Speaker 1: Hello, and welcome to another episode of the Odd Thoughts podcast.
Speaker 1: I'm Tracy Allaway and I'm Joe. Wisn't all Joe? We
Speaker 1: have been watching the VIX, haven't we? We have? Um,
Speaker 1: I mean it's been We've had a lot of episodes
Speaker 1: this year for pretty obvious reasons, looking at the VICS,
Speaker 1: exploring volatility, what signal is embedded in it, what it
Speaker 1: tells you, what it doesn't. But people love hearing about
Speaker 1: the VIX, so people, I'm always up to talk about it.
Speaker 1: You know, as I said that sentence, I kind of
Speaker 1: realized what a week intro it was, and I need
Speaker 1: to work put more thought into how I start these things.
Speaker 1: But yes, it's fine, it's fine. We talk a lot
Speaker 1: about volatility. We talk a lot about the market structure
Speaker 1: of aol utility, but lately we've been talking about volatility
Speaker 1: more in the short term, in the sense that we
Speaker 1: have this really big potential tail risk on the horizon
Speaker 1: in the form of the U S elections. And if
Speaker 1: you look at the VIX, even though it's relatively low,
Speaker 1: if you look at the actual term structure or the
Speaker 1: curve of the VIX, you can see it's quite well
Speaker 1: it's not as elevated as it was a few weeks
Speaker 1: or months ago, but it's still elevated, um, compared to
Speaker 1: normal right around the time of the elections and for
Speaker 1: a few weeks after that. So that's a bunch of
Speaker 1: people pricing in the risk of something unexpected happening in
Speaker 1: the U S elections. Yeah. Absolutely, I mean this has
Speaker 1: been building for a while. I shouldn't note we are
Speaker 1: recording this Tuesday, October twenty, so we're literally two weeks
Speaker 1: away from November third election day, although by the time
Speaker 1: you hear this, I think, based on when we expect
Speaker 1: release it, it's going to be about a week. Yeah,
Speaker 1: you're absolute lutely correct. Which is that for several weeks
Speaker 1: or months now, um, the volatility term structure has been
Speaker 1: super bit up, not just at the beginning of November,
Speaker 1: but for a long period as the risk of a
Speaker 1: long drawn out count is a possibility. Some of that
Speaker 1: has come in a little bit less concerns about that
Speaker 1: as some of the polls have widened between the two.
Speaker 1: I mean, I guess there's always sort of going to
Speaker 1: be some expected volatility to round election. But the clear
Speaker 1: thing is because of so much going on and all
Speaker 1: the uncertainty and economic situation. There is a lot of
Speaker 1: anxiety and uncertainty about the election and its ramifications. Yeah,
Speaker 1: it kind of for me. It brings to mind three questions. So,
Speaker 1: number one, our markets pricing in the results of the
Speaker 1: US election correctly? So are they you know, are the
Speaker 1: polls right this time versus what we saw in And Secondly,
Speaker 1: are investors We've discussed this before with Chris Sidile most recently,
Speaker 1: but our investors so well hedge now around the election
Speaker 1: that it's going to be really tough to actually spark
Speaker 1: a volatility event or Third, are we mistaking that's volatility
Speaker 1: premium um? And is it in fact market complacency? Should
Speaker 1: investors be more worried? So those are all sort of
Speaker 1: interconnected questions, but we're going to be discussing them with
Speaker 1: an All Thoughts favorite on this episode. I think this
Speaker 1: is going to be his third appearance on All Thoughts.
Speaker 1: It's Josh Younger, head of US Interest Rate Derivative Strategy
Speaker 1: over at JP Morgan Chase, and he's been writing quite
Speaker 1: a lot about this three episodes. Is that when you
Speaker 1: get the odd that's sorry, you get the sweatshirt. Josh
Speaker 1: will send that over to you after this episode. Welcome
Speaker 1: to the show again. Yeah, thanks, it's great to be back.
Speaker 1: So I guess just to begin, could you maybe laid
Speaker 1: the scene for us in terms of what we're seeing
Speaker 1: in market positioning on volatility. We spoke a little bit
Speaker 1: about how at one point it looks like markets were
Speaker 1: building in quite a big risk premium around the elections
Speaker 1: and beyond it. Uh you are? You wrote about this
Speaker 1: that it looks like investors were bracing possibly for a
Speaker 1: contested election. But we've seen that risk premium come down recently.
Speaker 1: What does that say about where the market actually is positioned?
Speaker 1: So maybe it's best to start by getting a sense
Speaker 1: of how we actually extract these numbers, because it's it's
Speaker 1: sort of easy to say, well, the markets pricing x,
Speaker 1: y or z for the election, but but ultimately we
Speaker 1: need to get this from some traded instrument and the
Speaker 1: price of that instrument. And with options markets, the price
Speaker 1: of the option is proportional to or related to the
Speaker 1: potential for large moves, So that insurance is worth more
Speaker 1: if the likelihood of a large move is greater, and
Speaker 1: so you have to pay up for that insurance, and
Speaker 1: vice versa. Think the like ahotive of largeness is lesser.
Speaker 1: And so what we do is we take options that
Speaker 1: expire after the election, and we compare the price of
Speaker 1: those options, adjusting for the extra time value of them,
Speaker 1: to the price of options that expire before the election,
Speaker 1: and from that you can get a sense of how
Speaker 1: much additional risk premium there is simply because of the
Speaker 1: fact the election falls in that window. And it's tough
Speaker 1: to do this with a lot of precision in most
Speaker 1: asset classes. So you're mentioning the VIX and those vixed
Speaker 1: futures are typically calendar months, so it's it's rather hard
Speaker 1: to isolate the election date itself. Although options do trade
Speaker 1: at that level of precision. It's usually best done, especially
Speaker 1: if you're going to compare different asset classes these like
Speaker 1: benchmark type structure. So one month options on UH rates
Speaker 1: and equities and foreign exchange and commodities and different equity
Speaker 1: indices and so forth, and you can compare that to say,
Speaker 1: three months options on the same and we did that
Speaker 1: back in late August early September, and you can pull
Speaker 1: out of that the risk premium and say UH isolated
Speaker 1: to just election day. That's an assumption you can make
Speaker 1: and you say how much extra risk is there on
Speaker 1: election day relative to some background level of volatility, because
Speaker 1: obviously if markets are are bald already, then volatility risk
Speaker 1: is gonna be more expensive. And so when we did
Speaker 1: that exercise, we got something like seven to eight times
Speaker 1: the typical daily move priced for election day across really
Speaker 1: a broad range of asset classes. That's not just the
Speaker 1: US equities, the VIX, the sp that was interest rates,
Speaker 1: the US dollar interest rates, it was foreign currencies to
Speaker 1: some extent, especially dollar C N Y exchange rates. So
Speaker 1: that makes sense, that's a gio political element of the election.
Speaker 1: You can see it in gold, you could see it
Speaker 1: in oil, you can see it in credit markets, you
Speaker 1: can basically everywhere that options traded. You could see something
Speaker 1: between six and eight times the typical daily move price
Speaker 1: for election day. And that's again subject to the assumption
Speaker 1: that it's all isolated to that particular day. No, that's
Speaker 1: not a great assumption because seven eight times a big number, right,
Speaker 1: So what we could equivalently say is, well, not only
Speaker 1: is election date pricing a lot of all risk, but
Speaker 1: that risk is spread out and and persists past the
Speaker 1: election itself. That's mathematically consistent. So we interpreted that not
Speaker 1: to say the markets expect basis point moving interest rates
Speaker 1: or movement equities on election day, but that the market
Speaker 1: was assuming a persistent, elevated volatility environment for for weeks
Speaker 1: and potentially a month thereafter. And so that just increased
Speaker 1: the value of of those options. Now now that since
Speaker 1: come down um and and now that's more like three
Speaker 1: or four times. Go back over the past twenty five years,
Speaker 1: and why not further It's because we really didn't have
Speaker 1: much of an options market earlier than years ago, so
Speaker 1: you really have a small sample there. But and many
Speaker 1: of those elections weren't that close, especially in the late nineties.
Speaker 1: But if we go back over that period, we can say, look,
Speaker 1: typical event risk premium is two to three times the
Speaker 1: background level of volatility, and we were looking at seven
Speaker 1: to eight and that's coming down to three or four,
Speaker 1: but it's still multiples of what you would expect for
Speaker 1: for a typical election cycle. That was great recent episodes.
Speaker 1: I feel like I've been asking really remedial questions about volatility.
Speaker 1: With our recent guest, I asked why vix curves, futures
Speaker 1: curve slope upward. I'm not going to ask about that now.
Speaker 1: I'm going to ask it even stupider question though, when
Speaker 1: I pull up a quote of the VIX and I
Speaker 1: see right now it's for people who are curious about that.
Speaker 1: What is that number actually represent? So that's a break
Speaker 1: even annualized move. So if you were to buy a
Speaker 1: one year option at it, imply volatility of the equity
Speaker 1: index would have to move for you to make zero
Speaker 1: dollars relative to the insurance costs up front. So um,
Speaker 1: you can change the time frame of that. Obviously, if
Speaker 1: if you're paying for a three month option, you'd have
Speaker 1: to have less of a break even because there's less
Speaker 1: time value. The idea of an option is you have, uh,
Speaker 1: the intrinsic value of the thing, which is how much
Speaker 1: the strike price differs from the current price of the assets.
Speaker 1: So if I bought a call on the smp AT's trading,
Speaker 1: that means I can buy something worth It's worth hundred dollars, right,
Speaker 1: And and then the question is if that option expires
Speaker 1: in six months to a year, If I have protection
Speaker 1: for a year, that's worth more than protection for six months, um,
Speaker 1: and so uh, that price is just going to be
Speaker 1: higher because of that time value. But what we want
Speaker 1: to do is put everything on equal footing apples to apples,
Speaker 1: and so we say, what is the break even change
Speaker 1: over the expiry of this option? UM to UH to
Speaker 1: get sort of a break a neutral PANO, a neutral payoff.
Speaker 1: That's where I'm agnostic to eying the option or not.
Speaker 1: That was great. I think Joe just managed to sneak
Speaker 1: in the actual question why is the VIX curve upward sloping?
Speaker 1: And you very nicely answered, so thank you for that.
Speaker 1: I have a slightly different question, which is you talked
Speaker 1: about how you've been studying the risk premium across a
Speaker 1: bunch of different asset classes, and I think you found
Speaker 1: that it looked like it was bigger or higher in
Speaker 1: the interest rates market than, for instance, in the stock market.
Speaker 1: Why do you think that's happening. Why do you think
Speaker 1: some asset classes or some markets appear to be pricing
Speaker 1: in higher levels of expected volatility? So I think some
Speaker 1: of it makes a lot of intuitive sense. So if
Speaker 1: we if we think about dollar c N Y so
Speaker 1: Chinese currency exchange rates, UM, I think it's fair to
Speaker 1: say that a Trump presidency versus Biden presidency could lead
Speaker 1: to very different outcomes there, and so the binary event
Speaker 1: has a lot of potential impact on on pricing, and
Speaker 1: so you'd expect the insurance value of options that protect
Speaker 1: you against changes in that price to be very high.
Speaker 1: So that's that's pretty intuitive. Interest rates is a little
Speaker 1: counterintuitive only in that if we think of interest rates
Speaker 1: is driven by two things. One is monetary policy and
Speaker 1: the other is fiscal policy. So on the monetary policy side,
Speaker 1: how does the FED move short term rates to react
Speaker 1: to economic conditions? What is their framework, who's in charge? Um?
Speaker 1: That that is important for even long term interest rates,
Speaker 1: because if I'm thinking about buying a tenure bond, I
Speaker 1: could equivalently just roll three months bonds for ten years.
Speaker 1: So there's an expectations element of this that tenure yield
Speaker 1: should be roughly equal to the average expected short term
Speaker 1: yield over the next ten years. And so whoever's in
Speaker 1: charge of the Fed and how they make decisions affects
Speaker 1: what that short term rate is going to do. Um.
Speaker 1: The second is on the fiscal side, which is that
Speaker 1: tenure bond is not a rolling basket of three months bonds,
Speaker 1: it's a tenure instrument. Um. The government needs to borrow
Speaker 1: money across a range of tenors, sometimes for longer, sometimes
Speaker 1: for shorter, and so the amount they need to borrow
Speaker 1: um and the way that they choose to do so,
Speaker 1: like the different maturities they focus on. All that tells
Speaker 1: you what the level of longer term interest rates is
Speaker 1: going to do relative to this expectation. So this is
Speaker 1: often called term premium, right It's it's the premium that
Speaker 1: is assigned as something that locks you into position for
Speaker 1: ten years, and or the format in which that risk comes,
Speaker 1: which is a treasury bond versus U corporate bond versus
Speaker 1: versus a rolling basket of short term securities, etcetera. So
Speaker 1: you know that fiscal outlook is not that different. Frankly,
Speaker 1: across the two candidates. The Committee for Responsible Budget has
Speaker 1: estimates for both campaign platforms, and I think over the
Speaker 1: next ten years or so, they have the stock of
Speaker 1: US debt for Biden at of GDP and for Trump
Speaker 1: at a hundred twenty five percent of GDP. So it's
Speaker 1: a very similar fiscal outlook, and that's consistent. The format
Speaker 1: of that dead increase maybe is different. In one case
Speaker 1: it's more spending and in the other it's more tax related.
Speaker 1: But tax expenditures and fiscal expenditures are fundamentally deficit spending anyways,
Speaker 1: and so like, the outlook isn't that different. And then
Speaker 1: if we turn to the monetary policy side, I think
Speaker 1: it's fair to say both candidates preferred devish outlook. And
Speaker 1: Pal's term doesn't expire until so there's some time there
Speaker 1: and and so, and the Fed just completed a review
Speaker 1: of how they make decisions, and they're unlikely to do
Speaker 1: a wholesale review over the short term, and so like,
Speaker 1: the outlook isn't that different for interest rates across the
Speaker 1: two candidates. I think the reason why you you get
Speaker 1: that knee jerk pricing of event risk is, on the
Speaker 1: one hand, everyone remembers and there was quite a bit
Speaker 1: of volatility in interest rates, and by many measures, the
Speaker 1: reaction of the interst rate market was much more chaotic
Speaker 1: to the unexpected outcome than equities and effects and other
Speaker 1: asset classes. So a lot of that post election volatility
Speaker 1: in six was really concentrated in interest rates. And there's
Speaker 1: a lot of muscle memory there. Yeah, I remember that.
Speaker 1: So like stocks, you know, mostly went up pret Trump,
Speaker 1: they they then continued going up during most of Trump's
Speaker 1: tenure obviously, but that the tenure moved, the long end
Speaker 1: of the curve really moved violently after probably people thought
Speaker 1: maybe Trump would deliver some sort of growth jold due
Speaker 1: to taxes or spending that would cause rate hike. I
Speaker 1: remember that day November eight, November nine, pretty some of
Speaker 1: the biggest moves ever. And so you think that sort
Speaker 1: of that the memory of that sort of loom looms
Speaker 1: large in terms of a potential rights turning point. Yeah.
Speaker 1: And I think one of the funny experiences from that time.
Speaker 1: We have a model that we trained. It's a machine
Speaker 1: learning model that looks at different market signals um and
Speaker 1: and tries to come up with a view on treasuries
Speaker 1: for a week. So it'says longer short, should I do
Speaker 1: ten percent? Size size, and it uses it in put.
Speaker 1: I think we have things going into it, but it's
Speaker 1: all stuff you can see. It's economic data, it's pricing,
Speaker 1: it's the yield curve, it's ball levels, it's the depth
Speaker 1: of the market and different different permutations of that. UM.
Speaker 1: It doesn't know there's an election. And what I thought
Speaker 1: was really interesting about that model is that it quote
Speaker 1: unquote got the election right, meaning it was max short
Speaker 1: the rates market in the lead up to the to
Speaker 1: the election date itself. And so if it doesn't know
Speaker 1: there's an election, it doesn't know what the polls are.
Speaker 1: It's not like it knew something that Nate Silver didn't.
Speaker 1: It was basically just saying, like, I look at this
Speaker 1: set of market data and economic data, and I'm of
Speaker 1: the opinion, as this agnostic model, that this is a
Speaker 1: rising rate environment. Economic growth is accelerating, inflation is firming,
Speaker 1: and rates should be higher, um. And the question is
Speaker 1: then why were they so low? And and I think
Speaker 1: you can argue, I mean, it's not a particularly robust argument,
Speaker 1: but you could say, you know, the narrative that that
Speaker 1: makes that reasonable is to say the election risk, the
Speaker 1: binary election risk, independent of what its impact, the impact
Speaker 1: of either outcome would have been on markets, just the
Speaker 1: fact of this big event coming up kind of held
Speaker 1: the market back from pricing in what was otherwise like
Speaker 1: a very supportive background. Um. And then once the election passes,
Speaker 1: you just knee jerk price in six months of economic
Speaker 1: developments over a week or two. And so if that's
Speaker 1: the case this time around, even if this these these
Speaker 1: deficit outlooks which are very similar are the same. It
Speaker 1: could also be the case this time that the event
Speaker 1: and uncertainties around the event, and even if it seems
Speaker 1: reasonably high probability of one outcome or nother, everyone's saying,
Speaker 1: you know, you never know, and polls could be wrong
Speaker 1: and so forth, And we might talk about that in
Speaker 1: the episode. But just the fact of this one day
Speaker 1: that matters and we don't really know what's going to
Speaker 1: happen can hold things back, and then in the wake
Speaker 1: of it, potentially independent of what the outcome is, you
Speaker 1: could frontload all of this repricing very quickly, and that's
Speaker 1: a very volatile environment. Well, so why don't we talk
Speaker 1: about that point? Because we have the risk premium built
Speaker 1: into various markets. So you could argue that in many ways,
Speaker 1: investors are quite well positioned for something actually happening in November.
Speaker 1: But on the other hand, you could argue perhaps that
Speaker 1: the polls are wrong or there's still a chance of
Speaker 1: something completely unexpected happening, and in that sense they might
Speaker 1: even be considered complacent despite that higher risk premium. So
Speaker 1: what's your take on that particular argument. So I think
Speaker 1: there's not a ton of trading that generates these price
Speaker 1: changes so the stock of equity positions, the stock of
Speaker 1: rates positions is just enormous, and a lot of those
Speaker 1: just have to get rebalanced when there's a big change.
Speaker 1: So you can get these self reinforcing spirals simply because
Speaker 1: like changes in the environment, especially among You know, we
Speaker 1: often think intuitively in terms of retail investing, but you
Speaker 1: know that the most important transfers of risks happen with
Speaker 1: with institutions. They're just much larger and they have very
Speaker 1: different incentives. Um. So, if we think about an insurance company,
Speaker 1: for example, if rates are rising, they need to shed
Speaker 1: duration mechanically because of the way that there risks are evolving.
Speaker 1: So they're they're pro cyclical with the market, and they're
Speaker 1: just very large in size billions and billions and billions
Speaker 1: of dollars, and so they're less well hedged just because
Speaker 1: their their risks are much more complex. On the equity side,
Speaker 1: you know a lot of exotic type products can generate
Speaker 1: convexity as well, and those, you know, you can hedge that.
Speaker 1: But um, the the risks embedded in exotic instruments like
Speaker 1: these structured notes and other things are very on linear.
Speaker 1: In many cases, there are sort of binary type risk
Speaker 1: and other things that it's just hard to keep up frankly. UM.
Speaker 1: And and so even if you have decent hedged positions
Speaker 1: out there, I think, on the one hand, and this
Speaker 1: is more a rates thing than an equace thing, like
Speaker 1: if the if the environment is changing, that's going to
Speaker 1: blow through all of these all of these protections, because
Speaker 1: it's impossible if you're fully hedged. You know, I'm making
Speaker 1: any money, right, so, so nobody's nobody's ever fully hedged.
Speaker 1: Otherwise it's you might as well just not be participating
Speaker 1: in investing. UM. And so those residuals can get very big,
Speaker 1: very quickly. UM. So I guess a a sort of
Speaker 1: long winded way to answer the question, which is, I
Speaker 1: think it's sort of functionally impossible for everyone to be
Speaker 1: fully hedged. And the event of a sufficiently large macro shock,
Speaker 1: which again doesn't need to be anything other than the
Speaker 1: passing of the event itself, which in some sense was
Speaker 1: the case in two thousand sixteen, UM, you can you
Speaker 1: can generate these these spirals that that keep out the
Speaker 1: volatility ellow at it for a long time. UM. It's
Speaker 1: important to say that the presidential elections are rarely that thing,
Speaker 1: so is very much the aberration. Usually you get a
Speaker 1: couple of decently large moves in the lead up and
Speaker 1: in the aftermath of the election invent itself. But even
Speaker 1: in two thousand, when we didn't really know the result
Speaker 1: for weeks, like, the volatility was modestly elevated, but it
Speaker 1: really wasn't anything like um. And and then if you
Speaker 1: go back further, you know, in the eighties these weren't
Speaker 1: really particularly contested elections. Even in the nineties they were
Speaker 1: pretty solid results. And so now we're talking about the seventies,
Speaker 1: and I think at that point the utility of the
Speaker 1: of the analogy kind of drops off quite a bit.
Speaker 1: Talk to us about the hedging needs of investors this year,
Speaker 1: because it's been a really weird year. So, I mean,
Speaker 1: you have some investors that are probably sitting on fantastic
Speaker 1: gains that they never would have expected to reap in
Speaker 1: a year in which we have this terrible economic outcome
Speaker 1: and a pandemic and so forth. So that's maybe some
Speaker 1: inclination to lock in those gains. We've also had a
Speaker 1: lot of people missed this ralliant somewhere or another, either
Speaker 1: because they were just under invested throughout the whole thing,
Speaker 1: or maybe just overly hedge throughout this whole time due
Speaker 1: to fears about some second wave or second shoe about
Speaker 1: to drop. So as people look towards the election, how
Speaker 1: has what we've seen building up to it influenced their
Speaker 1: desire to hedge against various outcomes? So so I think
Speaker 1: there's two really interesting behavioral elements to this. The first is,
Speaker 1: if you're sitting on very significant gains, you're very highly
Speaker 1: incentivized not to lose them. And so if if we're
Speaker 1: trying to figure out why options markets would price so
Speaker 1: much excess risk around this particular election, On the one hand,
Speaker 1: there's just the memory of sixteen, but that's not a
Speaker 1: great argument because that's just saying, you know, I don't
Speaker 1: want this to happen to me again. But more importantly,
Speaker 1: I think we were talking about time value before, Like
Speaker 1: the election is not that far and so the actual
Speaker 1: dollars you need to put up to protect yourself is
Speaker 1: not that many, and so the price of that option
Speaker 1: can adjust significantly on relatively small dollar amounts of trading.
Speaker 1: And so if you're sitting on significant gains, is a
Speaker 1: hedge funder or individual or asset manager anybody. UM, you
Speaker 1: know that insurance cost is relatively low, so you might
Speaker 1: as well buy it. Um And and the the converse
Speaker 1: is true too, which is why would you sell insurance
Speaker 1: premium and a relatively small dollar amount when you look
Speaker 1: insane if you get it wrong, It's just like, that's
Speaker 1: a really bad look for selling those options. Like, no
Speaker 1: one wants to have that conversation with their boss. Why
Speaker 1: did you sell a bunch of puts on the SMP
Speaker 1: like two weeks before the election? Right? So, so like
Speaker 1: if you have a bias towards buyers of this risk,
Speaker 1: they don't have to put up a ton of concrete
Speaker 1: dollars to get it. And the incentives are very highly
Speaker 1: skewed in favor of protecting gains. You're going to get
Speaker 1: very expensive options optically, And this is where that adjustment.
Speaker 1: If I if the price of a one year option
Speaker 1: was at levels that the price of a two week
Speaker 1: option we're trading, it would be a very expensive instrument.
Speaker 1: But realistically we're talking about a handful of days of
Speaker 1: protection and that just comes relatively inexpensive. So who's actually
Speaker 1: selling volatility protection this year? Then? I mean, again, it's
Speaker 1: been such an unusual year, and we've seen a lot
Speaker 1: of options trades exploding in popularity. Who are the big
Speaker 1: sellers of protection at the moment, So in many cases
Speaker 1: it's the dealers themselves that meaning they don't have the
Speaker 1: other side in full UM. There is a uh there.
Speaker 1: There is a pretty large and relatively persistent systematic program
Speaker 1: UM that's employed by a range of investors. But basically
Speaker 1: the ideas options tend to trade relatively rich, and so
Speaker 1: it's a good sort of risk addusted return to keep
Speaker 1: selling them. So harvesting that risk premium over time will
Speaker 1: be a good returns relative to the volatility of that
Speaker 1: position volatiling in this case, meaning the returns on that
Speaker 1: strategy of selling options UM that that's been around for
Speaker 1: years and years and years, twenty years really UM as
Speaker 1: of the two thousand five vintage like that was really
Speaker 1: a consequence of at least in rates markets. Fannie and
Speaker 1: Freddie were very large UM. They had a trillion dollars
Speaker 1: in their retain portfolio of mortgage backed securities, and their
Speaker 1: business model was to buy mortgage backed securities, hedge the
Speaker 1: duration risk with swaps, and then buy back options that
Speaker 1: replicate the borrowers option to prepay their their mortgage, and
Speaker 1: they were harvesting that what people call it the mortgageness
Speaker 1: of the mortgage. But you can build a model that
Speaker 1: tries to use all the rate risk you can to
Speaker 1: replicate the risks embedded in a mortgage instrument, and whatever
Speaker 1: is left over is the thing that Fannie and Freddie
Speaker 1: wanted to earn. And they had forty turns of leverage,
Speaker 1: so point two times forty is is a pretty good
Speaker 1: return basically UM. And so what that meant was they're
Speaker 1: willing to buy options at a relatively expensive level, and
Speaker 1: whoever was selling them those options was participating to some
Speaker 1: extent in this in this transformation and earning a bit
Speaker 1: of a transaction cost associated with the Fattie and Freddie
Speaker 1: and Fanny programs. UM. That hasn't been the case in
Speaker 1: a very long time. And yet it's been relatively profitable
Speaker 1: to just keep selling options every day, agnostic to the environment.
Speaker 1: So there have been these programs that have built up
Speaker 1: to continue this trade even even as Fanny and Freddie
Speaker 1: have shrunk, and they've been really active at least since
Speaker 1: and and grown and shrunk and over time. But with
Speaker 1: the with the very brief exception of March and April
Speaker 1: this year, they've been pretty persistent through periods of volatility,
Speaker 1: which which is supposed to be what what you do
Speaker 1: with a strategy like that. If you are in a
Speaker 1: systematic strategy, you're not supposed to sit there and say, well,
Speaker 1: it doesn't feel right today, so I'm not going to
Speaker 1: do it. Um. And so I mean I think in
Speaker 1: March it was pretty easy to say it doesn't feel
Speaker 1: right today. Um, when you have the largest discrepancies in
Speaker 1: pricing and and moves in in basically history. Um. And
Speaker 1: we talked about that back in in April. So maybe
Speaker 1: that's the exception, But um, you're really supposed to keep
Speaker 1: doing this through periods of stress, in fact, periods of
Speaker 1: stress or when you earn most of your money and
Speaker 1: a strategy like that, And and it's likely that that
Speaker 1: kind of activity has persisted today. Why hasn't the premium
Speaker 1: on options selling going away? I mean, if it's been
Speaker 1: profitable for twenty years, if people seem to systematically overpay
Speaker 1: for protection, um, why isn't that, just like everything else,
Speaker 1: like sort of crowded out to the point where there's
Speaker 1: no money left in it? Yeah? There should be alpha
Speaker 1: decay and stuff like that. Uh, it's it's one of
Speaker 1: those sort of great mysteries of of interest rate derivative markets,
Speaker 1: and and some people have attributed this to central bank
Speaker 1: activity and and just repression of volatility across various asset classes,
Speaker 1: especially interest rates. So basically the idea of being you're
Speaker 1: you're not fighting the FED by selling options, you're going
Speaker 1: with the FED in doing so, and the presumption that
Speaker 1: the FED will backstop any significant period of volatility with
Speaker 1: purchases that dampen it UM. I don't particularly like that
Speaker 1: narrative just because it's a very strong assumption and and
Speaker 1: we don't really there's there's no reason to believe that
Speaker 1: specifically what's going on day to day. Um. There's also
Speaker 1: this presumption that options are pricing in much higher risk
Speaker 1: of jumps. So when we think of an options price,
Speaker 1: the models that people typically build, they assume some probability
Speaker 1: that will be these discontinuous jumps in the middle of
Speaker 1: the day. And in October was a great example of that,
Speaker 1: where tenure yields drop thirty basis points in ten minutes
Speaker 1: and then came right back. And when you start pricing
Speaker 1: in significant jump risk into the options, it bleeds through
Speaker 1: to other sort of prices associate with them, so it
Speaker 1: makes all options richer. If you think there's some chance
Speaker 1: of these kinds of jumps, and what would generate a
Speaker 1: jump an election result to generate a jump that happened
Speaker 1: in twenty sixteen, You can have the Brexit outcome. Things
Speaker 1: like that. You could have developments overnight with the Chinese
Speaker 1: Yuandi val and implications for for rates, markets and e
Speaker 1: M and so forth. So maybe that could generate some riches.
Speaker 1: But that's me kind of grasping at straws. Frankly, I
Speaker 1: think the usual response has been, UM, I don't know,
Speaker 1: but it keeps working, so which is not a great
Speaker 1: reason to keep doing it, but it is a reason
Speaker 1: to keep doing it. And it's been ten years now,
Speaker 1: ten fifteen years, and it's been a persistent, uh, you know,
Speaker 1: reasonably good return type strategy. UM. I wanted to go
Speaker 1: back to some of your older research, so I gosh,
Speaker 1: I guess it was just last year, but it feels
Speaker 1: like ages ago. You created this thing called the vole
Speaker 1: Fefe index, which basically had well you can explain it,
Speaker 1: but attempted to quantify the impact of Donald Trump's tweeting
Speaker 1: on the rates market, and I know you've been revisiting
Speaker 1: that index every once in a while over the past
Speaker 1: year or so. I guess my question is how much
Speaker 1: does the rates volatility regime change if Biden wins the
Speaker 1: election and you get a more traditional president, let's say,
Speaker 1: one who's less active on social media, for instance, Does
Speaker 1: it change everything for the rates market? Do you have
Speaker 1: to discontinue vol fefe? Probably? So this was work that
Speaker 1: a colleague of my men your sale did and he
Speaker 1: came up with the name. So I shouldn't take credit
Speaker 1: for that, um. And basically the idea was it it
Speaker 1: had felt this way to a lot of people, that
Speaker 1: that the president's tweets had an impact on markets um
Speaker 1: and when they veered towards certain topics, they had a
Speaker 1: greater impact on markets. And at the time, this was
Speaker 1: the height of the trade war. And so you get
Speaker 1: these pronouncements over Twitter about the progress of talks, and
Speaker 1: I remember people being very focused on parsoning words and
Speaker 1: the likelihood of a deal or not a deal, or
Speaker 1: with tears go up or not go up? And and
Speaker 1: and basically the idea was, let's try to put some
Speaker 1: numbers around this. And the reason why this works in
Speaker 1: the first places is for two elements, the first being political,
Speaker 1: which is um, policy announcements were made over Twitter, which
Speaker 1: is a sharp departure. Obama had a Twitter account, but
Speaker 1: that he didn't announce new policies on it, at least
Speaker 1: not initially UM. And just the policy process changed in
Speaker 1: Twitter was a good or was it was a preferred
Speaker 1: vehicle for making those announcements, and so we had to
Speaker 1: pay attention whether we like it or not. UM. The
Speaker 1: second is it's more of a technical thing, but it
Speaker 1: turns out this is a very good problem for machine learning.
Speaker 1: And the reason and natural language processing because UM, the
Speaker 1: data we use it was something like tweets, which is
Speaker 1: not a massive database, but the language that Trump uses
Speaker 1: is pretty consistent and frankly doesn't use that many words. UM.
Speaker 1: And so if you want to try to to digitize
Speaker 1: that in a way that can be analyzed systematically, it's
Speaker 1: it's a good it's a good toy problem for it.
Speaker 1: It's something you can do in a desktop. It doesn't
Speaker 1: take up cherabytes of memory. This is not like trying
Speaker 1: to teach a car how to self drive. This is
Speaker 1: like a very concrete question does this tweet effect market? Um?
Speaker 1: So we can we can very clearly like tag tweets
Speaker 1: as having moved or not moved markets. We don't have
Speaker 1: to try to figure out what sentiment is. Usually when
Speaker 1: we talk about natural language processing, we're talking about like
Speaker 1: good or bad? Is this a good or bad statement? Here,
Speaker 1: we're just saying to the market move. So it's it's
Speaker 1: easy to build a database of of tweets and impact. UM.
Speaker 1: We don't have to think about that much variation in
Speaker 1: the language because it's uses pretty clear, it's pretty consistent,
Speaker 1: not a ton of words, So it's it's something you
Speaker 1: can analyze pretty straightforwardly. UM. And our goal was to
Speaker 1: come up with it. It was more of a detection
Speaker 1: than a forecast. I've I've often got the question like
Speaker 1: does this tweet move markets? Or is this tweet more
Speaker 1: or less likely to move markets than that tweet? And
Speaker 1: I think that the idea here was less to get
Speaker 1: a forecast for any particular Twitter announcement, but to say,
Speaker 1: what's the background of noise that this creates, Like to
Speaker 1: what extent does the accumulated uncertainty with the potential for
Speaker 1: tweeting in general? And especially on particular topics. Does that
Speaker 1: lead to elevated levels of implied volatility? Meaning protection from
Speaker 1: options is more expensive because any minute now the president
Speaker 1: can tweet about the Chinese Strait negotiations. UM. And so
Speaker 1: what we do is we we took this database of tweets.
Speaker 1: We we tried to identify the words that occurred more
Speaker 1: frequently in those that moved the market, and then we
Speaker 1: built a random forest model that tried to account not
Speaker 1: only for the relatives sort of value of each of
Speaker 1: those words and categories in generating market moves, but also
Speaker 1: the interactions between them. So if us if good appeared
Speaker 1: with the word China that had a different meaning than
Speaker 1: have good appeared with with a different word, and and
Speaker 1: so the classic NLP problem. And so what we found
Speaker 1: was when we generated this index UM, it had statistical
Speaker 1: significance in modeling volatility. And what does that mean. It
Speaker 1: means that if we're trying to explain the drivers of
Speaker 1: interest rate volatility and we incorporate this index into that
Speaker 1: statistical explanation, it plays a significant role. So UM, to
Speaker 1: directly answer your question, if if we move to a
Speaker 1: Biden presidency, I don't know what his twitter Twitter habits
Speaker 1: will be I think it's fair to say they would
Speaker 1: be different. Maybe I should hedge a little bit and
Speaker 1: say we'd have to wait and see and try to
Speaker 1: generate enough data to build a model. But looking at
Speaker 1: the at Joe Biden twitter feed versus the at real
Speaker 1: Donald Trump twitter feed, I think it's fair to say
Speaker 1: his look sort of more vetted and um less and
Speaker 1: much more consistent with policies that have been previously announced.
Speaker 1: And so the utility of watching Joe Biden's Twitter feed
Speaker 1: is probably less in a Biden presidency than than that
Speaker 1: of watching Trump's and the Trump presidency. So, um, I
Speaker 1: don't know if we have to mothball it, because who
Speaker 1: knows whether that framework is useful in the future, But
Speaker 1: at a minimum, it means the background level of uncertainty
Speaker 1: comes down because you're transitioning back to a policy process
Speaker 1: where new ideas are vetted internally leaked out in some
Speaker 1: fashion through appearances in the Sunday shows or through articles newspapers,
Speaker 1: and you're going back to a more traditional policy policy
Speaker 1: generating process that doesn't really rely on on Twitter as much. Um. God,
Speaker 1: I can't even I can't even imagine what that world
Speaker 1: could be like, it's it's almost it's so it's like
Speaker 1: I have this vague memory that things used to operate
Speaker 1: like that. I want to ask you another question about
Speaker 1: the current regime, and again not necessarily relating to the
Speaker 1: imminent election, but one of the things on the right
Speaker 1: side that's really sort of characterized the past several months
Speaker 1: is essentially just the fact that the FED has indicated
Speaker 1: very strongly that the bar to a future the first
Speaker 1: rate hike or a future rate hike is probably higher
Speaker 1: than it's ever been before, very ambitious goals with hitting
Speaker 1: its inflation target, full employment, far more forward guidance than
Speaker 1: we ever got during past recoveries or anything like that.
Speaker 1: And as such, even through this huge stock market rally
Speaker 1: from the end of March through now, basically we've seen
Speaker 1: almost no upward move in rates, and I'm curious, like,
Speaker 1: how does that change the game from your perspective, from
Speaker 1: a rates volutility of perspective, The fact that we have
Speaker 1: so much aggressive forward guided so little uh so, a
Speaker 1: little left to the imagination in terms of what the
Speaker 1: FED is going to do. Yeah, so forward guidance is
Speaker 1: very effective at suppressing volatility, when when we didn't experiment recently,
Speaker 1: we took options that the experty was a year out,
Speaker 1: two years out, three years out, five years out, ten
Speaker 1: years out, and we said, you know, these options are
Speaker 1: related to one of two things really, one the pricing
Speaker 1: of them, one the policy outlook. So that's forward guidance. UM.
Speaker 1: I think we used something like the months until the
Speaker 1: next hype per hYP per the FEDS pronouncements, and we
Speaker 1: have a series for that. We can go back and
Speaker 1: just say when did they think they were gonna hike?
Speaker 1: And they had forward guidance into any ten and they
Speaker 1: had forward guns in twenty twelve and they forward guidance again. Um.
Speaker 1: The second part was proxying these exotics flows that that
Speaker 1: tend to drive very long dated options volatility. So this
Speaker 1: comes back to the Taiwanese life insurance companies, which will
Speaker 1: probably talk about at some point we could get bred
Speaker 1: on here, bred sets around here, and we can never
Speaker 1: get enough exactly. Uh. And so what we found was
Speaker 1: if you go out two or three years in expiry,
Speaker 1: so if you want protection for the next two or
Speaker 1: three years, the price of that protection is mostly related
Speaker 1: to this forward guide. So as the forward guidance becomes stronger,
Speaker 1: and further out, the price of that protection comes down
Speaker 1: because in a sense, the FIT is subsidizing it with
Speaker 1: their with their policy. Then when you get further out
Speaker 1: than that, you start getting into this world of well,
Speaker 1: who really trades tenure options on thirty year rates, and
Speaker 1: then you're really talking about much different not really informed
Speaker 1: by likely that as informed by likely realized volatility over
Speaker 1: the next ten years. You're informed by the vale. You
Speaker 1: have that ten years of protection to a very specific
Speaker 1: and relatively idiosyncratic subset. So so you know, to answer
Speaker 1: your question like, it suppresses volatility at least out um
Speaker 1: ways in the term structure UM. And yeah, I think
Speaker 1: the FED has made it very clear that that that's
Speaker 1: their plans, So you know they're they're not purchasing assets
Speaker 1: to tap yields out of three to five years. That
Speaker 1: was something that was sort of floated or speculated at
Speaker 1: times a firm of a form of yield curve control,
Speaker 1: but one that's very tied to reinforcing forward guidance as
Speaker 1: opposed to say, what the Bank of Japan is doing. UM.
Speaker 1: But I think the FED has plenty of credibility in
Speaker 1: this department UM. Their their communications are very clear um,
Speaker 1: and you know it just has not generally paid to
Speaker 1: fight these things. So the feed is suppressing volatility. Plus
Speaker 1: for various reasons that you've already described, you have the
Speaker 1: price of near term volatility protection that's quite reasonable and
Speaker 1: cheap at the moment. So I guess my question is,
Speaker 1: if come November we have the election and everything goes
Speaker 1: sort of not according to plan, but everything goes as
Speaker 1: expected and indicated by the polls currently, how quickly does
Speaker 1: the risk premium that's currently built into markets go away
Speaker 1: if at all? Well, it depends a bit on the Senate.
Speaker 1: So we talked about the presidency a lot, but that
Speaker 1: GDP target for for the stock of government debt that
Speaker 1: depends on Biden actually being able to do things, and
Speaker 1: in order to do things, he needs to have the
Speaker 1: support of both houses of Congress because the vast majority
Speaker 1: of fiscal policy is going to be an Act of Congress. So, um,
Speaker 1: the the outcome of the Senate is key there. If
Speaker 1: you have a democratic leap, which I think betting markets
Speaker 1: haven't what or fifty and these quantitative election models like
Speaker 1: have that closer to seventy. So if you have that
Speaker 1: kind of outcome, then there's potential for a real shift
Speaker 1: in policy um, and that could generate volatility just simply
Speaker 1: for the reasons we were talking about earlier. Now now
Speaker 1: there's now we know what's going to happen, and so
Speaker 1: we're going to reprice the market significantly, and I don't
Speaker 1: think options markets anticipate necessarily that, meaning an extended period
Speaker 1: of repricing of interest rates UM. A lot of that
Speaker 1: election risk has really become more concentrated around the event itself.
Speaker 1: If you have a split power situation, you know, I
Speaker 1: think it's it's quite suppressive of volatility only because it's
Speaker 1: unclear what beyond the current status quo could possibly happen.
Speaker 1: You know that the one caveat to that being if
Speaker 1: literally nothing can get done, then there's a macroeconomic consequence
Speaker 1: to that, which is in the absence of support from
Speaker 1: the federal government, like what is what what is you
Speaker 1: canotic growth? What is GDP do? What is what does
Speaker 1: employment do? Etcetera, And so like that's kind of the
Speaker 1: caveat there um. But you know, I think what's what's
Speaker 1: really interesting about this is it comes back to the
Speaker 1: FED again, which is, let's say there's a democratic suite
Speaker 1: and the Biden campaign platform is is implemented. So this
Speaker 1: brings up broader questions of fiscal dominance, meaning what is
Speaker 1: the role of the FED in an environment where the
Speaker 1: dead is expanding that quickly? And the question that I
Speaker 1: think the market is grappling with is on the one hand,
Speaker 1: the FED is clearly not tied their purchase program to
Speaker 1: fiscal policy, and nor should they, right, independence is important,
Speaker 1: But they've also be very firmly committed themselves to to
Speaker 1: market functioning. And this is a lot what was going
Speaker 1: on in March that the centrality of the treasury market
Speaker 1: not just as an investment, but in just the flow
Speaker 1: of money throughout the financial system and as a provider
Speaker 1: a provider of liquidity to the banking system, and like it,
Speaker 1: it serves a much more important purpose than simply a
Speaker 1: risk free investment. Um. So, if market functioning is part
Speaker 1: of the mandate and part of the reaction function for
Speaker 1: their purchase program, then at some point wider deficits could
Speaker 1: in principle generate market functioning issues, at which point the
Speaker 1: FED has to step in. So like in the absence
Speaker 1: of a change to the regulatory framework that generates these risks,
Speaker 1: are we setting ourselves up for fiscal dominance de facto
Speaker 1: just because of this relationship. And it's not on a
Speaker 1: short term basis, not on a day by day basis.
Speaker 1: But if we look out into the future and we
Speaker 1: say that the ability of dealers to intermediate the sale
Speaker 1: of treasuries and the purchase of treasuries is fixed in size,
Speaker 1: are relatively fixed, but the stock of treasuries is going
Speaker 1: up substantially, then at some point the FED has to
Speaker 1: provide an outlet for that, and so that that at
Speaker 1: some point will rise to some version of fiscal dominance,
Speaker 1: even though it's not UM, not explicitly that. And so
Speaker 1: if you're thinking about volatility like that, that is suppressive
Speaker 1: of volatility. Right If there's a if there's a backstop
Speaker 1: on what yields can do UM, and there's ultimately a
Speaker 1: FED backstop in the market, then the potential for large
Speaker 1: changes is very is very much mitigated. You know, I
Speaker 1: wanna go back to something you're saying about UM looking
Speaker 1: at the polls and the five thirty eight models in
Speaker 1: the betting markets, and of course I read a lot
Speaker 1: of cell side research where the strategists, uh, you know,
Speaker 1: talk about d C and those charts are often in there.
Speaker 1: How much is that really being inputed in real time
Speaker 1: into models these days from clients that you deal with,
Speaker 1: where they have sort of ongoing updates of these models
Speaker 1: that then automatic, you know, take in all this stuff,
Speaker 1: the betting markets and so forth, UH and then spit
Speaker 1: out some results in terms of how they want to
Speaker 1: trade that. I don't get the sense that it's directly
Speaker 1: incorporated that often. I would say the more likely candidate
Speaker 1: for that is UH is prediction markets. And maybe that's
Speaker 1: just our biases as an investment types, where we say, look,
Speaker 1: if there's money behind this, if there's a transactional, transactionally
Speaker 1: based measure, I have a preference for it. Typically those
Speaker 1: two things have gone together to some extent. I think
Speaker 1: there's less of that now. And predicted has what chance
Speaker 1: of Biden victory? And these quantitative election models, And I'm
Speaker 1: saying that because I want to include like the economists
Speaker 1: and Sam Waiting stuff and and five thirty eight stuff,
Speaker 1: all of them kind of converge around something. And that's frankly,
Speaker 1: because there's only so many ways to do this and
Speaker 1: we all have the same input data, Like that's a
Speaker 1: pretty significant discrepancy and I think that raises this this
Speaker 1: issue that was alluded to earlier of polling errors, because
Speaker 1: ultimately these models don't assume polls are right, but they
Speaker 1: assume that they are as wrong as they've been the
Speaker 1: past on average um. And so I think the memory
Speaker 1: of again is relatively fresh, and which is a little
Speaker 1: ironic in the sense that was basically a one sigma
Speaker 1: polling air. It really wasn't that outsize relative to history.
Speaker 1: And and if you get a one signa polling air
Speaker 1: and just the right way in the context of the
Speaker 1: electoral college, you can get a very unexpected outcome. But
Speaker 1: I don't think it's fair to say the polls were
Speaker 1: quote unquote wrong in so you know that that degree
Speaker 1: of wrongness is incorporated into number. What you're saying, you're
Speaker 1: you're seeing is markets in general, and that's reflected in
Speaker 1: option premiums, and then these prediction markets, which admittedly have
Speaker 1: very small transaction volumes, but they feel more financing um
Speaker 1: than that then say pure model estimate, so you know
Speaker 1: that those are more traded together. I think that over
Speaker 1: over time. But you know, I don't think these models
Speaker 1: are really incorporated rigorously into any investment process. I think
Speaker 1: they're there's just not enough time to do that. You
Speaker 1: don't have good data over long periods, and so what
Speaker 1: you end up doing is kind of handicapping, and you
Speaker 1: you make one of these grids, which is like the
Speaker 1: least quantitative exercise in the world, where you say, like
Speaker 1: house Senate control and the y axis and presidential control
Speaker 1: into the X axis, and like, what is it due
Speaker 1: to yields and what's the probability of each and and
Speaker 1: that's how I come up with some target. I mean,
Speaker 1: that's kind of all you can really do UM. And
Speaker 1: it's not like we can choose not to participate in
Speaker 1: the selection from a market's perspective. It's that you have
Speaker 1: to have a view because it matters UM. But it's
Speaker 1: it's hard to do it in a very rigorous way.
Speaker 1: You know. The one thing I like to highlight also
Speaker 1: and others have as well, is that this uncertainty cuts
Speaker 1: both ways. So I think intuitively, when we talk about
Speaker 1: pulling errors, we're talking about Trump winning even when his
Speaker 1: probabilities are relatively low. But you know, a lot of
Speaker 1: these models have a ten plus percent Biden's sweep and
Speaker 1: landslide as more likely than a Trump win. So like
Speaker 1: that generally speaking, error is symmetric, or at least reasonably symmetric.
Speaker 1: So I think there's less attention to the market on
Speaker 1: that potential outcome because a Biden victory by ten plus
Speaker 1: percentage points is a mandate that has implications for policy
Speaker 1: that a two percentage point win would not. So it's
Speaker 1: definitely worth thinking about those scenarios. Do you think that
Speaker 1: the fact that, um, you know, when you talk to clients,
Speaker 1: do you feel like that is um underappreciated, that fact
Speaker 1: that everyone has sort of the twenty six mental model
Speaker 1: in their head where you take Biden's lead and then
Speaker 1: you chop a few points off of it because reasons,
Speaker 1: and then you get maybe this close raise, and by
Speaker 1: and large people aren't thinking about that alternative form of error. Yeah,
Speaker 1: never comes up as an example. So like the I
Speaker 1: think the potential were a very significant Biden win, that
Speaker 1: that brings with it this kind of mandate, and that
Speaker 1: brings into the fold all kinds of other policies that
Speaker 1: were really not in the base case of of the campaign. Um,
Speaker 1: you know, that starts to look a little different, And
Speaker 1: I just don't hear about that very often. That's interesting, Yeah,
Speaker 1: And it's more it's much more along the lines of
Speaker 1: what you're describing, which is like Biden is probably gonna
Speaker 1: win per the polls, But what if we're wrong for
Speaker 1: reasons I'm specified, and so I need to handicap this.
Speaker 1: So mycent turns into from a betting markets perspective and
Speaker 1: and maybe from an investment side perspective, I buy some
Speaker 1: protection on top of that because maybe I'm wrong twice
Speaker 1: and so and so that's why the options get rich, right,
Speaker 1: and that that's why they increasing costs, because that insurance
Speaker 1: is protection against being wrong in that one. Okay, so
Speaker 1: we're going to leave it there for now, but Josh
Speaker 1: will have to have you on after the elections for
Speaker 1: your fourth All Thoughts appearance will make that happen to
Speaker 1: discuss what what may or may not have actually changed
Speaker 1: in the volatility regime. Yah, sounds good to me, looking
Speaker 1: forward to it. Thanks John, Thanks very much, so, Joe.
Speaker 1: It's it's always great to talk to Josh. I think
Speaker 1: he's really good at elaborating on these quite complex topics
Speaker 1: and really you can kind of throw anything at him.
Speaker 1: But the points he was saying about who's actually selling
Speaker 1: volatility at the moment and how it's being priced, and
Speaker 1: why you might want to buy volatility protection just two
Speaker 1: weeks out ahead of the election, or why you might
Speaker 1: avoid selling it. I thought that was really interesting. We're
Speaker 1: very lucky. I feel we've had a richest, a richness
Speaker 1: of people who are just extremely clear about explaining this stuff.
Speaker 1: Recently talked to Chris ben Ifford, who we've had on
Speaker 1: a few times, and Josh just like so clear the
Speaker 1: way he sort of describes the contours of this market.
Speaker 1: I really appreciate that conversation. Yeah. Absolutely, And also the
Speaker 1: point about how no matter what happens in the elections
Speaker 1: and sort of no matter whether it's a huge surprise
Speaker 1: relative to the current polls or not, there is going
Speaker 1: to be a need for investors to reposition. I thought
Speaker 1: that was a really important thing to mention as well. Yeah,
Speaker 1: like the idea that an event doesn't even have to
Speaker 1: be a big event, It just has to pass and
Speaker 1: then suddenly a new regime, a new a new regime
Speaker 1: can emerge, even if nothing really happened that fundamentally changed
Speaker 1: the outlook, just because in the lead up to that
Speaker 1: expected event there was so much sort of um, I
Speaker 1: don't know the word I'm looking for, but hesitant, hesitancy
Speaker 1: perhaps to make any big moves. Yeah, you know, so
Speaker 1: if that was interesting, is like there's like this sort
Speaker 1: of like highly quantitative flair or sort of characterization of
Speaker 1: all of this stuff. But when you talk to Josh,
Speaker 1: like so much of what he describes, uh, in the
Speaker 1: market is sort of just like heuristics and sort of
Speaker 1: people making normal judgments that don't seem that Matthew, it's like,
Speaker 1: do you really want to be the one that's sold
Speaker 1: puts two weeks before the election when everyone remember, do
Speaker 1: you really Okay, you take this expectation, But was like this,
Speaker 1: so maybe it'll be a little closer, Like a lot
Speaker 1: of things that don't seem like that quantitative or rigorous
Speaker 1: at all, and more just like gut feels about how
Speaker 1: you're supposed to play this. So it's interesting that you know,
Speaker 1: here it's like options, derivatives, hedging, volatility curves. You think
Speaker 1: it was this very sort of like mathematical approach, but
Speaker 1: a lot of it is sort of just you know,
Speaker 1: just people going out their gut. Yeah. Well, also the
Speaker 1: example of the systematic vole sellers who are supposed to
Speaker 1: be doing that on a quantitative basis that doesn't change.
Speaker 1: But then back in March, they sort of collectively thought, well,
Speaker 1: wait a second, it's crazy out there, maybe we should
Speaker 1: stop doing this. Whoops, even though it would have been profitable. Yeah, exactly.
Speaker 1: Shall we leave it there? Yeah, let's leave it there. Okay.
Speaker 1: This has been another episode of the All Thoughts Podcast.
Speaker 1: I'm Tracy Alloway. You can follow me on Twitter at
Speaker 1: Tracy Alloway, and I'm Joe Wisenthal. You can follow me
Speaker 1: at The Stalwart. Follow our producer Laura Carlson. She's at
Speaker 1: Laura M. Carlson. Follow the Bloomberg head of podcast, Francesca
Speaker 1: Levy at Francesca Today, and check out all of our
Speaker 1: podcasts at Bloomberg under the handle add Podcasts. Thanks for
Speaker 1: listening to