that bank cue is desks have historically had a bit of a mixed reputation when it comes to launching systematic strategies. There’s a number of cases where you sort of get that classic up into the right index goes live, and then it flatlines. Why do you think this is historically been the case? And do you think that QA s desks have evolved over time to address this problem?
Gerald Rushton 03:25
Definitely come out swinging there. So you’re referring to the hockey stick profile. As you said, the backtest looks amazing, then you can pinpoint the date and it’s live, because then it starts trickling down. And I think there were a lot of examples of that in the past 10 years or so ago. But nowadays, I don’t really think that’s the case. I think there’s a couple of things to say about that. One, everyone has been guilty of a bit of back test overfitting the past, when you’re a bit younger, bit naive, your expectations are not particularly grounded, then you want to make some mistakes. I think one of the disadvantages we have secure is businesses that we’re very transparent. You can see the performance of every strategy that we launch. And so of course, it’s easy to find a few industries that have the hockey stick, and one of them and make a few checks, have I’d say the industry has really evolved the last decade or so. So as way of background, the big kind of boom in queue is desks, happened after the publication of what’s known as the three professors report by an Desmond Schaffer, which was basically commissioned by naugus Bank, the sovereign wealth fund of Norway to basically ask the question, what happened the GFC we thought we were diversified, but we still lost a ton of money. And they basically said, Well, you’re not really meant Following the literature and the developments we’ve had in finance. And there are other factors that were missing things like trend carry value. So that point, a lot of the Nordic pension schemes started looking at these types of strategies, and looking to banks to implement them. And maybe 10 years ago, there was a bit of a rush to fill out the so called risk premium matrix, maybe a little bit of equity quality tourism, where people apply the classic decile longshot approach to the macro asset classes where it’s a little bit less of a valid approach, I’d say. But nowadays, we have plenty of very serious people in the business people publishing in top academic journals. And we have a lot more experience. I would also say the clients are a lot more sophisticated. There’s a lot of tough questions, they want to see all the rigorous analysis about parameter sensitivity, how you’ve come up with these models. So there’s definitely been a big evolution as a whole industry is doing a lot better now than it was 1015 years ago.
Corey Hoffstein 03:25
Part of the problem with the last generation of QIS indices was maybe an over reliance on academic work, or an attempt at directly porting equity quant strategies. I love that phrase you used maybe equity quite tourism was going on. And you can contrast that to where new ideas are coming from today.
Gerald Rushton 03:25
I think back then there was maybe a view that you couldn’t deviate from the literature that what was written in an academic paper was almost some kind of god given truth. And the only changes you could make were to restrict the universe to the tradable instruments, for example, and maybe add some transaction costs. So I think trend following was an interesting case there were prior to the publication time series momentum by muskets and all. There was a bit of skepticism about despite the fact that the CTA industry have been implementing trend for decades very successfully. Nowadays, people are much more open to ideas that not come from the academic literature. The porting thing is definitely a bugbear of mine as a macro guy. And here we’re talking about the idea that you need to be essentially dollar neutral, you need to rank things and go long, the top X short the bottom y. And in the macro space, you we have fewer instruments that are tradable, we potentially have higher correlations and things like rates. And also we have very wildly different volatilities, like if you think about commodities, like the vol, nat gas and the whole of gold are really not the same. So in a long, short decile type strategy. If you’re long, like nat gas, you’ve got a nat gas strategy unless you’re a little bit smarter about risk taking. And so like how trend was kind of validated by literature has become a staple of QIS desks, other hedge fund like strategies are now the staple of QIS desks as well. We get those ideas from our trading desk and speaking to clients and just trying to think,
Gerald Rushton 03:25
So I think really technology and sitting inside a global investment bank is where our edge comes from. There are a lot of strategies that it’s just not practical to run. Without that tech and trading capability like something like commodities, volatility carry a strategy, where being high touch selling options every day, multiple different strikes, delta hedging multiple times per day, across a whole range of commodities, it’s a pain, you need a lot of traders, you need a lot of data, you have models to do that. So what a lot of our clients faces, not a limitation in terms of their kind of intelligence or understanding what they want to do is just purely operations. And so being within a bank trading, that’s our bread and butter, that’s very easy for us to do. And a lot of these strategies, significant edge does come from in high touch. They’re also slightly more boring reasons why we have some edge, which is that there’s something called Rob Carver talks about in his books, you can’t run some strategies in small size, because of the contract size, it’s the size of the JGB contract is like a million dollars. So you’re not going to get to kind of very refined position taking unless you have very large notional. Also data. We spend millions of dollars a year on data, we spend millions of dollars a year on our tech team. It’s all very expensive to run systematic strategies. Well, the other advantage I think we have is that because we’re working very closely with the smart clients, we’re essentially almost crowdsourcing some ideas from them and refining ideas. Whereas in a hedge fund type setup, you might be working with a much smaller group of individuals. And so that kind of questioning and probing that we get from clients helps us improve the models.
Corey Hoffstein 03:25
In our pre call you mentioned that over 50% of the strategies you trade on behalf of clients are actually customized. Can you talk maybe to the sorts the strategies clients are looking for today and some of the ways in which you’re able to customize them.
Corey Hoffstein 06:04
How do you think operating within a bank provides an edge to the types of strategies you can deliver for clients versus say, the strategies a hedge fund could deliver.
10:05
So luckily, at Macquarie, we’re privileged to work with some really smart clients. And we work very closely with them. We’re sharing code that runs in Python. And these guys, as I mentioned, they know what they want to do. They’ve got really good ideas, they just can’t execute it. So we’ll develop a model with them. It could be a trend following strategy, it could be a carrier strategy. There’s many, many things we can do with them. And once we’ve kind of finalized their custom trend program, for example, we can put that in production and run that for them. And that’s obviously going to give everyone much better outcomes, better outcomes for them, because they get the product that they want a much better outcome for us as a bank, because those trades obviously likely to be more sticky, people going to stick with the program they designed. Whereas if it’s an off the shelf program, they might just get out of the trade as soon as it starts going wrong. There’s another much kind of lighter customization that we can do. So maybe you want a different volatility target, maybe you can’t trade agricultural commodities. All of these kind of smaller, light touch customization is possible to for relatively small size compared to what is offered in the asset management QC due to the just the fixed costs of setting up funds
Corey Hoffstein 11:16
Trend following as a strategy is at the forefront of everyone’s mind nowadays, particularly after last year’s performance of stocks and bonds. And cross asset trend has historically been a staple for most QIS desks. This is a strategy that’s now being pitched as positive Sharpe crisis help with providing an inflation hedge. And I know that you take some umbrage with this, particularly the crisis Alpha part. So I wanted to give you a little bit of a soapbox. What’s wrong with CTAs today?
Gerald Rushton 11:45
l just want to make it clear, I really, really love trend following I think everybody should have it in their portfolio. And I don’t have any problem with CTAs. What I have a problem with is maybe the way we’ve been pitching trend. As an industry. As you’re saying the pitches are so good. It’s pretty high. Sharpe, positive skewness does well, in an equity crisis does well and innovation crisis, what’s not to like? I mean, it’ll even tuck you in bed with a bedtime story and give you a glass of milk. It really is the ultimate strategy. I think the issue there is a lot of that pitch is not true. If you’ve not been very thoughtful about how you’ve built the trend following strategy, and particularly started looking at the evidence. We’re pitching trend as an industry, including my past sell as a crisis hedge. But what happened to q4 2018? Well, trend following was down what happened in the acute period and stress in the COVID. Era? Well trend following Stan. So I was quite sure that when we came up the idea of crisis alpha that it was supposed to be a positive alpha, not a negative alpha. So we’ve got a bit of a problem here. And that has led to a lot of disappointment from investors and a lot of mockery in the media. As an aside. And this is not obviously investment advice. If you do see an article in the FT saying quants, bamboozled by markets, that I’m pretty certain it’s got 100% hit rate in terms of the time to get max long trend following, someone should write a research paper about that. So you can build trend to get very different payoffs are the same kind of rules. People nowadays have been talking about faster trend following and more long term trend following. And those strategies have very different payoffs. And you need to understand the kinds of goals when you’re pitching them trend. What are they really looking for what is even a crisis mean? Some people really do care about a really bad week. Some people care about really bad here, and we need to make sure that the clients expectations are met by the transplant program we have provided to them. One of the other issues that I have is the famous convexity chart. So everyone’s seen it use plot quality returns of s&p 500 versus quality returns of the CTA benchmark, choose your favorite. I like the new hedge one. And you see this lovely strap like profile. And you say hey, and also remember the phone shape paper. The risk in hedge fund strategies trend is like a look at straddle QED it’s long vol it’s a hedge. The issue is if you look at the data over the past 20 years, it’s just not true. So yes, you plot the quarterly returns of s&p versus the new edge index, you do get a straddle this is not a robust result. You remove a single point you remove q4 2008. The whole story disappears. It’s no longer convex when you’re looking at the CTA industry as a whole. Of course, there will be people who still are convex. And I bet those guys are doing shorter term trend following in our deck we have a chart which really kind of blows people’s minds when they see it, which is putting the skewness versus Sharpe ratio of course as a trend following so one day trend following or let’s say one year trend, following up What you see if you do that exercise is also a bit alarming is trend becomes negatively skewed. When you start looking at trends over six months, it’s got a great Sharpe ratio. But it’s no longer this positively skewed convex hedge that we’ve been pitching as an industry. The other issue I have with the way as an industry have been pitching trend is really in our explanation of it, like why does it work? So there’s a lot of papers about their behavioral aspects, you know, the under reaction, and then subsequent overreaction to news, the institutional lags that can exist in absorbing that news, creating that effect, overcrowding, hurting all these kind of things? And then there’s some truth to that. But I think what we’re not talking about is they’re also rational reasons why trend following could exist. Think about Eurodollar futures are now safer futures. Those are really just tracking the policy rate of the Fed. And what is the Fed doing? Well, it’s reacting to the business cycle, and inflation. And those are very cyclical. If you check the quarterly changes in CPI, it looks like a random walk until you condition it on the previous change. And so if the last quarter CPI rose, the next quarter, there’s an 80% probability that it rose like there are trends in the economic cycle, and the Fed is reacting to those trends. Now, maybe there’s a question, why do they not react faster? Well, if you read Ben Bernanke is essay on Central Bank, gradualism, you’ll understand that there’s a reason why central banks are cautious and act gradually. And it’s basically long and variable lags. So I think as an industry, we just need to do a better job of communicating why trend following is a great strategy is having a portfolio, but also make sure that we’re giving people the right type of trend following for the objectives. And I should probably stop now before getting any more trouble.
Corey Hoffstein 16:54
Well, one of the things you mentioned there was the ability to customize the payoff profile of trend following and one of the things we often see trend following strategies that are particularly pitched as being crisis Alpha trend strategies, as they impose different limits to explicitly trying to create negative betas to equities. So for example, you might see them impose constraints that they can never be long equity index futures, or that they could never be short bonds, given the desire to create a payoff profile that is convex during periods of equity crises. Do you see these sorts of constraints as ultimately being useful?
Gerald Rushton 17:29
I think it’s a really good idea. The issue is it’s fraught with danger. Estimating betas is hard. Like we know that. How are you going to define your covariance matrix? Are you going to use shrinkage? The estimation of covariances is such a complex topic that when we go through our monthly review, or for the academic journals, our team finds 234 papers and better ways to do it. Now, it’s about intraday data. It’s about using machine learning. So it’s hard thing to do. And one of the issues with this is that we know that these betas can change. And the problem with using it in a trend following program, in my opinion, that’s supposed to be a hedge is that the time these betas change is a crisis period. It’s kind of changing at the exact time, you need to view super accurate. So I think it’s a good idea. But if you’re not doing well, it’s going to be potentially backfire. And I think that’s quite a bad situation to be in with a client, right? It’s different, in my opinion, that if your trend following estimation of beats goes wrong, when you take a big loss, that’s a very bad outcome. Whereas if you were trying to say just be to hedge your portfolio, or something like FX carry, which is highly procyclical, and it goes wrong, you’re probably still going to significantly reduce the drawdown, and the clients probably going to be quite happy. So I think it’s so great area of research and good idea, but if it’s done poorly, not so great, I think trend, you should let the data speak for itself. It’s very rare that we have a true exogenous shock, even COVID. We knew about it really three months before the market. So I think it’s better to just focused on making sure your implementation is robust in terms of having the right kind of signals the right kind of rebalancing rules, the right overall risk taking than trying to be a little bit too swamped.
Corey Hoffstein 19:24
I think, for most institutions, and perhaps individuals, the Holy Grail is finding a strategy that can provide that convex hedge that we’re looking for, and then pairing it with a strategy that can carry positively and offset the bleed of the hedge. And the risk, of course, is choosing a strategy that carries strategy that blows up at the same time the hedge kicks in ultimately negating any benefit from the hedge. Curious what sort of strategies you think might fit in this bucket.
Corey Hoffstein 19:50
This is the million dollar question. Right now. We’re working with clients in every single region on this question. Broadly speaking, obviously, people can buy puts But that’s very, very expensive. So people are turning to statistical hedges and shorter term trend following syscall hedge. There are other ones, like rates Vega intraday trend following. There’s a whole bunch of ideas out there that have shown good returns in crisis periods. The issue with this is, with the exception of, say, rates vaguer, they’re going to probably carry it negatively. And that makes it hard to live with. What I’ve seen in my career so far is people put these hedges on, it bleeds for one or two years, and then they decide to take it off, or their managers tell them to take it off, or they’re bored, or it might be. And obviously, the moment they do that, it works really well. And it’s a crisis. And that’s just the way life is. So we as quants probably think of ourselves as hyper rational beings that would never ever do such a thing like that. But that’s not really the world we live in. So what we’ve been trying to do, and others is to pair these negative carried strategies and suggested with positive carry strategies. And we find that commodities markets are really an interesting place to be looking for these sources of returns. Because things like aunties carry, and congestion, they have pretty good performance through time. And the times that these strategies underperform don’t always coincide with equity beater, basically. So you’re not likely to blow up at the same time. And so that’s the kind of space we’ve been looking at. Obviously, the story doesn’t end there, you then need to think about well have i size these two legs? And we’ve done a lot of work on that. But in terms of the strategies, commodities, I think is the best place to be looking.
Corey Hoffstein 21:43
Let’s dive into some of those strategies. And maybe let’s start with commodity carry. Can you provide an overview, what is commodity carry? Why you think it fits the bill have this sort of positive carry strategy that maybe is less correlated with equities? And then I hate doing multi part questions. Here we go is a multi part question. I would love to know from your perspective, what the major design levers are, that can influence the return profile of this sort of strategy,
Corey Hoffstein 22:10
quantities carry. It’s not a strategy that’s particularly well covered in the literature. And it’s basically a curve strategy. And so the simplest implementation would be something we would call F three versus F zero carry. And for example, you’d be short, the August WTI, contract, and long the November WTI contract. And you do this across the liquid set of commodities that you have, what is the point of doing this is that commodities curves are driven by basically storage costs, which gives these past trades long and short contracts, different role yields that you’re trying to capture. So it’s not really about roll up the curve or carry between commodities, like you might see in FX, it’s really the story of role yields. Now, even the simplest implementation of commodities carry on, say, a beacom. Universe, it’s got a pretty high Sharpe ratio. So post cost, you’re looking at something well north of one as a Sharpe ratio, and pretty consistent returns through time. So that makes it very, very attractive as a funding leg for these kind of negative carry strategies that we were mentioning earlier. And it is a risk premium strategy. So it’s not free money, it’s an arbitrage. But those risks are typically commodity specific shocks, typically to the upside. So a drought in agricultural markets, stocks can be bad for commodities carry strategy, or the invasion of Ukraine in terms of oil prices spiking. But most often than not, these risks don’t coincide with the big macro shops. And in fact, it’s actually the opposite. Typically, in a big macro shock, the front end of the commodities curve, particularly oil sells off very rapidly giving these strategies, a little bit of an extra kick in those crisis periods. So also make them pretty attractive. Now, that was pretty much the most simple implementation could describe the F three versus F zero. In terms of the levers? Well, obviously, the first question is, what contracts am I going to select? So in commodities, we have pretty liquid curves. So unlike equities, where you can only trade the front month apartment during the roll period, you’ve got good liquidity, two or three years out in most commodities. So we’re on the curve tool to be taking longer curve spreads. So not just three months part is going to give you more follow potential return, but it’s a little bit riskier. Maybe you want to be dynamic, why stick to F three, f zero and f six F zero? Maybe you could try and identify the best places to be on the curve dynamically. So once you’ve done that, you need to think about sizing. So do I want to be notional neutral? Do I want to be locked neutral or do I want to use some risk based measure? Maybe I want to be voltage drop, maybe on a bar for the race literature, and do something really funky like a PCA weighted exposure. That’d be pretty cool. And so now you’ve got your, what we would call a mono alpha. So you’ve got your dynamic risk weighted single commodity curve strategy, well, how do I make a portfolio of this? What’s my objective, if I want to do massive size, then I’m probably gonna go be calm or GSCI weighted, because that’s where the liquidity is becoming GSCI indices, that weights are basically determined by liquidity in production. But if you’re less concerned about that, maybe you could think of a better portfolio construction technique. And there are obvious ideas out there in the literature about how to combine portfolios of alphas, the value added of a cure structure is that we understand what these levers are. And we can help explain them to you and make sure you’re getting the right implementation for your portfolio.
Corey Hoffstein 25:57
I would imagine that one of the other potential benefits being at Macquarie in particular, is that you are a commodity Bank, which would mean compared to many folks on the buy side, or perhaps even in comparison to many other banks. On the sell side, you have access to a larger universe of commodities. Can you provide some examples of maybe these non benchmark commodities that you might have access to and talk a little bit about the impact of including them within trend or carry strategies.
Corey Hoffstein 26:23
So as well as background of the listeners, when we refer to the benchmark commodities, we’re referring to the commodities inside the beat calm and the s&p GSCI universe, these are the economists you know, love Brent, WTI, gold, copper, wheat, etc. The non benchmark quantities are basically all that other commodity futures that didn’t make the cut, and they’re not particularly exotic. Like you’ve got things, orange juice futures, that obviously made by famous biofilm Trading Places, you have UK, natural gas, European natural gas, you have London cocoa has a whole range. And there’s a good number of liquid instruments that you can trade systematically. There’s also the onshore China commodities, these are still they’re not strange things. But maybe the contracts a bit more exotic to us, for example, there are egg futures, or glass futures or plastic futures, but there’s commodities. It’s just the real world things that we use to build stuff. Obviously, the astute listener will be thinking, well, that lists liquid that must be really expensive to trade. And luckily, that’s not true, they are a little bit more expensive. There are a few exceptions that are a lot more expensive. And as I think some of your previous guests have hinted at getting access to onshore China, commodities is not easy with offshore money, but it can be done. If you take the relatively low cost liquid seven non benchmark commodities that are outside of China, and apply trend following for example, one of the things you’re gonna see is you’re gonna have almost two and a half times the Sharpe ratio post cost compared to what you’d see in a benchmark universe. So just very standard trend following windows, you have between one and 12 months 2.5 times the Sharpe ratio in last 15 years. So that’s pretty cool. We’d like to we’d like higher Sharpe issues. The question is, what’s going on there? Why is that the case? Is that just some kind of historical fluke? Or is there a reason for that? And the reason isn’t necessarily what I think most people think we’re using the same model. So it’s not like we’ve got a better model. And if you looked at the data, it’s not really the case that these commodities are trending more than the benchmark commodities, maybe with the exception of natural gas last two years. And you’re really what is happening is the correlation between the individual trend strategies is just much much lower. So okay, well, that’s why but high Sharpe ratio. But why is the correlation lower? Is that a fluke? And again, there’s a fundamental explanation that basically, the benchmark commodities are part of the asset allocation toolkit that most kinds of asset owners have, and they are subject to flows from pension funds, sovereign wealth, funds, etc. And so they’re more financialized. And there’s been a ton of papers over the last 15 years or so about this topic. And, you know, essentially, there’s much high correlation between professional commodities. And while the non benchmark commodities remain outside of there, it’s not likely that the pairwise correlation will increase. The trends in say orange juice futures are being driven by the fundamentals of orange juice, you’re getting a more kind of like idiosyncratic trend than, let’s say like commodity betta trend, curb carry. Unfortunately, the curve is not quite as liquid. So you’re going to struggle a bit there, but sort of a trend following super useful instruments, as I know none of your guests have piloted before.
Corey Hoffstein 29:54
One of the things we saw in the early 2000s to sort of around 2000 10 was that there was a large increase in the demand for commodity strategies, you can just commodity beta. And one of the strategies that became popular was this idea of commodity congestion. And we’re starting to see that again, as commodity strategies ramp up in popularity. I’m starting to hear a lot more people talk about commodity congestion strategies. Can you talk us through what is a commodity congestion strategy, and maybe once again, talk about sort of the major design levers that can influence the return profile.
30:29
So you’re referring to a strategy known as the gstr roll, which is a super popular contest congestion strategy, it’s been around for 25 years or so. And the basic idea is that all we had any congestion strategy is that you know, of an event that is going to cause a lot of trading, I don’t need something like NFP where obviously we’re not trading, but you know, the trading, that’s going to happen. So how do you know this? Well, you go to the s&p GSCI website, you download the index rules of the index. And you can see what days it pulls its positions. And so if you want to know that there’s a lot of assets following that particular index, then of course, you could position around that and essentially provide liquidity during the roll period, and earn a pretty decent return. Now, these strategies are obviously not as well known in the academic literature and more kind of practitioner strategies. But as you’re saying, they’ve been around for a long time. And they exist across all asset classes. So examples in FX could be around the 4pm, WR fix in rates, you could think about bond auctions, you could think about the duration extension at the end of the month, and an equities we obviously have things like stocks entering exes in the indices and other effects. When it comes to congestion, clearly, then you need to know is really the intricacies of the market? Who are the players? What are they doing? Are they still behaving in the way you expect? So people don’t necessarily invest in just the front month of the quantities universe. Now, there are such things as deferred indices, where you might go a bit further down the curve. So in terms of the Levers, it’s obviously very strategy specific. But really, the three things are kind of adjusting are, how early to enter how late to exit? And how do you kind of risk manage that portfolio of congestion strategies to make sure you’re not taking on other unwanted risks. And the other thing is you need to keep consistently innovating. So the original GSCI strategy, a simplest implementation, in the pitch mentioned, Sharpe ratio too easy. Nowadays, that strategies a lot weaker, but there are ideas you can use to make strategy dynamic, and those dynamic implementations still performing really well.
Corey Hoffstein 32:52
So we could probably argue that commodity carry is more of a risk premium. But congestion strategies are much more of a behavioral phenomenon and can probably, therefore, more easily be arbitrage away. How do you make sure that congestion doesn’t get congested?
Corey Hoffstein 33:09
Absolutely, it is behavioral, it is an anomaly and it can be arbitrage away. I think the issue with congestion or the master congestion versus other arbitrage does is that the behavior is dictated by the index rules. If you are hedging an index exposure for a client, if you don’t follow the next rules, you’re taking a basis risk. And that’s not necessarily everyone’s job. So you need to keep track of the index footprint, try and measure what the flows are into the common GSCI. And the ways you can do this, you need to think about measuring congestion returns to try and find out if it is being traded or not. There are ways you can track this and ways you can think about where to play congestion. So yes, congestion itself is congested. Well, maybe I could have a strategy around that. As I mentioned, we’ve seen a lot of consistency across the dynamic implementations. And they’ve generally done a pretty good job of kind of outperforming the more simple static ones. And yet, you just have to keep innovating both in commodities, and other asset classes. A good example is, as I mentioned, the FX WR fix. So the idea here is that at 4pm, at the end of the month, people adjust the FX hedges like so global investors. And so people used to try and catch that Premier, just really using quite slow trading rules, your trade 4pm The day before, exit at the end of the month. Well, that strategy infant in that way has deteriorated significantly in the last decade or so. But maybe you could be a bit faster. Maybe you could trade just a few hours before the 4pm Fix. That’s obviously if you do that, it’s gonna prevent you taking too much like general macro risk, which will obviously did a lot of last few years in terms of data releases and the Feds features. So yeah, Innovation is key in contrast
Corey Hoffstein 35:00
To taken somewhat to an absurd limit, a congestion strategy gets implemented further away from an index role, in many ways just becomes a carry strategy. Can you discuss maybe how these two strategies interact and how we should think about allocating to them so as to avoid doubling up on the same risks?
Corey Hoffstein 35:19
Yeah, it’s definitely a fair point. So in commodities, both of those strategies are what we’d call short spread strategies. So there is going to naturally be some correlation. I think the key thing is just first to be aware that this is what you’re doing. The issue is, when you start using dynamic presentations, it becomes quite a tricky exercise to think about that. And so I think just awareness of the problem is key. And making sure like in the worst case scenario of them both having exactly the same positions, that you’re not taking on too much risk, in terms of the other issue about doing congestion strategies earlier and earlier. And that can be carried trade. I think, as long as you’re measuring how much of your congestion returns has come from carry, and then you still got that congestion spirit of your strategy, still probably okay. And those returns can be quite different for different countries as well. It’s about awareness and just keeping track of what is going on. And making sure you’re not doubling up in the wrong times.
Corey Hoffstein 36:22
So one of the strategies that you mentioned earlier, though, didn’t explicitly mention here. But something that’s top of mind for you is commodity volatility carry, I’d love to dive a little bit into that strategy. And maybe start with a comparison into how it’s similar or different than equity volatility carry.
Corey Hoffstein 36:41
At, it’s cool, it’s the same, you are selling options at a high implied volatility that is higher than the subsequent realized volatility. And you’re capturing that spread by selling an option structure like a straddle and then delta hedging that package. Also, the return profile is similar in each day, you’re probably earning a little bit. And then in a period of stress, when realized bowl jumps, you’re probably going to take a pretty big loss. So in that way, they’re extremely similar. The differences come in that the tails don’t necessarily overlap. Like a lot of people argue that vault carries essentially like an insurance product to insure in the market, and how insurance businesses work or they diversify, the things are ensuring and so quantities for carry, there’s at least eight 910, very liquid commodities, you can do this on, and then you could theoretically do it in the full universe, but smaller size, so up to 24. So you’re getting a lot of different tail exposure, it’s still earning that kind of VRP, the volatility risk premia. Clearly, in a big crisis period, like the GFC COVID, there is going to be some overlap in the tails, you need to be aware of that. You also need to be aware that each market has its own subtleties in terms of the players in that market and where they are active, it is not necessarily the case that the front of the curve is the best place to be in. And so yeah, commodities can be pretty weird, it’s best to go in there with a little bit of fundamental knowledge, and make sure you’re not making any terrible errors.
Corey Hoffstein 38:17
So on that topic of fundamental knowledge, despite the Q is name, I know that you and your team lean heavily on your colleagues who can provide a lot of fundamental insights into these different markets. So maybe you could provide some examples of how those fundamental insights make their way into the quantitative strategy design.
Corey Hoffstein 38:36
So we’re definitely not in a pure math cap. In a way you’re just kind of applying math to data and hoping for a good outcome. We think it is important to try and understand the fundamentals of the market, not just in commodities, but elsewhere, and trying to incorporate those into the strategy design. Now, when I’m talking about fundamentals, I’m not talking about the view from the research team, about what will happen to oil in the next couple of months. And talking about the market structure, things like seasonality, information from the trading desk about who are the different players in different parts of the curve. Like in rates, we have the concept of the market segmentation hypothesis, and quantities, producers, consumers and speculators are active in different parts and you should be aware of this. It’s really kind of like a form of craftsmanship Alpha. As you mentioned, seasonality is a pretty big part of the commodities complex. And what we mean by seasonality is that there are some commodities which have very distinct seasons. So natural gas in the winter behaves completely different to natural gas in the summer, as a lot of commodity strategies are curve based, taking a cross seasonal spread, can you expose you to a risk that you potentially not aware of? And while those risks can be measured, and may have historically been compensated uted we may also be suffering from a piece of property. So natural gas is a good example there were. Historically it looked like taking winter summer spreads was a positive risk premia. But then in winter 2018, we saw that that may not actually be the case. So it’s just being aware of what you’re doing on this market, it’s not quite as simple as other markets might be in terms of the market structure.
Corey Hoffstein 40:27
I think for those of us coming from an equity quant background, we can be caught off guard as to how important that seasonality element is, particularly in commodity strategies. You mentioned that gas, I love to just think of the coffee example where you go along the far dated coffee contract and short up front dated contract, you could be talking about totally different harvests, they are truly very different deliverables. That’s maybe hoping you could talk a little bit more about how considerations for seasonality find their way into these different commodity strategies, how’s it work into carry or congestion or volatility carry?
Corey Hoffstein 41:02
We did touch on this a little bit in the previous answer. And really, the adjustments can be pretty simple. You don’t have to do too crazy. So when we’re talking about carry, maybe you just want to adjust the roll schedule. So you’re doing let’s say, the F three F series strategy I described earlier. But you know, when you get to a cross seasonal spread, you just adjust the roll schedule. So if you’re taking a long day to contract, and you’re no longer taking to that winter, summer spread in voluntary, it can be just as simple as not selling options in those periods or when it comes to agricultural commodities. As you say, something like coffee, you may be looking at completely different harvests. And the risk and those types of strategies comes at harvest time, terrible weather, just as you’re about to harvest a coffee or wheat or corn or whatever, can result in extremely large moves. But just for a single contract, right? So we don’t have to be too complex with these adjustments. It’s about being aware of where the seasonality is, and deciding whether to take that risk or not. But to avoid that, you don’t have to be too sophisticated.
Corey Hoffstein 42:08
Taking a quick turn off the commodity topic here in our pre call you mentioned that FX carry was starting to make a comeback after being largely left for dead post GFC. What do you think we learned from the crisis and why is now a good time to be considering the strategy.
42:27
FX Carry is really having an incredible couple of years last few years, since central banks have started hiking, there is essentially just more carry to harvest. So in terms of what we learned coming back full circle to what I’ve seen about the evolution of Qs desks, effects carry is one of the oldest Qs strategies out there, there are implementations from 20 years or so ago. But the construction there was really incredibly naive, you’d have a G 10 universe, you would rank them from one to $10 billion dollar good long, top three short the bottom three equal notional, and then you would rebalance once a month or maybe even once a quarter. And so those strategies actually did pretty well, from the period of say 2002. To those three ups, the GFC were essentially the equity beta of FX carrier was exposed. There’s also been a ton of academic literature about this and the explanations being of why this person exists. I think it’s like shocks to consumption growth, liquidity premia, etc. And so you have something which turns out to be very, pretty simple. And post GFC. There wasn’t much carried to capture, so you had absolutely no upside, pretty much, and then all the downside of equities. And obviously, most clients have got enough equity beta in their portfolio already. So definitely left for dead. There are other issues, that kind of portfolio construction, which is your often hide carry currencies, particularly when you look at the EM space, are having a crisis of their own. So if you’re just doing, say, a simple, equal dollar weighted approach, then again, like how is that how that gas can dominators a simple, long, short strategy, you can just have your whole strategy being driven by a single currency, which is really not great. So the latest strategies in the space are really trying to think about this. So making sure the marginal contribution to rest of any one particular currency is not too high, making sure that you have adjusted the weights to be neutral to equity market beta to make the strategy really deliver, like let’s say the pure alpha of efforts carry without any additional equity meter. And by doing that, you can make the strategy a lot more attractive to clients because it gives them something a little bit different compared to those much more simple implementations
Corey Hoffstein 44:53
Ok Gerald, we’ve come to the end of the episode here and I want to ask you the same question I’ve been asking everyone this season. which is about the tarot card, they chose to inform the design of the cover of their episode. And you chose death, which I thought was an interesting choice. And I was wondering what was it about the imagery, or perhaps the cards meeting that spoke to you either personally or professionally,
45:18
My colleagues initially suggested I gave for the tower, because I’m really tall. And then that do you want us to would have been a better approach because I like to be thorough in my research, and I really fell into a deep, deep black hole of research into tarot, haven’t known nothing about before, I would have saved a lot of time, but listen to my colleagues. And that’s the case with Taro. And also for quantum research, I’d say, I certainly know that she’s death really, for three reasons. One, it is the most misunderstood card in tarot, it doesn’t really mean you’re going to die. And that resonated with me because I think cuantas basically the most misunderstood strategy, or at least investment style out there. And as we’re doing now, I spend a lot of my time trying to demystify quant investing, and get people comfortable with that, because I just think everyone should be doing it. So that was the first reason. The second thing is that it also represents the change of thinking from an old way into a new way. And as I mentioned, again, that kind of resonates with me in terms of trying to get people away from that, like very old school 1960s 1970s. Were investing. The idea that there’s nothing predictable out there, that everything’s a random walk, if you will, not being aware of the other kind of risk premia out there that they can use to enhance their portfolio. And also thinking in ways that not supported by the evidence, we try to be as quant super data driven, and make decisions based on data and not emotion. So that also resonated with me. And then thirdly, in the image in a very specific card, so the Rider Waite Smith, tarot series, the image of death features a white rose in the background. And it’s part of a niche English person answer but basically the white rose as the white race of Yorkshire and as a part of the auction, that also resonates with me. So those are the three reasons I went for it. But as I said, Probably should have listened to my colleagues who thought jealous at all go for the tower.
Corey Hoffstein 47:20
Well, I appreciate you putting them in all the time and effort to research it. I hope it was at least fun. As fun as this episode. I really appreciate you coming on. I know the listeners will likely have learned a ton about commodity strategies. So thank you very much.
Gerald Rushton 47:32
Thanks, Cory. Pleasure to be here.