Latest / The Capitalism and Freedom in the Twenty-First Century Podcast / Bob Litterman (Kepos Capital) on Quantitative Investing, Liquidity Risk and Climate Policy

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Bob Litterman (Kepos Capital) on Quantitative Investing, Liquidity Risk and Climate Policy · The Capitalism and Freedom in the Twenty-First Century Podcast

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>> Bob Litterman: We should immediately price climate risk.
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It's time to slam on the brakes.
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We have to create appropriate incentives to reduce emissions.
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It could be a carbon tax, it could be a cap and trade system.
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It could be fossil fuel taxes.
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Most people don't understand is, I would say, the magnitude of this problem.
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International Monetary Fund has been for many years estimating how
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big is the subsidy to fossil fuels that's created by not imposing the Peruvian tax,
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the appropriate incentive to reduce emissions.
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In the most recent year they estimated it was $7 trillion.
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Not billions, trillions.
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[MUSIC]
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>> Jon Hartley: This is the Capitalism and Freedom in the 21st century podcast,
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an official podcast of the Hoover Institution Economic Policy working group
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where we talk about economics, markets and public policy.
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I'm Jon Hartley, your host.
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Today my guest is Bob Litterman, who is a founding partner at Kepos Capital where
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he's also chairman of the risk committee.
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Prior to Kepos Capital, Bob spent 23 years at Goldman Sachs where he was
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head of the quantitative resources group in Goldman Sachs asset Management for
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eleven years starting in 1998.
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Prior to that position,
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Bob headed the firm wide risk department from 1994 to 1998.
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And prior to that he was the co head of the model development group
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in the research department of the Goldman Sachs fixed income Division where Bob
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developed the famous Black Litterman model of portfolio optimization
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together with Fisher Black in 1990.
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Prior to that, Bob did his PhD in economics at the University of Minnesota
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under the supervision of Tom Sargent, graduating in 1990.
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Bob is also a prominent climate policy expert and advocate for of carbon taxes.
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Bob developed the easy climate carbon pricing model with
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Columbia's Ken Daniel and Gernot Weger and
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led the climate related market risk subcommittee of the CFDC.
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Thank you so much for joining us today, Bob.
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>> Bob Litterman: It's my pleasure.
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Thank you, Jon.
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>> Jon Hartley: Bob, I wanna first sort of get into your background here.
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You grew up in Phoenix, Arizona.
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How did you first get into economics and decide you want to do a PhD?
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Was it during your undergraduate studies at Stanford?
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Were there any particular influences that stood out?
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>> Bob Litterman: No, actually my interest in economics was really a bit of a course
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correction that came later.
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My undergraduate degree at Stanford was in human biology, which by the way
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was itself a bit of a course correction because I started as a physics major.
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But in the spring of 1970, the Vietnam War protests
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reached a crescendo and that was my freshman year at Stanford and
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the entire student body kind of went on strike that spring and
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I thought I needed some more flexibility in my program.
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So I dropped out of physics and
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I joined about a third of my freshman class in this new major and
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in an interdisciplinary program called human biology,
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which to this day is still one of the most popular majors at Stanford.
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But my original career choice was actually journalism, which was an interest
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that I began in high school, following in the footsteps of my older brother.
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And I've always appreciated the basic writing skills
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that I honed as a young journalist.
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My first real job after spending four years at the Stanford daily was as
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a general assignment reporter for the San Diego Union.
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But after only three months on the job,
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they moved me from San Diego county out to El Centro in Imperial county,
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which is kind of rural Central Valley, California, best known for
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something called the Salton Sea, which was a bit of a man made disaster.
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But anyway, I was reporting from the El Centro office, and
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in fact, I was the El Centro office, and
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I had to fill a page every day of whatever was going on in the valley.
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And that was kind of fun.
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But like I said, it was a little bit out in the boonies.
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There were no computers in Central Valley of California at that time.
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And more important, it just wasn't a very attractive location for
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a single Jewish male looking to meet members of the opposite sex.
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So I thought about going back to graduate school, and
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I guess I assumed that economics training would help me become a business writer.
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But it turned out I went to the University of California,
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San Diego, which had a very mathematical program.
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And when my first micro course, I had a textbook theory of value by Debreu,
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it's kind of a very mathematical text, it became clear that
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it wasn't really gonna help me as a business writer, but
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I enjoyed the math, and I had unlimited access to computer time there.
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And I met my wife Mary there.
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So it was all good.
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>> Jon Hartley: Wonderful.
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It's funny, you wouldn't be the only economists out there
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to go from physics to economics.
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Hoover's very own John Cochrane was in a physics PhD
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program before moving to economics and many others.
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A lot of the same, I think, mathematical tools solving partial differential
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equations and so forth, it can be very useful in economic theory as well.
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I'm curious, you're getting a little more into what graduate school was like.
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I'm curious, what was graduate school like for you?
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I think you graduated from the University of Minnesota.
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You were a Tom Sargent student, if that's correct, and I know that in that time,
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certainly Minnesota Tom Sargent, they're very famous.
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There is a very famous and distinctive place inside macroeconomics,
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and not to mention you were there when the rational expectations revolution was
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going on, being led by luminaries like Sergeant Lucas Prescott,
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which revolutionized macroeconomic theory and modeling.
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I think you might have even overlapped, let's say, Lars Hansen as students and
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I'm sure many others.
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What was that time like, doing a PhD in the late 1970s?
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>> Bob Litterman: Yeah I mean, it was incredible.
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You have to appreciate that.
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I ended up at Minnesota because I followed my wife,
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who was my girlfriend at the time, from San Diego to Minnesota because
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that's where she was from, and she was going back there and going to.
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So after a year, she transferred back to Minnesota.
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And I went out there during the summer, I guess it was in August, and just decided
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kind of spur the moment, to see if I could get into the Minnesota program.
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So I went to the graduate department and said,
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any chance I could start here in a few weeks?
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And they were very nice.
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In fact, they said, sure, we don't have any money for you, but they got me a job.
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They actually had some contacts at the computer center.
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And so I went and applied for a job there, which, to be honest,
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I wasn't at all qualified for, but I became a consultant.
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Consultant, and those were the days when computer centers were offsite and
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you'd go there with cards.
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And I just sat at a desk and answered questions and
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supported some computer packages for statistical analysis,
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all of which were terrific skills to put together.
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But anyway, yeah,
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the department at Minnesota that I got into was unbelievable.
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And of course, at the time, I didn't appreciate that.
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So I showed up and, yeah, Sargent was there,
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Sims was there, I worked closely with them.
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You mentioned Lars Hansen.
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I mean, he was one year ahead of me.
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And you can imagine I didn't really have the background
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to appreciate what was going on around me.
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I thought when I met Lars, wow, this guy's amazingly smart.
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But I just thought, well, if they're this smart at Minnesota,
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can you imagine what they must be like at Harvard and Stanford and all?
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And of course, that wasn't the case.
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Lars was the best economist of our generation.
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And so I had kind of a warped view.
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In fact I ended up working with both Sargent and Sims.
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But I didn't think I was going to be an academic
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even from the beginning I was there, I thought, you know,
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I'll pick up some useful tools and especially the computer tools and,
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you know, forecasting tools and a lot of things.
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Anyway, after a few years and I had a very nice dissertation
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on forecasting with Bayesian vector autoregressions,
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Tom asked me, where do you want to teach?
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I said, I'm planning to go into business.
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And he was like, you can't do that.
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I didn't appreciate how he would react, but of course, he made a good argument.
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He said, you can always go from academia to business, but
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if you don't go on the market and start in academia, you can never do it later.
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And so he convinced me to go on the market.
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And to be honest, Lars had just gone on the market a year before from
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Minnesota and ended up at Carnegie Mellon.
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But a year later there was a feeling that, boy,
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these Minnesota students are really good.
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And so I kind of rode that wave and got a lot of good offers,
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including one from MIT.
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So I ended up going to Mit.
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But even after four years at Minnesota,
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I just thought this rational expectations thing is
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kind of interesting, almost religion here.
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And I assume that that was sort of freshwater
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economics at saltwater economics at Harvard and
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Yale and so on, and would have their own ideas.
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But when I got out, what I discovered is, no, actually,
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Minnesota was kind of special.
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And the folks at MIT and Harvard were very
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interested in learning about the tools and techniques and so
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on that we were developing, the Rational expectations school.
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So it's really, when I got to MIT, I realized, wow,
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Minnesota was incredibly special, yeah.
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>> Jon Hartley: Absolutely, well, yeah, it's absolutely right.
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I'd imagine that all the RBC machinery started there in those freshwater schools,
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places like Chicago, Minnesota.
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And that was very much a response to the great inflation era and
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the inability of traditional ISLM Keynesian models to really be
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able to explain, I guess you could call it stagflation,
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high unemployment, high inflation at the same time.
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And just this sort of general criticism or things like the Lucas critique that really
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there was a need for micro models or a model that has individual agents in it.
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And then over time, I guess, through the 80s and the 90s,
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places like Harvard, MIT, and scholars there developed the various
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sort of new Keynesian models that are based on those DSG models and
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include things like sticky wages, sticky prices, and
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I'm sure it creates more room for policy for central banks and so forth.
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I'm curious, I mean, what was that time at MIT like?
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And I'm curious, I also want to get into your time at Goldman Sachs,
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how you got your first job at Goldman Sachs in the Goldman Sachs fixed
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income division research department, how you met Fisher Black.
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I mean, really one of the great financial economists of the 20th century.
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And I'm curious, how did the famous Black Litterman model of portfolio management or
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portfolio optimization come about?
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>> Bob Litterman: Sure.
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Well, interestingly,
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I first met Fisher when I was at Minnesota as a graduate student.
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And I was working at the Federal Reserve bank there as a research assistant
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for Tom.
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And Fisher more or less called the folks at Minnesota up and
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said, can I come give a seminar on rational expectations?
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And so they said, sure.
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And he came actually to the Fed.
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And I remember it well, it was a very bizarre seminar.
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And later I learned that this was very typical of Fisher.
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But you can imagine, I was just a graduate student.
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And he shows up and starts talking about some Robinson Crusoe economy and
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put some things on the board.
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And after about ten minutes, I think it was Tom who said, fisher,
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can you talk a little bit more about the production function in this economy?
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And Fisher was like, hmm, long pause,
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which turns out was typical of Fisher.
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And he says, yeah, that's a very good question.
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And then he sits down, pulls out a notebook, and starts writing.
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And basically that was the end of the seminar.
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At that point, it just became a back and forth.
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But I thought, wow, that is very strange.
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Well, then later I accepted a job at MIT,
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and MIT was also a pretty incredible place.
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I mean, Paul Samuelson was there and
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he used to hold- Stan Fisher and- Stan Fisher was there,
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Larry Summers was an assistant professor along with me.
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We had offices next to each other and
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an awful lot of really good graduate students.
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And, you know, but Fisher was there in the business school and
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so I got to interact with him again there.
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And after two years at MIT,
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I decided that really, academia wasn't for me.
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I enjoyed the research.
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I really enjoyed my time at the Minneapolis fed when
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I was there working as a graduate student.
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And so I called up Tom and said, any chance I could come back to Minnesota?
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And the next day I got a call from the head of the research department at
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the Minneapolis fed, Art Rolnick, saying, bob, I hear you want to come back.
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When do you want to start?
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And so I went back to the Minneapolis Fed for about five years.
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And I was kind of in charge of forecasting, and
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I was really quite happy working for the president of the Minneapolis Fed,
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Jerry Corrigan, who was kind of Volcker's right hand man at the time.
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So it was very interesting times.
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>> Jon Hartley: Later became the president of the New York Fed.
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>> Bob Litterman: Later became the president of New York Fed.
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And then after that, came to Goldman Sachs.
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>> Jon Hartley: Right, right.
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>> Bob Litterman: Yes, I worked with him there.
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So Jerry and I overlap quite a bit.
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But at some point I got a call from a headhunter.
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I didn't even know what a headhunter was.
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And you can imagine my fed compensation at that time.
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I think it was $35,000.
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Quite a bump up from MIT, by the way, where I think when I left,
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I was making 20,000.
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But anyway, Goldman Sachs offered me a base salary of 200,000.
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And they kind of said there was a bonus.
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But what I didn't realize was that the bonus was the bigger part of
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the total compensation.
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And.
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But anyway, it was kind of an offer I couldn't refuse.
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Marion, I moved to New York, and
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I started at Goldman in 1986.
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Well, Fisher Black was at Goldman.
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He was there.
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>> Jon Hartley: Was he part of hiring you there, or.
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>> Bob Litterman: He was indeed.
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And it's funny, if it was up to Fisher, I don't think I would have gotten the job.
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He was one of the first PhDs hired on Wall street.
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They called him a rocket scientist, I think.
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And the position he took, they kind of said, Fisher, what do you wanna do?
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And he said, well, there's this arbitrage out there
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between the S and P future and the value line future.
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And I've been talking about it for years at MIT, but
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it's still there, so let's try and take advantage of it.
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And sure enough, there was a research assistant there who
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programmed up the trades that you'd have to do, and they did them.
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And sure enough, made about $20 million for Goldman Sachs,
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killed the value line index, and it was considered a big success.
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So I was maybe the next round of PhDs that they hired.
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And, well, I was interviewed by Fisher,
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even though he was in the equity division, and I was going to come into
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the fixed income division, but he, he was the academic there.
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And so I came in for the interview, and he said to me, bob,
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you're an econometrician, right?
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I said, yeah, I'm an econometrician.
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I thought, this is great.
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He says, so
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what makes you think an econometrician can add any value on Wall Street?
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I was like, I don't know, maybe we can estimate some parameters or something.
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He was very skeptical.
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And so then he asked me, he says, so tell me,
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what's the difference between a first moment and a second moment?
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And I was like, well, first moment is like a mean, and
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the second moment is like a variance.
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I had no idea what he was asking, but he was a very practical guy,
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and he had this idea that expected returns,
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which you can estimate by taking the mean return,
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are basically unknowable and are inestimable.
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You can't really get a lot of information about it.
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And whereas variances, if you chop the data finely enough,
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you can get more and more information.
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So he felt like you might as well treat those as observable.
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Well, I flunked that question, and
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I don't know if Fisher would have hired me if that was coming
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into equity research, but it was fixed income research.
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I got the job, and then it wasn't too many years later,
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maybe three years after I joined Goldman.
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And I had a great time there.
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It was a time when markets weren't as efficient as they are today.
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And if you came in with some computer skills,
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there were just a lot of things that had never been done.
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Estimating factor models, fitting yield curves, valuing embedded options.
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We were doing all kinds of fun things.
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And at some point, my boss said to me, our clients in Japan,
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so this is in the late 80s, and the Japanese stock
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market had gone crazy, and they had all this wealth that
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they wanted to invest globally in fixed income markets.
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And so my boss said, bob, we need an asset allocation model for our japanese clients,
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but I don't want to have a separate model and Tokyo and a different one in New York.
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So why don't you build us an asset allocation model that we can use
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everywhere?
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And so he said, and why don't you talk to Fisher about it?
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So I went to Fisher.
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I'd never used or played with an asset allocation model.
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I said, so what are these models?
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What do you suggest, Fisher?
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And he said well, I always start simple, and
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then if it doesn't work we can get more complicated.
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But the simplest approach here is mean variance optimization.
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>> Jon Hartley: Just like Markowitz mean variance optimization starting from here.
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And I think it's solving the problem that expected returns aren't really a stable
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thing.
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>> Bob Litterman: Right, right.
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So I looked up the mean variance model.
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I'd never, like I said, never played with it, programmed it up.
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It was pretty simple.
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And I was only using bonds.
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So I had, I think, 20 bond markets and then 19 currencies to play with.
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And so we put that into a covariance matrix.
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Well, you can imagine it was nearly singular with all the high correlations
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among those currencies and bonds.
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And so I then put in the expected returns that came from Goldman Sachs's economists.
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It was a nice place to be because there were plenty of forecasts around, so
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I didn't have to worry about coming up with the forecast.
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Well, you can imagine without any constraints, the model went crazy and
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long, 400% here and short 300% there and so on.
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And so I went back to Fisher.
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Well, and by the way, if you then change those expected returns a little bit,
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maybe change the forecasted yield six months out by five basis points,
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the whole thing would change completely.
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>> Jon Hartley: Very unstable, that original.
23:27
So I guess just to give our listeners a bit of sort of a history of some of
23:32
these models and just really how important your contributions with Fisher Black are,
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and those, I really can't overstate how seminal these contributions have been.
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So Fisher Black came up with black scholes model in the 70s and
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that became a widespread options pricing model.
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Black Litterman model is based on Markowitz mean variance approach, where.
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You're trying to maximize your expected returns, so
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that's taking the percentage returns or just percentage changes on prices.
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And we're trying to maximize returns subject to some level of volatility or
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standard deviation of those returns.
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And the problem with the original sort of Markowitz mean variance optimization,
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which came in the 1960s.
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And Harry Markowitz, who's now passed,
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famously came up with, I think, maybe in the 50s or 60s.
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But the problem, as you've illustrated,
24:34
is that it's not very practically useful to investors.
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Because the recommended weights will change very quickly depending on how you,
24:43
say, update or change your expected returns.
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And then, many investors have various constraints.
24:49
They can't short, or they can't use derivatives or things like that.
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How do you make a model useful to those sorts of investors?
24:55
That's exactly what Black-Litterman does, and it's been used
24:59
certainly by quantitative investors and many others very pervasively.
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And so the success of that approach can't be overstated, and I think, tragically
25:12
it's also worth pointing out, too Fisher Black passed away in the mid 1990s.
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That was life cut short, very young, from cancer, and
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it's amazing I've heard stories from my time at Goldman Sachs that Fisher,
25:28
he's a very kind of eccentric guy.
25:31
He was one of the first people to get computers and
25:35
sort of would be in his office and close his blinds,
25:39
all sorts of things, but like a true genius in many respects.
25:44
And the work that both yourselves have done and contribute to and
25:49
being pioneers in financial economics really can't be overstated.
25:54
I wonder whether maybe Bob Rubin was part of that hiring.
25:56
I mean, Bob Rubin, who later became secretary of the treasury,
26:00
he sort of made his name in that fixed income division.
26:04
And there was a lot going on at that time in terms of PhDs,
26:08
adding value to financial economics.
26:12
You think about those that aren't familiar with the book,
26:16
When Genius Failed, which talks a lot about long term capital management.
26:20
That firm was founded by Salomon Brothers alumni.
26:24
They were doing a lot of fixed income arbitrage,
26:28
they famously blew up in the late 1990s.
26:32
They also had Myron Scholes involved
26:37
with their firm, along with Bob Merton.
26:43
Whereas yourself and Fisher Black were at Goldman Sachs and
26:48
Culmen Sachs survived that era of the late 1990s when
26:53
during the Asian tire financial crisis.
26:56
Which sort of blew out some spreads like the on and off treasury trade,
27:03
that those fixed income arbitrages kinda blew up temporarily and
27:08
firms like LTCM were taking a lot of risk.
27:12
I sort of wanna move onto your later Goldman years, investing in GSAM and
27:18
using tools in that group, which [INAUDIBLE] I worked in after your time,
27:24
after you had left in 2009, I actually joined that group in 2011.
27:31
You were really one of the great pioneers of quantity investing.
27:34
And on top of developing Black Letterman portfolio optimization model,
27:39
you were really one of the first to really build and run systematic
27:43
quantitative portfolios based on factors and ideas like momentum and value.
27:49
The idea in the case of momentum, if a stock's gone up, say a lot over the past
27:54
twelve months, that it's gonna continue to go up over the next few months or so.
27:59
Value being sort of almost diametrically the opposite of that, but
28:02
somewhat different.
28:03
The idea that companies that have high book to market ratios or
28:09
good fundamentals should outperform in the future.
28:13
And you've done, I think, a lot of this in the macro space as well, so
28:18
thinking about time series momentum and various macro assets.
28:21
It's worth noting that in this era,
28:24
you moved to Goldman Sachs Asset Management in 1998.
28:27
It's worth noting that there are a lot of economics PhDs working in those
28:32
quant groups as well within GSAM, Goldman Sachs Asset Management.
28:36
A good number of Eugene Fama students in that era,
28:40
you had folks like Cliff Asness who left to start EQR.
28:43
Mark Carhartt, [INAUDIBLE] these are all University
28:47
of Chicago economics PhD students, students of Eugene Fama.
28:52
You also co-authored with many of the people in the Goldman Quant group,
28:57
the investment classic modern investment management equilibrium approach,
29:02
which talks about things like Black-Litterman and related concepts.
29:07
And you really helped build that quant business at Goldman,
29:10
at its peak in the mid two thousands,
29:12
became one of the largest quantitative hedge funds in the world.
29:15
And since leaving GSAM, you also co-found Kepos Capital,
29:19
which is a quantitative asset management firm that you run along with
29:23
Mark Carhartt, many others from the old GSAM team.
29:27
The firm now manages over $2 billion, again,
29:29
each serve as chair of the risk committee.
29:31
What do you think is the best case for systematic quantitative investing today?
29:37
So, using factors, thinking about things in expected returns and
29:41
volatilities, using portfolio optimization tools like Black-Litterman,
29:46
I mean, what is the best case for that approach today?
29:50
As opposed to, say, a more discretionary or
29:54
concentrated types of investing that many others still use today?
29:59
>> Bob Litterman: Yeah, well, your brief history there is very accurate.
30:06
And you're right that when Fisher and
30:08
I first started with that mean variance model, it was incredibly badly behaved.
30:13
And I remember going to Fisher and saying to him,
30:16
I don't know how people can use this?
30:19
And he said, yeah, no, they put constraints on everything,
30:24
maximums and minimums.
30:25
And I said, well, then there's no value added, and he said, yeah, no,
30:30
there's no value added.
30:31
And he was right, and so he suggested, why don't we put this equilibrium in there?
30:39
It was a very academic sounding idea,
30:43
but it turned out to work really well.
30:47
And so we started trying to market the model, actually, at first.
30:51
And we discovered that there weren't too many people out there, first of all,
30:56
who were quants, and then secondly, who actually used a model.
31:00
[LAUGH] There was a little bit of quant marketing, but not a lot of really
31:04
depending on models, because people didn't trust him and they didn't work that well.
31:11
But I was very lucky at Goldman Sachs to come in and
31:17
have Fisher there and develop that model with him.
31:23
And we first started using it internally to
31:27
understand that the risks that Goldman was taking.
31:33
And then we started using it at Goldman Sachs Asset management to build
31:38
portfolios.
31:39
In fact, Cliff Asness, who was at Goldman Sachs Asset Management before I arrived,
31:46
developed this quantitative team and started using Black-Litterman.
31:52
And he also used a computer program that I had a hand in creating.
31:59
Called rats regression analysis of time series.
32:02
And he was incredibly successful the first year of the hedge fund he ran at GSAM.
32:10
I think it had like 80 or 90% returns.
32:13
And as a young quant I was like, wow, that's pretty good.
32:18
>> Jon Hartley: Was that Global Alpha?
32:20
>> Bob Litterman: Yeah, that was, Global Alpha it was called.
32:24
And then Cliff decided he was going to leave and form his own firm,
32:28
which obviously AQR became very successful.
32:32
And I was lucky that Goldman asked me to come in and
32:36
take over the business that he was running.
32:39
And that's when I saw these incredible returns and
32:43
the use of Black Litterman and RATS.
32:47
And then Mark Carhartt and Ray were there.
32:51
And so it was a great time.
32:53
In fact, very lucky for me, the timing,
32:57
because that was right before LTCM blew up.
33:02
And as you may know, the broker dealer side of
33:06
Goldman Sachs had a lot of the same trades on that LTCM had.
33:11
So having left the risk management position right
33:15
before all of that happened was good timing.
33:20
And then arriving in GSAM and heading this quantitative group when
33:25
markets were very disrupted also created an awful lot of opportunities.
33:31
Volatility, which not a lot of people thought of as an asset class,
33:36
but we did was trading long term.
33:40
Volatility was trading at unbelievable levels, and
33:44
you'd have to think that the market would be disrupted and three times
33:49
normal volatility for the next five years to make these prices make sense.
33:55
So we decided, all right, we can do this.
34:00
Everyone was afraid of touching it because it had just gone up and
34:05
up and up as LTCM and others were covering their positions and
34:09
they had shorted the volatility and now it was just going crazy.
34:14
We came in and we started selling volatility at the peak and
34:19
it turned out to be a very, very profitable trade.
34:23
So that was lucky.
34:27
And then the markets weren't as efficient in those days.
34:31
And so you're right, we started investing in factors and
34:37
it seemed like this just was almost like magic.
34:42
You could create these factors that would work and
34:47
be uncorrelated with the market and
34:50
you put together a bunch of these factors that are uncorrelated and
34:56
you can get very, very good returns.
35:01
And there are a couple of lessons here [LAUGH] that we discovered.
35:06
One of them is that you have to worry about crowded trades, and
35:10
that's kind of obvious.
35:12
>> Jon Hartley: And I'm going to get to that in just one one second, I mean,
35:15
before we get to the quant crisis of 2007.
35:18
>> Bob Litterman: Yeah, sure.
35:19
>> Jon Hartley: Its so worth highlighting just how successful
35:22
the Goldman Sachs asset management quant business was during the 2000s.
35:27
So we were talking about the late 90s, LTCM blowing up,
35:31
you coming into GSAM in 1998.
35:34
There was a stretch from roughly that 90s period, to really 2006,
35:39
2007 or so, where Goldman Sachs asset management was running
35:44
the largest quantitative hedge funds in the world.
35:48
And it was so successful that I know some of the heads of or
35:55
Mark Harhar they had special comp deals.
36:00
They were getting paid more than the CEO of Goldman Sachs,
36:03
Lloyd Blankfein at the time, in certain years.
36:07
Also, some of the returns on the global Alpha fund were,
36:11
I think there was maybe even a three digit return year.
36:15
I mean, it was so successful, you guys were pioneers in using
36:20
factors when factors maybe weren't quite being priced in,
36:26
or markets weren't quite as efficient.
36:30
But its amazing.
36:31
And there were these famous ski trips to Colorado.
36:36
Ray and Mark and yourself were so important, critical to the business, that
36:41
the firm made you guys ride on different private planes, because if you lost one,
36:47
it would be such a critical loss to Goldman Sachs and the firm at that time.
36:52
So you really can't stress enough, one, how successful these academic ideas were.
36:58
You're coming up with expected returns using factor models and
37:02
putting those expected returns into a portfolio
37:05
optimization model like Black Litterman, super successful.
37:11
And really, I think a case study of really just how academic thinking can be very,
37:17
very useful in the real world, especially in finance.
37:22
So then we hit 2007, and for the non quants out there,
37:26
we'll explain a little bit about what happened there.
37:30
But for quants, the quant crisis of 2007 is a distant but
37:34
still very important memory.
37:36
And Global Alpha, one of the many quant funds that Goldman ran at the time,
37:42
was caught very heavily in it.
37:45
And I think part of the explanation there, kind of what happened was
37:50
that there were a lot of crowded trades, a lot of quantum mechanism was becoming so
37:56
popular that it was causing crowding within certain names or certain trades.
38:02
Everybody was sort of doing similar things with leverage.
38:06
And then what happened was in August of 2007,
38:08
a lot of people tried to get out of those trades very quickly.
38:12
And it sort of bears some similarities to, for example,
38:16
maybe the treasury liquidity crisis of 2020, or
38:20
perhaps even more appropriately, that the recent yen carry trade
38:25
liquidity crisis that we've seen recently in the summer of 2024 here.
38:31
I'm curious, what do you think the main lessons of the quant crisis of 2007 are as
38:36
it applies today?
38:37
>> Bob Litterman: Yeah, well, you've described it, right.
38:40
But I would say, first of all, avoiding crowded trades.
38:45
That's pretty obvious.
38:46
And people understand that.
38:50
>> Jon Hartley: And you can monitor that, too, I suppose,
38:52
with things like 13 f filings and things like that.
38:55
>> Bob Litterman: Right. How do you identify a crowded trade?
38:58
It's not always obvious and in particular for us quants.
39:06
And really, the crisis started in quant equities,
39:11
and we had small positions in lots of hundreds of thousands,
39:17
really, of individual equities.
39:21
And none of those positions were large relative to the liquidity of that stock.
39:27
And that was metric that we use.
39:30
We had limits on how big a position we could have,
39:34
such that if we traded small pieces every day so that we didn't have
39:39
any significant market impact, how quickly could we get out?
39:44
And we said, we have to be able to get out within a couple of months.
39:48
Well, it turned out we were thinking about crowding in the wrong way.
39:55
It wasn't the individual equities that were crowded.
39:57
It was the factors.
39:59
That were crowded.
40:01
And what had happened was not obvious to us.
40:07
But as you describe it, over a period of years,
40:12
quantitative asset managers had really quite good returns.
40:17
We were one of them, but there were many others.
40:21
There was now at Blackrock.
40:25
At the time it was BGI, and
40:28
then you had of course, Jim Simons and others.
40:34
We were one of many, and we didn't see the crowding.
40:40
But what had happened is, as more and
40:44
more people started investing using these approaches,
40:49
the price pressure would push these stocks to the,
40:53
to line up along the quant factors.
40:57
You can imagine.
40:59
You mentioned momentum, valuation factors.
41:02
There's various factors.
41:04
>> Jon Hartley: Carry, too.
41:05
Carry is another high interest rate currencies and borrowing,
41:09
low interest rate currencies, kind of like the dollar yen trade you
41:14
invest in US has had historically higher interest rates than Japan,
41:19
which has been at 0% for a long time.
41:21
>> Bob Litterman: It started to unwind, and when that started,
41:26
all of a sudden all of these factors at once started to go the wrong way.
41:34
And so a lot of people tried to get out.
41:37
People called us and said, are you doing this?
41:40
We were like trying to figure out who's doing this.
41:44
I don't know that there was ever someone who started it.
41:49
I certainly don't know.
41:52
All of a sudden, those limits that we had that said you can't
41:57
have too big of position were meaningless because we were losing
42:02
five standard deviations every day for several days in a row.
42:10
We had limits on the amount of borrowing we could do against the portfolio,
42:16
and we were coming up against that.
42:19
Avoiding crowded trades is one lesson.
42:23
And the fact that the quants themselves can actually cause
42:29
these prices to go beyond where they should be was another lesson.
42:37
Now, these days, I would say you talked
42:42
about the carry trade and yen and some of the other.
42:51
I guess it was treasuries that became very disrupted during COVID.
42:58
I think systematic quantitative investing is certainly evolving.
43:05
And with access to huge new data sets,
43:08
I would say that's one of the differences today versus a decade or
43:14
two back, is the amount of data that we have.
43:18
And then computers and computer power, machine learning, AI, machine learning.
43:27
In some ways I have to laugh because we used to use regressions, and those
43:31
quantitative tools that I was talking about back in the 80s were very valuable.
43:36
Well, it's really a progression of the same idea,
43:40
except that obviously with natural language processing and so
43:45
on, we've come a long way and markets are becoming more efficient.
43:51
But I think the discipline to
43:54
follow a systematic approach is always an advantage.
43:59
And with so much going on these days,
44:03
geopolitically in the monetary and fiscal policy,
44:09
that discipline is really valuable and
44:14
we think provides an advantage in this latest yen trade.
44:22
Managers making discretionary decisions often are whipsawed by
44:27
the volatility in the markets.
44:29
And with systematic managers,
44:32
when you have a disciplined process, you tend not to overreact.
44:38
You have processes that were set up to react appropriately and
44:42
you have a much better chance of riding out the volatility,
44:47
I would say, with portfolios and performance intact.
44:51
That's certainly what we have experienced at Kepos recently.
44:56
>> Jon Hartley: That's a great point.
44:57
When that August drawdown was going on and I think at one point
45:01
maybe the global fund lost maybe 40% or so of its market value in a day.
45:06
And it came back very quickly, like what we've seen with the yen USD trade,
45:12
carry trade in its subsequent reversal.
45:16
I know like Gary Cohn for example, because one concern was that what was about
45:21
solvency too, and whether or not certain broker dealers would even take trades
45:26
from Goldman Sachs Asset Management or gold alpha fund, what was a problem?
45:32
And I know Gary Cohn famously was basically then the COO of Goldman
45:36
at the time, chief operating officer.
45:39
He was stationed out in the GCM floor for a while and
45:43
would be calling up if, say, Deutsche bank wouldn't take a trade.
45:48
He would get on the phone with a much senior person at Deutsche and
45:51
would get them to take the trade and exert that pressure.
45:55
Speaking on behalf of the entire firm,
45:59
never heard something quite like that in terms of just how crazy that was.
46:08
And Goldman Sachs asset management survived and
46:11
lived to tell a tale and still running quantitative portfolios today.
46:16
I wanna shift the conversation a bit to climate because you've been a long
46:21
time climate policy expert and defender of pguvian taxation,
46:25
carbon taxes as the appropriate policy response to climate change.
46:30
How worried in your mind should we be about climate?
46:33
What do you think needs to be done?
46:35
What is the best case for, say, policy solutions to climate in your mind?
46:40
Also wondering if you could maybe explain to us a little bit about what the easy
46:45
climate carbon pricing model is.
46:47
And I think it's fair to say that carbon taxes have had a number of challenging
46:53
political economy issues, whether it be the yellow vest protests or
46:58
the protests in France, the unpopularity of carbon taxes in Canada,
47:03
where axioms the tax is a central piece of the current Canadian
47:07
conservative leader Pierre Polyev's platform.
47:11
And he's currently leading Trudeau by 20 percentage points or so
47:15
in the polls ahead of the 2025 election.
47:17
I'm curious, how do you see carbon tax policy battles going in the future,
47:22
especially when we're talking about solving a challenge, which is sort
47:28
of a global one, and you have some of the largest polluters out there like China,
47:33
that maybe don't wanna take climate policy to curb emissions all that seriously.
47:39
How does one get beyond the global coordination problem?
47:43
Or could it be the case that say, various technological solutions like carbon
47:48
capture or electric vehicles or cheaper wind and solar,
47:52
may play a larger role in curbing climate emissions, say than policies?
47:56
What would you say to those sorts of arguments in making the.
47:59
The best case for a carbon tax regime?
48:03
>> Bob Litterman: Okay, well, there's a lot there, I would say.
48:06
First of all, you're correct to start by asking about how risky is this?
48:11
How much should we be worried?
48:13
Because that's the heart of the problem.
48:16
We don't know what the future is going to bring, and
48:20
there's many possible scenarios.
48:22
It's a risk management problem ultimately.
48:27
And the answer is we should be very worried about
48:31
the potential impacts because we're making these slow but
48:36
potentially unstoppable impacts from warming the planet.
48:41
And the basic problem is that we're not pricing the risk.
48:48
That's the essence of it.
48:52
It's an obvious mistake.
48:56
It's a simple mistake.
48:57
It's easy to understand.
48:59
But I would say something that people don't appreciate
49:03
is that it's an urgent problem when you're managing risk.
49:08
Time itself is a scarce resource and we're wasting time when you're managing risk,
49:14
if you have enough time, you can solve almost any problem.
49:18
But it's when you run out of time that a risk management problem can
49:23
become an unstoppable catastrophe.
49:26
Should immediately price climate risk.
49:29
I've been talking about, it's time to slam on the brakes now for almost a decade.
49:36
And what it means is we have to create appropriate
49:40
incentives to reduce emissions.
49:44
It could be a carbon tax, it could be a cap and trade system,
49:48
it can be fossil fuel taxes.
49:50
There's all kinds of policies.
49:54
What most people don't understand is, I would say, the magnitude of this problem.
50:01
The International Monetary Fund has been for many years estimating
50:06
how big is the subsidy to fossil fuels that's created by not imposing the tax,
50:13
the appropriate incentive to reduce emissions?
50:17
Well, in the most recent year, they estimated it was $7 trillion.
50:24
Not billions, trillions.
50:26
When you look at how they did this estimate, it's pretty rigorous,
50:31
although I would say a little bit conservative,
50:35
because at the heart of it is something called the social cost of carbon,
50:41
which is the damages, the present value of the expected marginal
50:46
damages that are done by emitting a ton of CO2.
50:49
They at the IMF use a social cost of carbon of about $70 a ton.
50:55
Whereas the most recent work that's been done by the EPA in the US,
51:00
Michael Greenstone, a University of Chicago economist,
51:05
I think probably has done the best work,
51:08
they're now estimating the social cost of carbon is over $200 a ton.
51:14
And at that- >> Jon Hartley: I wonder how how big those
51:17
standard error bars are on there.
51:19
>> Bob Litterman: Yeah, the standard error bars are huge.
51:21
No one knows what this is, but
51:24
if you come at it from a risk management perspective like I do.
51:29
And you say, well, we think the social cost of carbon is about $200,
51:34
but it could be anywhere from $50 to 500.
51:39
And you say, where do you want me to set it?
51:42
I say to you,
51:43
well, how confident do you want to be that you're going to solve the problem?
51:48
And the suggestion being that you probably want to be above the mean or the median.
51:54
You want to be at the upper end of that distribution because it's uncertain,
51:58
as you point out.
52:00
And that's the real problem, the fundamental uncertainty.
52:04
So, yeah, I got involved in this, actually,
52:07
when I was retiring from Goldman Sachs.
52:10
One of my other partners, Larry Linden, who was head of
52:14
the World Wildlife Fund board for a while, and resources for the future.
52:20
He invited me to lunch and we were talking and he said,
52:23
are you interested in the environment?
52:25
And I said, well, Larry, this climate change seems to me to be
52:29
a risk management problem that's not being addressed.
52:33
We're not pricing the risk.
52:35
And Larry said to me, well, Bob, a brilliant insight from an economist
52:40
like yourself, but the problem is no one knows where to price it.
52:44
And I took that as a bit of a challenge.
52:47
I thought, well, hell, I can read the literature,
52:50
you know, let's see what it says.
52:53
And so I did.
52:54
And actually, you know, Bill Nordhaus, who is, you know, the leading
53:00
economist on this and won a Nobel Prize- >> Jon Hartley: DICE model I think.
53:04
>> Bob Litterman: Yeah, DICE model.
53:05
He and I were the same generation.
53:08
We used to both be macroeconomists.
53:09
So I know Bill, and I was reading his stuff, and
53:14
it didn't take into account what we do in pricing risk on Wall Street,
53:20
where we worry about beta, the correlation of assets with the market.
53:27
And in the climate literature, they never worried about beta,
53:31
they never worried about correlation.
53:34
And they were really doing some pretty old fashioned risk analysis.
53:41
And so that kind of drew me in.
53:44
I started working, as you mentioned, with a couple of other economists,
53:49
Gernot Wagner and Kent Daniel.
53:51
And the three of us wrote a paper where we tried to
53:56
take seriously the uncertainty about climate.
54:03
We thought of the fragility of the planet as being an unknown
54:07
about which information would be revealed over time.
54:11
And so you form an optimal policy today and then you revise it.
54:19
If you get good news, you can lower the price.
54:22
If you get bad news, you can raise the price.
54:25
And in that context, we found something very interesting,
54:28
which is that the optimal policy is to immediately create a very strong incentive
54:33
to reduce emissions, strong enough that you're very confident that you're going
54:37
to solve the problem, that the emissions are going to come down, you're not going
54:42
to cross the tipping point, you're not going to have any of these disasters.
54:46
And in fact, one of the surprises to me was how hard it was to actually get
54:51
a model where there would be catastrophes.
54:54
You were always conservative enough that you would never go off the cliff.
55:02
That was interesting.
55:03
And something else we did which was interesting is we were able to
55:08
ask the following question, what's the cost of delay?
55:12
Yes, the optimal policy is immediately price emissions appropriately at a high
55:17
level.
55:18
But what happens if we wait a year?
55:21
And we could answer that as economists, you can set it up and say,
55:25
how much would you have to pay the current generation to wait a year before they
55:30
start pricing carbon?
55:32
And the answer was pretty shocking and pretty incredible.
55:38
It was basically that the cost of
55:42
delay grows quadratically.
55:47
Obviously it starts at zero and then as you move forward,
55:51
you're no longer pricing carbon and so you're emitting more into the atmosphere.
55:57
Well, that has two impacts.
55:59
One is The amount of carbon in the atmosphere is going up.
56:02
That's essentially linearly.
56:03
But the other thing that's happening is that the damage that's created by each
56:08
ton of carbon is also going up because that's a function of the amount of carbon
56:13
in the atmosphere.
56:14
And so when you then quantify how much does it cost,
56:20
it's about 2% of consumption times t squared.
56:26
And so one year is about 2% of consumption and
56:32
five years is like 25% times 2%.
56:37
So 50% of consumption.
56:40
So you can see it's just a massive cost of not pricing climate risk and.
56:49
>> Jon Hartley: Well, but the political economy kind of challenge.
56:52
Yeah, it's a political economy challenge.
56:54
>> Bob Litterman: And you mentioned that, you know, the politics of this are tough,
56:59
tough, and they are tough not just in this country, but
57:03
as you point out around the world, we understand why the politics are tough.
57:08
It's because the cost is immediate and it's in your face.
57:13
Every time you fill up your car with gasoline, you experience the cost.
57:19
The benefit is far into the future and very uncertain.
57:24
And so it makes it easy for opponents.
57:29
And obviously, not everyone benefits from this.
57:33
Fossil fuel interests are not going to benefit from a rapid transition away
57:38
from fossil fuels.
57:39
And so this has made it very difficult to get the politics right.
57:46
And then the other problem is it's a global problem.
57:48
And so you have a bunch of countries that are saying, hey, you guys caused this.
57:55
The US, historically, we've been by far the biggest emitter.
58:01
Today, of course, China is the biggest emitter,
58:03
and China's pointing the finger at us, and we're pointing the figure at China.
58:07
And meanwhile, the emissions pricing
58:12
right now around the globe is pathetic.
58:17
The global average we estimate is about $4 a ton.
58:22
It should be two orders of magnitude higher than that.
58:25
And you've got this hurricane force wind, $7 trillion subsidizing fossil fuels,
58:33
and you're trying to move in the other direction.
58:37
It just doesn't work.
58:39
So, yeah, it's a huge problem and we've got to address it.
58:46
We should have addressed it just two decades ago and
58:50
we wouldn't have this problem, but
58:53
now it's really quite late and there's inevitable impacts now.
58:59
And the only thing is how bad are they going to be?
59:03
So we still should be slamming on the brakes.
59:06
I tend to be optimistic that we will soon because it's just so
59:11
insane that we're not doing it.
59:14
And it's so simple and so obvious, we need to have a price on carbon.
59:19
And until we do, the costs are just growing
59:23
quadratically, and they're huge already.
59:28
>> Jon Hartley: Well, it's a good question.
59:30
I know in the sense that as these sort of changes evolve,
59:36
people also adapt as well and say certain coastal real
59:40
estate becomes less valuable or inhabitable.
59:46
It is a slow changing process, and people can adapt these things.
59:50
And that has a pretty significant impact.
59:52
Really, this has been an amazing conversation, Bob.
59:55
And hearing about your risk management approach to climate policy,
59:59
I think it's very enlightening.
1:00:01
And your amazing career and ideas in quantitative finance, really amazing.
1:00:07
You're a true pioneer in quantitative finance,
1:00:09
and it's a real honor to be able to interview you here.
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Really want to thank you so much for joining us.
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>> Bob Litterman: Well, thank you, Jon.
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