Bob Litterman (Kepos Capital) on Quantitative Investing, Liquidity Risk and Climate Policy

Bob and Jon discuss Bob’s role in the history of the development of quantitative finance at Goldman Sachs, including his seminal work with Fischer Black, along with the carry trade liquidity crisis of August 2024 and its similarities to the quant crisis of 2007. They also discuss the case for quantitative investing and its ability to ride out risky environments. They also discuss a risk management approach to climate policy, Bob’s E-Z climate carbon pricing model, as well Bob’s advocacy for carbon taxes.

Recorded on August 9, 2024.

ABOUT THE SPEAKERS:

Robert Litterman is the Chairman of the Risk Committee at Kepos Capital LP. Prior to joining Kepos Capital in 2010, Litterman enjoyed a 23-year career at Goldman, Sachs & Co., where he served in research, risk management, investments, and thought leadership roles. While at Goldman, Litterman also spent six years as one of three external advisors to Singapore’s Government Investment Corporation (GIC). Bob was named a partner of Goldman Sachs in 1994 and became head of the firm-wide risk function; prior to that role, he was co-head of the Fixed Income Research and Model Development Group with Fischer Black. During his tenure at Goldman, Bob researched and published a number of groundbreaking papers in asset allocation and risk management. He is the co-developer of the Black-Litterman Global Asset Allocation Model, a key tool in investment management, and has co-authored books including The Practice of Risk Management and Modern Investment Management: An Equilibrium Approach (Wiley & Co.).

Litterman earned a Ph.D. in economics from the University of Minnesota and a B.S. in human biology from Stanford University. He is also the inaugural recipient of the S. Donald Sussman Fellowship at MIT’s Sloan School of Management and serves on a number of boards, including Commonfund, the Robert Wood Johnson Foundation, the Sloan Foundation, and World Wildlife Fund. He is also currently serving as the chair of the CFTC Climate-Related Market Risk Subcommittee.

Jon Hartley is a Research Associate at the Hoover Institution and an PhD candidate in economics at Stanford University, where he specializes in finance, labor economics, and macroeconomics. He is also currently a research fellow at the Foundation for Research on Equal Opportunity and a senior fellow at the Macdonald-Laurier Institute. Jon is also a member of the Canadian Group of Economists and serves as chair of the Economic Club of Miami.

Jon has previously worked at Goldman Sachs Asset Management as well as in various policy roles at the World Bank, the International Monetaty Fund, the Committee on Capital Markets Regulation, the US Congress Joint Economic Committee, the Federal Reserve Bank of New York, the Federal Reserve Bank of Chicago, and the Bank of Canada.

Jon has also been a regular economics contributor for National Review Online, Forbes, and the Huffington Post and has contributed to the Wall Street Journal, the
We have to create appropriateincentives to reduce emissions.
It could be a carbon tax,it could be a cap and trade system.
It could be fossil fuel taxes.
Most people don't understand is, I wouldsay, the magnitude of this problem.
International Monetary Fund has been formany years estimating how

(00:24):
big is the subsidy to fossil fuels that'screated by not imposing the Peruvian tax,
the appropriate incentiveto reduce emissions.
In the most recent year theyestimated it was $7 trillion.
Not billions, trillions.
[MUSIC]

>> Jon Hartley (00:49):
This is the Capitalism and Freedom in the 21st century podcast,
an official podcast of the HooverInstitution Economic Policy working group
where we talk about economics,markets and public policy.
I'm Jon Hartley, your host.
Today my guest is Bob Litterman, who isa founding partner at Kepos Capital where
he's also chairman of the risk committee.
Prior to Kepos Capital, Bob spent 23years at Goldman Sachs where he was

(01:13):
head of the quantitative resources groupin Goldman Sachs asset Management for
eleven years starting in 1998.
Prior to that position,
Bob headed the firm wide riskdepartment from 1994 to 1998.
And prior to that he was the cohead of the model development group
in the research department of the GoldmanSachs fixed income Division where Bob

(01:36):
developed the famous Black Littermanmodel of portfolio optimization
together with Fisher Black in 1990.
Prior to that, Bob did his PhD ineconomics at the University of Minnesota
under the supervision of Tom Sargent,graduating in 1990.
Bob is also a prominent climate policyexpert and advocate for of carbon taxes.
Bob developed the easy climatecarbon pricing model with

(02:00):
Columbia's Ken Daniel and Gernot Weger and
led the climate related marketrisk subcommittee of the CFDC.
Thank you so much forjoining us today, Bob.

>> Bob Litterman (02:11):
It's my pleasure.
Thank you, Jon.

>> Jon Hartley (02:13):
Bob, I wanna first sort of get into your background here.
You grew up in Phoenix, Arizona.
How did you first get into economics anddecide you want to do a PhD?
Was it during your undergraduatestudies at Stanford?
Were there any particularinfluences that stood out?

>> Bob Litterman (02:32):
No, actually my interest in economics was really a bit of a course
correction that came later.
My undergraduate degree at Stanfordwas in human biology, which by the way
was itself a bit of a course correctionbecause I started as a physics major.
But in the spring of 1970,the Vietnam War protests

(02:53):
reached a crescendo andthat was my freshman year at Stanford and
the entire student body kind ofwent on strike that spring and
I thought I needed some moreflexibility in my program.
So I dropped out of physics and
I joined about a third of myfreshman class in this new major and

(03:14):
in an interdisciplinaryprogram called human biology,
which to this day is still one ofthe most popular majors at Stanford.
But my original career choice was actuallyjournalism, which was an interest
that I began in high school, followingin the footsteps of my older brother.

(03:36):
And I've always appreciatedthe basic writing skills
that I honed as a young journalist.
My first real job after spending fouryears at the Stanford daily was as
a general assignment reporter forthe San Diego Union.
But after only three months on the job,

(03:57):
they moved me from San Diego countyout to El Centro in Imperial county,
which is kind of rural Central Valley,California, best known for
something called the Salton Sea,which was a bit of a man made disaster.
But anyway, I was reportingfrom the El Centro office, and

(04:19):
in fact, I was the El Centro office, and
I had to fill a page every day ofwhatever was going on in the valley.
And that was kind of fun.
But like I said,it was a little bit out in the boonies.
There were no computers in Central Valleyof California at that time.
And more important, it just wasn'ta very attractive location for

(04:43):
a single Jewish male looking tomeet members of the opposite sex.
So I thought about goingback to graduate school, and
I guess I assumed that economics trainingwould help me become a business writer.
But it turned out I went tothe University of California,

(05:04):
San Diego,which had a very mathematical program.
And when my first micro course, I hada textbook theory of value by Debreu,
it's kind of a very mathematical text,it became clear that
it wasn't really gonna helpme as a business writer, but

(05:25):
I enjoyed the math, and I had unlimitedaccess to computer time there.
And I met my wife Mary there.
So it was all good.

>> Jon Hartley (05:38):
Wonderful.
It's funny, you wouldn't bethe only economists out there
to go from physics to economics.
Hoover's very own John Cochranewas in a physics PhD
program before moving to economics andmany others.
A lot of the same, I think, mathematicaltools solving partial differential

(06:01):
equations and so forth, it can be veryuseful in economic theory as well.
I'm curious, you're getting a littlemore into what graduate school was like.
I'm curious,what was graduate school like for you?
I think you graduated fromthe University of Minnesota.
You were a Tom Sargent student, if that'scorrect, and I know that in that time,

(06:25):
certainly Minnesota Tom Sargent,they're very famous.
There is a very famous anddistinctive place inside macroeconomics,
and not to mention you were there whenthe rational expectations revolution was
going on, being led by luminarieslike Sergeant Lucas Prescott,

(06:45):
which revolutionized macroeconomictheory and modeling.
I think you might have even overlapped,let's say, Lars Hansen as students and
I'm sure many others.
What was that time like,doing a PhD in the late 1970s?

>> Bob Litterman (07:00):
Yeah I mean, it was incredible.
You have to appreciate that.
I ended up at Minnesotabecause I followed my wife,
who was my girlfriend at the time,from San Diego to Minnesota because
that's where she was from, andshe was going back there and going to.
So after a year,she transferred back to Minnesota.

(07:21):
And I went out there during the summer,I guess it was in August, and just decided
kind of spur the moment, to see if Icould get into the Minnesota program.
So I went to the graduate department andsaid,
any chance I could starthere in a few weeks?
And they were very nice.
In fact, they said, sure, we don't haveany money for you, but they got me a job.

(07:48):
They actually had some contactsat the computer center.
And so I went and applied fora job there, which, to be honest,
I wasn't at all qualified for,but I became a consultant.
Consultant, and those were the dayswhen computer centers were offsite and
you'd go there with cards.
And I just sat at a desk andanswered questions and

(08:11):
supported some computer packages forstatistical analysis,
all of which were terrificskills to put together.
But anyway, yeah,
the department at Minnesota thatI got into was unbelievable.
And of course, at the time,I didn't appreciate that.

(08:33):
So I showed up and,yeah, Sargent was there,
Sims was there,I worked closely with them.
You mentioned Lars Hansen.
I mean, he was one year ahead of me.
And you can imagine I didn'treally have the background
to appreciate what was going on around me.

(08:55):
I thought when I met Lars, wow,this guy's amazingly smart.
But I just thought, well,if they're this smart at Minnesota,
can you imagine what they must belike at Harvard and Stanford and all?
And of course, that wasn't the case.
Lars was the best economistof our generation.
And so I had kind of a warped view.

(09:20):
In fact I ended up workingwith both Sargent and Sims.
But I didn't think I wasgoing to be an academic
even from the beginning I was there,I thought, you know,
I'll pick up some useful tools andespecially the computer tools and,

(09:40):
you know, forecasting tools anda lot of things.
Anyway, after a few years andI had a very nice dissertation
on forecasting with Bayesianvector autoregressions,
Tom asked me, where do you want to teach?
I said, I'm planning to go into business.

(10:02):
And he was like, you can't do that.
I didn't appreciate how he would react,but of course, he made a good argument.
He said, you can always gofrom academia to business, but
if you don't go on the market and startin academia, you can never do it later.
And sohe convinced me to go on the market.

(10:24):
And to be honest, Lars had just goneon the market a year before from
Minnesota and ended up at Carnegie Mellon.
But a year later there was a feeling that,boy,
these Minnesota students are really good.
And so I kind of rode that wave andgot a lot of good offers,

(10:44):
including one from MIT.
So I ended up going to Mit.
But even after four years at Minnesota,
I just thought this rationalexpectations thing is
kind of interesting, almost religion here.
And I assume that thatwas sort of freshwater

(11:08):
economics at saltwatereconomics at Harvard and
Yale and so on, andwould have their own ideas.
But when I got out,what I discovered is, no, actually,
Minnesota was kind of special.
And the folks at MIT and Harvard were very
interested in learning about the tools andtechniques and so

(11:33):
on that we were developing,the Rational expectations school.
So it's really, when I got to MIT,I realized, wow,
Minnesota was incredibly special, yeah.

>> Jon Hartley (11:46):
Absolutely, well, yeah, it's absolutely right.
I'd imagine that all the RBC machinerystarted there in those freshwater schools,
places like Chicago, Minnesota.
And that was very much a responseto the great inflation era and
the inability of traditionalISLM Keynesian models to really be

(12:08):
able to explain,I guess you could call it stagflation,
high unemployment,high inflation at the same time.
And just this sort of general criticism orthings like the Lucas critique that really
there was a need for micro models ora model that has individual agents in it.

(12:29):
And then over time, I guess,through the 80s and the 90s,
places like Harvard, MIT, andscholars there developed the various
sort of new Keynesian models thatare based on those DSG models and
include things like sticky wages,sticky prices, and
I'm sure it creates more room forpolicy for central banks and so forth.

(12:52):
I'm curious, I mean,what was that time at MIT like?
And I'm curious, I also want to getinto your time at Goldman Sachs,
how you got your first job atGoldman Sachs in the Goldman Sachs fixed
income division research department,how you met Fisher Black.
I mean, really one of the great financialeconomists of the 20th century.

(13:16):
And I'm curious, how did the famous BlackLitterman model of portfolio management or
portfolio optimization come about?

>> Bob Litterman (13:23):
Sure.
Well, interestingly,
I first met Fisher when I was atMinnesota as a graduate student.
And I was working at the Federal Reservebank there as a research assistant
for Tom.
And Fisher more orless called the folks at Minnesota up and
said, can I come give a seminaron rational expectations?

(13:43):
And so they said, sure.
And he came actually to the Fed.
And I remember it well,it was a very bizarre seminar.
And later I learned that thiswas very typical of Fisher.
But you can imagine,I was just a graduate student.
And he shows up and starts talkingabout some Robinson Crusoe economy and

(14:05):
put some things on the board.
And after about ten minutes,I think it was Tom who said, fisher,
can you talk a little bit more aboutthe production function in this economy?
And Fisher was like, hmm, long pause,
which turns out was typical of Fisher.

(14:26):
And he says, yeah,that's a very good question.
And then he sits down,pulls out a notebook, and starts writing.
And basically that wasthe end of the seminar.
At that point,it just became a back and forth.
But I thought, wow, that is very strange.
Well, then later I accepted a job at MIT,
and MIT was also a prettyincredible place.

(14:50):
I mean, Paul Samuelson was there and
he used to hold- Stan Fisherand- Stan Fisher was there,
Larry Summers was an assistantprofessor along with me.
We had offices next to each other and
an awful lot of reallygood graduate students.
And, you know, but Fisher wasthere in the business school and

(15:16):
so I got to interact with him again there.
And after two years at MIT,
I decided that really,academia wasn't for me.
I enjoyed the research.
I really enjoyed my timeat the Minneapolis fed when
I was there working as a graduate student.

(15:38):
And so I called up Tom and said,any chance I could come back to Minnesota?
And the next day I got a call fromthe head of the research department at
the Minneapolis fed, Art Rolnick, saying,bob, I hear you want to come back.
When do you want to start?
And so I went back to the Minneapolis Fedfor about five years.

(15:59):
And I was kind of incharge of forecasting, and
I was really quite happy working forthe president of the Minneapolis Fed,
Jerry Corrigan, who was kind ofVolcker's right hand man at the time.
So it was very interesting times.

>> Jon Hartley (16:20):
Later became the president of the New York Fed.

>> Bob Litterman (16:22):
Later became the president of New York Fed.
And then after that,came to Goldman Sachs.

>> Jon Hartley (16:27):
Right, right.

>> Bob Litterman (16:27):
Yes, I worked with him there.
So Jerry and I overlap quite a bit.
But at some point I gota call from a headhunter.
I didn't even know what a headhunter was.
And you can imagine my fedcompensation at that time.
I think it was $35,000.

(16:47):
Quite a bump up from MIT, by the way,where I think when I left,
I was making 20,000.
But anyway, Goldman Sachs offeredme a base salary of 200,000.
And they kind of said there was a bonus.
But what I didn't realize was thatthe bonus was the bigger part of

(17:08):
the total compensation.
And.
But anyway,it was kind of an offer I couldn't refuse.
Marion, I moved to New York, and
I started at Goldman in 1986.
Well, Fisher Black was at Goldman.
He was there.

>> Jon Hartley (17:27):
Was he part of hiring you there, or.

>> Bob Litterman (17:30):
He was indeed.
And it's funny, if it was up to Fisher,I don't think I would have gotten the job.
He was one of the firstPhDs hired on Wall street.
They called him a rocket scientist,I think.
And the position he took, they kind ofsaid, Fisher, what do you wanna do?

(17:52):
And he said, well,there's this arbitrage out there
between the S and P future andthe value line future.
And I've been talking about it foryears at MIT, but
it's still there, so let's try andtake advantage of it.
And sure enough,there was a research assistant there who

(18:15):
programmed up the trades that you'dhave to do, and they did them.
And sure enough,made about $20 million for Goldman Sachs,
killed the value line index, andit was considered a big success.
So I was maybe the next roundof PhDs that they hired.

(18:36):
And, well, I was interviewed by Fisher,
even though he was in the equity division,and I was going to come into
the fixed income division, buthe, he was the academic there.
And so I came in for the interview,and he said to me, bob,
you're an econometrician, right?

(18:57):
I said, yeah, I'm an econometrician.
I thought, this is great.
He says, so
what makes you think an econometriciancan add any value on Wall Street?
I was like, I don't know, maybe we canestimate some parameters or something.
He was very skeptical.
And so then he asked me,he says, so tell me,

(19:18):
what's the difference betweena first moment and a second moment?
And I was like, well,first moment is like a mean, and
the second moment is like a variance.
I had no idea what he was asking,but he was a very practical guy,
and he had this ideathat expected returns,

(19:40):
which you can estimate bytaking the mean return,
are basically unknowable andare inestimable.
You can't really get a lotof information about it.
And whereas variances,if you chop the data finely enough,
you can get more and more information.

(20:01):
So he felt like you might aswell treat those as observable.
Well, I flunked that question, and
I don't know if Fisher wouldhave hired me if that was coming
into equity research, butit was fixed income research.
I got the job, andthen it wasn't too many years later,

(20:23):
maybe three years after I joined Goldman.
And I had a great time there.
It was a time when markets weren'tas efficient as they are today.
And if you came in withsome computer skills,
there were just a lot of thingsthat had never been done.
Estimating factor models, fitting yieldcurves, valuing embedded options.

(20:51):
We were doing all kinds of fun things.
And at some point, my boss said to me,our clients in Japan,
so this is in the late 80s,and the Japanese stock
market had gone crazy, andthey had all this wealth that
they wanted to invest globallyin fixed income markets.

(21:13):
And so my boss said, bob, we need an assetallocation model for our japanese clients,
but I don't want to have a separate modeland Tokyo and a different one in New York.
So why don't you build us an assetallocation model that we can use
everywhere?
And so he said, andwhy don't you talk to Fisher about it?

(21:34):
So I went to Fisher.
I'd never used orplayed with an asset allocation model.
I said, so what are these models?
What do you suggest, Fisher?
And he said well,I always start simple, and
then if it doesn't work wecan get more complicated.
But the simplest approach hereis mean variance optimization.

>> Jon Hartley (21:57):
Just like Markowitz mean variance optimization starting from here.
And I think it's solving the problem thatexpected returns aren't really a stable
thing.

>> Bob Litterman (22:07):
Right, right.
So I looked up the mean variance model.
I'd never, like I said,never played with it, programmed it up.
It was pretty simple.
And I was only using bonds.
So I had, I think, 20 bond markets andthen 19 currencies to play with.

(22:30):
And sowe put that into a covariance matrix.
Well, you can imagine it was nearlysingular with all the high correlations
among those currencies and bonds.
And so I then put in the expected returnsthat came from Goldman Sachs's economists.
It was a nice place to be because therewere plenty of forecasts around, so

(22:52):
I didn't have to worry aboutcoming up with the forecast.
Well, you can imagine without anyconstraints, the model went crazy and
long, 400% here andshort 300% there and so on.
And so I went back to Fisher.
Well, and by the way, if you then changethose expected returns a little bit,

(23:16):
maybe change the forecasted yieldsix months out by five basis points,
the whole thing would change completely.

>> Jon Hartley (23:24):
Very unstable, that original.
So I guess just to give our listenersa bit of sort of a history of some of
these models and just really how importantyour contributions with Fisher Black are,
and those, I really can't overstate howseminal these contributions have been.

(23:44):
So Fisher Black came up with blackscholes model in the 70s and
that became a widespreadoptions pricing model.
Black Litterman model is based onMarkowitz mean variance approach, where.
You're trying to maximizeyour expected returns, so
that's taking the percentage returns orjust percentage changes on prices.

(24:09):
And we're trying to maximize returnssubject to some level of volatility or
standard deviation of those returns.
And the problem with the original sortof Markowitz mean variance optimization,
which came in the 1960s.
And Harry Markowitz, who's now passed,
famously came up with, I think,maybe in the 50s or 60s.

(24:32):
But the problem, as you've illustrated,
is that it's not verypractically useful to investors.
Because the recommended weights willchange very quickly depending on how you,
say, update orchange your expected returns.
And then,many investors have various constraints.
They can't short, or they can't usederivatives or things like that.

(24:52):
How do you make a model usefulto those sorts of investors?
That's exactly what Black-Litterman does,and it's been used
certainly by quantitative investors andmany others very pervasively.
And so the success of that approach can'tbe overstated, and I think, tragically
it's also worth pointing out, tooFisher Black passed away in the mid 1990s.

(25:18):
That was life cut short,very young, from cancer, and
it's amazing I've heard stories frommy time at Goldman Sachs that Fisher,
he's a very kind of eccentric guy.
He was one of the firstpeople to get computers and
sort of would be in his office andclose his blinds,

(25:39):
all sorts of things, butlike a true genius in many respects.
And the work that both yourselveshave done and contribute to and
being pioneers in financial economicsreally can't be overstated.
I wonder whether maybe Bob Rubinwas part of that hiring.
I mean, Bob Rubin, who laterbecame secretary of the treasury,

(26:00):
he sort of made his name inthat fixed income division.
And there was a lot going onat that time in terms of PhDs,
adding value to financial economics.
You think about those thataren't familiar with the book,
When Genius Failed, which talks a lotabout long term capital management.

(26:20):
That firm was founded bySalomon Brothers alumni.
They were doing a lot offixed income arbitrage,
they famously blew up in the late 1990s.
They also had Myron Scholes involved
with their firm, along with Bob Merton.

(26:43):
Whereas yourself andFisher Black were at Goldman Sachs and
Culmen Sachs survived thatera of the late 1990s when
during the Asian tire financial crisis.
Which sort of blew out some spreadslike the on and off treasury trade,
that those fixed income arbitrageskinda blew up temporarily and

(27:08):
firms like LTCM were taking a lot of risk.
I sort of wanna move onto your laterGoldman years, investing in GSAM and
using tools in that group, which[INAUDIBLE] I worked in after your time,
after you had left in 2009,I actually joined that group in 2011.

(27:31):
You were really one of the greatpioneers of quantity investing.
And on top of developing Black Lettermanportfolio optimization model,
you were really one of the firstto really build and run systematic
quantitative portfolios based on factorsand ideas like momentum and value.
The idea in the case of momentum, ifa stock's gone up, say a lot over the past

(27:54):
twelve months, that it's gonna continueto go up over the next few months or so.
Value being sort of almostdiametrically the opposite of that, but
somewhat different.
The idea that companies that havehigh book to market ratios or
good fundamentals shouldoutperform in the future.
And you've done, I think, a lot ofthis in the macro space as well, so

(28:18):
thinking about time series momentum andvarious macro assets.
It's worth noting that in this era,
you moved toGoldman Sachs Asset Management in 1998.
It's worth noting that there are a lotof economics PhDs working in those
quant groups as well within GSAM,Goldman Sachs Asset Management.
A good number of Eugene Famastudents in that era,

(28:40):
you had folks like Cliff Asnesswho left to start EQR.
Mark Carhartt,[INAUDIBLE] these are all University
of Chicago economics PhD students,students of Eugene Fama.
You also co-authored with many ofthe people in the Goldman Quant group,
the investment classic modern investmentmanagement equilibrium approach,

(29:02):
which talks about things likeBlack-Litterman and related concepts.
And you really helped buildthat quant business at Goldman,
at its peak in the mid two thousands,
became one of the largest quantitativehedge funds in the world.
And since leaving GSAM,you also co-found Kepos Capital,
which is a quantitative assetmanagement firm that you run along with

(29:23):
Mark Carhartt,many others from the old GSAM team.
The firm now manages over $2 billion,again,
each serve as chair of the risk committee.
What do you think is the best case forsystematic quantitative investing today?
So, using factors, thinking aboutthings in expected returns and
volatilities, using portfoliooptimization tools like Black-Litterman,

(29:46):
I mean, what is the best case forthat approach today?
As opposed to, say,a more discretionary or
concentrated types of investingthat many others still use today?

>> Bob Litterman (29:59):
Yeah, well, your brief history there is very accurate.
And you're right that when Fisher and
I first started with that mean variancemodel, it was incredibly badly behaved.
And I remember going to Fisher andsaying to him,
I don't know how people can use this?

(30:19):
And he said, yeah, no,they put constraints on everything,
maximums and minimums.
And I said, well, then there's novalue added, and he said, yeah, no,
there's no value added.
And he was right, and so he suggested, whydon't we put this equilibrium in there?
It was a very academic sounding idea,

(30:43):
but it turned out to work really well.
And so we started trying to marketthe model, actually, at first.
And we discovered that there weren't toomany people out there, first of all,
who were quants, and then secondly,who actually used a model.
[LAUGH] There was a little bit of quantmarketing, but not a lot of really

(31:04):
depending on models, because people didn'ttrust him and they didn't work that well.
But I was very lucky atGoldman Sachs to come in and
have Fisher there anddevelop that model with him.
And we first startedusing it internally to

(31:27):
understand that the risksthat Goldman was taking.
And then we started using it atGoldman Sachs Asset management to build
portfolios.
In fact, Cliff Asness, who was at GoldmanSachs Asset Management before I arrived,
developed this quantitative team andstarted using Black-Litterman.

(31:52):
And he also used a computer programthat I had a hand in creating.
Called rats regressionanalysis of time series.
And he was incredibly successful the firstyear of the hedge fund he ran at GSAM.
I think it had like 80 or 90% returns.

(32:13):
And as a young quant I was like,wow, that's pretty good.

>> Jon Hartley (32:18):
Was that Global Alpha?

>> Bob Litterman (32:20):
Yeah, that was, Global Alpha it was called.
And then Cliff decided he was goingto leave and form his own firm,
which obviously AQRbecame very successful.
And I was lucky that Goldmanasked me to come in and
take over the businessthat he was running.
And that's when I saw theseincredible returns and

(32:43):
the use of Black Litterman and RATS.
And then Mark Carhartt and Ray were there.
And so it was a great time.
In fact, very lucky for me, the timing,
because that was rightbefore LTCM blew up.
And as you may know,the broker dealer side of

(33:06):
Goldman Sachs had a lot ofthe same trades on that LTCM had.
So having left the riskmanagement position right
before all of thathappened was good timing.
And then arriving in GSAM andheading this quantitative group when
markets were very disrupted alsocreated an awful lot of opportunities.

(33:31):
Volatility, which not a lot ofpeople thought of as an asset class,
but we did was trading long term.
Volatility was trading atunbelievable levels, and
you'd have to think that the marketwould be disrupted and three times
normal volatility for the next fiveyears to make these prices make sense.

(33:55):
So we decided, all right, we can do this.
Everyone was afraid of touching itbecause it had just gone up and
up and up as LTCM andothers were covering their positions and
they had shorted the volatility andnow it was just going crazy.
We came in and we started sellingvolatility at the peak and

(34:19):
it turned out to be a very,very profitable trade.
So that was lucky.
And then the markets weren'tas efficient in those days.
And so you're right,we started investing in factors and
it seemed like this justwas almost like magic.

(34:42):
You could create thesefactors that would work and
be uncorrelated with the market and
you put together a bunch of thesefactors that are uncorrelated and
you can get very, very good returns.
And there are a couple of lessonshere [LAUGH] that we discovered.

(35:06):
One of them is that you have toworry about crowded trades, and
that's kind of obvious.

>> Jon Hartley (35:12):
And I'm going to get to that in just one one second, I mean,
before we get to the quant crisis of 2007.

>> Bob Litterman (35:18):
Yeah, sure.

>> Jon Hartley (35:19):
Its so worth highlighting just how successful
the Goldman Sachs asset managementquant business was during the 2000s.
So we were talking about the late 90s,LTCM blowing up,
you coming into GSAM in 1998.
There was a stretch from roughlythat 90s period, to really 2006,
2007 or so, where Goldman Sachsasset management was running

(35:44):
the largest quantitativehedge funds in the world.
And it was so successful thatI know some of the heads of or
Mark Harhar they had special comp deals.
They were getting paid morethan the CEO of Goldman Sachs,
Lloyd Blankfein at the time,in certain years.

(36:07):
Also, some of the returns onthe global Alpha fund were,
I think there was maybe evena three digit return year.
I mean, it was so successful,you guys were pioneers in using
factors when factors maybeweren't quite being priced in,
or markets weren't quite as efficient.

(36:30):
But its amazing.
And there were these famousski trips to Colorado.
Ray and Mark and yourself were soimportant, critical to the business, that
the firm made you guys ride on differentprivate planes, because if you lost one,
it would be such a critical loss toGoldman Sachs and the firm at that time.

(36:52):
So you really can't stress enough, one,how successful these academic ideas were.
You're coming up with expectedreturns using factor models and
putting those expectedreturns into a portfolio
optimization model like Black Litterman,super successful.
And really, I think a case study of reallyjust how academic thinking can be very,

(37:17):
very useful in the real world,especially in finance.
So then we hit 2007, and forthe non quants out there,
we'll explain a little bitabout what happened there.
But for quants,the quant crisis of 2007 is a distant but
still very important memory.
And Global Alpha, one of the many quantfunds that Goldman ran at the time,

(37:42):
was caught very heavily in it.
And I think part of the explanation there,kind of what happened was
that there were a lot of crowded trades,a lot of quantum mechanism was becoming so
popular that it was causing crowdingwithin certain names or certain trades.

(38:02):
Everybody was sort of doingsimilar things with leverage.
And then what happenedwas in August of 2007,
a lot of people tried to get outof those trades very quickly.
And it sort of bears some similarities to,for example,
maybe the treasury liquiditycrisis of 2020, or
perhaps even more appropriately,that the recent yen carry trade

(38:25):
liquidity crisis that we've seenrecently in the summer of 2024 here.
I'm curious, what do you think the mainlessons of the quant crisis of 2007 are as
it applies today?

>> Bob Litterman (38:37):
Yeah, well, you've described it, right.
But I would say, first of all,avoiding crowded trades.
That's pretty obvious.
And people understand that.

>> Jon Hartley (38:50):
And you can monitor that, too, I suppose,
with things like 13 f filings andthings like that.

>> Bob Litterman (38:55):
Right. How do you identify a crowded trade?
It's not always obvious andin particular for us quants.
And really,the crisis started in quant equities,
and we had small positions inlots of hundreds of thousands,

(39:17):
really, of individual equities.
And none of those positions were largerelative to the liquidity of that stock.
And that was metric that we use.
We had limits on how biga position we could have,
such that if we traded small piecesevery day so that we didn't have

(39:39):
any significant market impact,how quickly could we get out?
And we said, we have to be able toget out within a couple of months.
Well, it turned out we were thinkingabout crowding in the wrong way.
It wasn't the individualequities that were crowded.
It was the factors.

(39:59):
That were crowded.
And what had happenedwas not obvious to us.
But as you describe it,over a period of years,
quantitative asset managershad really quite good returns.
We were one of them, butthere were many others.

(40:21):
There was now at Blackrock.
At the time it was BGI, and
then you had of course,Jim Simons and others.
We were one of many, andwe didn't see the crowding.
But what had happened is, as more and

(40:44):
more people started investingusing these approaches,
the price pressure wouldpush these stocks to the,
to line up along the quant factors.
You can imagine.
You mentioned momentum, valuation factors.
There's various factors.

>> Jon Hartley (41:04):
Carry, too.
Carry is another high interestrate currencies and borrowing,
low interest rate currencies,kind of like the dollar yen trade you
invest in US has had historicallyhigher interest rates than Japan,
which has been at 0% for a long time.

>> Bob Litterman (41:21):
It started to unwind, and when that started,
all of a sudden all of these factorsat once started to go the wrong way.
And so a lot of people tried to get out.
People called us andsaid, are you doing this?
We were like trying tofigure out who's doing this.

(41:44):
I don't know that there wasever someone who started it.
I certainly don't know.
All of a sudden, those limitsthat we had that said you can't
have too big of position weremeaningless because we were losing
five standard deviations every day forseveral days in a row.

(42:10):
We had limits on the amount of borrowingwe could do against the portfolio,
and we were coming up against that.
Avoiding crowded trades is one lesson.
And the fact that the quantsthemselves can actually cause
these prices to go beyond wherethey should be was another lesson.

(42:37):
Now, these days, I would say you talked
about the carry trade andyen and some of the other.
I guess it was treasuries thatbecame very disrupted during COVID.

(42:58):
I think systematic quantitativeinvesting is certainly evolving.
And with access to huge new data sets,
I would say that's one ofthe differences today versus a decade or
two back,is the amount of data that we have.
And then computers and computer power,machine learning, AI, machine learning.

(43:27):
In some ways I have to laugh becausewe used to use regressions, and those
quantitative tools that I was talkingabout back in the 80s were very valuable.
Well, it's really a progressionof the same idea,
except that obviously with naturallanguage processing and so
on, we've come a long way andmarkets are becoming more efficient.

(43:51):
But I think the discipline to
follow a systematic approachis always an advantage.
And with so much going on these days,
geopolitically in the monetary andfiscal policy,
that discipline is really valuable and

(44:14):
we think provides an advantagein this latest yen trade.
Managers making discretionarydecisions often are whipsawed by
the volatility in the markets.
And with systematic managers,
when you have a disciplined process,you tend not to overreact.

(44:38):
You have processes that were setup to react appropriately and
you have a much better chanceof riding out the volatility,
I would say, with portfolios andperformance intact.
That's certainly what we haveexperienced at Kepos recently.

>> Jon Hartley (44:56):
That's a great point.
When that August drawdown was going on andI think at one point
maybe the global fund lost maybe 40% orso of its market value in a day.
And it came back very quickly, likewhat we've seen with the yen USD trade,
carry trade in its subsequent reversal.

(45:16):
I know like Gary Cohn for example, becauseone concern was that what was about
solvency too, and whether or not certainbroker dealers would even take trades
from Goldman Sachs Asset Management orgold alpha fund, what was a problem?
And I know Gary Cohn famously wasbasically then the COO of Goldman
at the time, chief operating officer.

(45:39):
He was stationed out in the GCM floor fora while and
would be calling up if, say,Deutsche bank wouldn't take a trade.
He would get on the phone witha much senior person at Deutsche and
would get them to take the trade andexert that pressure.
Speaking on behalf of the entire firm,
never heard something quite like thatin terms of just how crazy that was.

(46:08):
And Goldman Sachs assetmanagement survived and
lived to tell a tale and stillrunning quantitative portfolios today.
I wanna shift the conversation a bitto climate because you've been a long
time climate policy expert anddefender of pguvian taxation,
carbon taxes as the appropriatepolicy response to climate change.

(46:30):
How worried in your mindshould we be about climate?
What do you think needs to be done?
What is the best case for, say,policy solutions to climate in your mind?
Also wondering if you could maybe explainto us a little bit about what the easy
climate carbon pricing model is.
And I think it's fair to say that carbontaxes have had a number of challenging

(46:53):
political economy issues,whether it be the yellow vest protests or
the protests in France, the unpopularityof carbon taxes in Canada,
where axioms the tax is a centralpiece of the current Canadian
conservative leaderPierre Polyev's platform.
And he's currently leading Trudeauby 20 percentage points or so

(47:15):
in the polls ahead of the 2025 election.
I'm curious, how do you see carbon taxpolicy battles going in the future,
especially when we're talking aboutsolving a challenge, which is sort
of a global one, and you have some of thelargest polluters out there like China,
that maybe don't wanna take climate policyto curb emissions all that seriously.

(47:39):
How does one get beyondthe global coordination problem?
Or could it be the case that say, varioustechnological solutions like carbon
capture or electric vehicles orcheaper wind and solar,
may play a larger role in curbingclimate emissions, say than policies?
What would you say to those sortsof arguments in making the.
The best case for a carbon tax regime?

>> Bob Litterman (48:03):
Okay, well, there's a lot there, I would say.
First of all, you're correct to startby asking about how risky is this?
How much should we be worried?
Because that's the heart of the problem.
We don't know what the futureis going to bring, and
there's many possible scenarios.
It's a risk management problem ultimately.

(48:27):
And the answer is we shouldbe very worried about
the potential impacts becausewe're making these slow but
potentially unstoppable impactsfrom warming the planet.
And the basic problem is thatwe're not pricing the risk.

(48:48):
That's the essence of it.
It's an obvious mistake.
It's a simple mistake.
It's easy to understand.
But I would say somethingthat people don't appreciate
is that it's an urgent problemwhen you're managing risk.
Time itself is a scarce resource and we'rewasting time when you're managing risk,

(49:14):
if you have enough time,you can solve almost any problem.
But it's when you run out of timethat a risk management problem can
become an unstoppable catastrophe.
Should immediately price climate risk.
I've been talking about, it's time to slamon the brakes now for almost a decade.

(49:36):
And what it means is wehave to create appropriate
incentives to reduce emissions.
It could be a carbon tax,it could be a cap and trade system,
it can be fossil fuel taxes.
There's all kinds of policies.
What most people don't understand is, Iwould say, the magnitude of this problem.

(50:01):
The International Monetary Fundhas been for many years estimating
how big is the subsidy to fossil fuelsthat's created by not imposing the tax,
the appropriate incentiveto reduce emissions?
Well, in the most recent year,they estimated it was $7 trillion.

(50:24):
Not billions, trillions.
When you look at how they did thisestimate, it's pretty rigorous,
although I would saya little bit conservative,
because at the heart of it is somethingcalled the social cost of carbon,
which is the damages,the present value of the expected marginal

(50:46):
damages that are done byemitting a ton of CO2.
They at the IMF use a social costof carbon of about $70 a ton.
Whereas the most recent work that'sbeen done by the EPA in the US,
Michael Greenstone,a University of Chicago economist,
I think probably has done the best work,

(51:08):
they're now estimating the socialcost of carbon is over $200 a ton.
And at that->> Jon Hartley: I wonder how how big those
standard error bars are on there.
Yeah, the standard error bars are huge.
No one knows what this is, but
if you come at it from a riskmanagement perspective like I do.

(51:29):
And you say, well, we think the socialcost of carbon is about $200,
but it could be anywhere from $50 to 500.
And you say,where do you want me to set it?
I say to you,
well, how confident do you want to bethat you're going to solve the problem?
And the suggestion being that you probablywant to be above the mean or the median.

(51:54):
You want to be at the upper end of thatdistribution because it's uncertain,
as you point out.
And that's the real problem,the fundamental uncertainty.
So, yeah, I got involved in this,actually,
when I was retiring from Goldman Sachs.
One of my other partners,Larry Linden, who was head of

(52:14):
the World Wildlife Fund board fora while, and resources for the future.
He invited me to lunch andwe were talking and he said,
are you interested in the environment?
And I said, well, Larry,this climate change seems to me to be
a risk management problemthat's not being addressed.
We're not pricing the risk.

(52:35):
And Larry said to me, well, Bob,a brilliant insight from an economist
like yourself, but the problem isno one knows where to price it.
And I took that as a bit of a challenge.
I thought, well, hell,I can read the literature,
you know, let's see what it says.
And so I did.
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.
Yeah, DICE model.
He and I were the same generation.
We used to both be macroeconomists.
So I know Bill, andI was reading his stuff, and
it didn't take into account what wedo in pricing risk on Wall Street,

(53:20):
where we worry about beta,the correlation of assets with the market.
And in the climate literature,they never worried about beta,
they never worried about correlation.
And they were really doing somepretty old fashioned risk analysis.

(53:41):
And so that kind of drew me in.
I started working, as you mentioned,with a couple of other economists,
Gernot Wagner and Kent Daniel.
And the three of us wrotea paper where we tried to
take seriously the uncertaintyabout climate.

(54:03):
We thought of the fragility ofthe planet as being an unknown
about which informationwould be revealed over time.
And so you form an optimal policytoday and then you revise it.
If you get good news,you can lower the price.
If you get bad news,you can raise the price.

(54:25):
And in that context,we found something very interesting,
which is that the optimal policy is toimmediately create a very strong incentive
to reduce emissions, strong enough thatyou're very confident that you're going
to solve the problem, that the emissionsare going to come down, you're not going
to cross the tipping point, you're notgoing to have any of these disasters.

(54:46):
And in fact, one of the surprises tome was how hard it was to actually get
a model where there would be catastrophes.
You were always conservative enoughthat you would never go off the cliff.
That was interesting.
And something else we did whichwas interesting is we were able to

(55:08):
ask the following question,what's the cost of delay?
Yes, the optimal policy is immediatelyprice emissions appropriately at a high
level.
But what happens if we wait a year?
And we could answer that as economists,you can set it up and say,
how much would you have to pay the currentgeneration to wait a year before they

(55:30):
start pricing carbon?
And the answer was pretty shocking andpretty incredible.
It was basically that the cost of
delay grows quadratically.
Obviously it starts at zero andthen as you move forward,

(55:51):
you're no longer pricing carbon and soyou're emitting more into the atmosphere.
Well, that has two impacts.
One is The amount of carbon inthe atmosphere is going up.
That's essentially linearly.
But the other thing that's happening isthat the damage that's created by each
ton of carbon is also going up becausethat's a function of the amount of carbon

(56:13):
in the atmosphere.
And so when you then quantifyhow much does it cost,
it's about 2% of consumptiontimes t squared.
And soone year is about 2% of consumption and
five years is like 25% times 2%.

(56:37):
So 50% of consumption.
So you can see it's just a massivecost of not pricing climate risk and.

>> Jon Hartley (56:49):
Well, but the political economy kind of challenge.
Yeah, it's a political economy challenge.

>> Bob Litterman (56:54):
And you mentioned that, you know, the politics of this are tough,
tough, and they are tough notjust in this country, but
as you point out around the world,we understand why the politics are tough.
It's because the cost is immediate andit's in your face.
Every time you fill up your car withgasoline, you experience the cost.

(57:19):
The benefit is far into the future andvery uncertain.
And so it makes it easy for opponents.
And obviously,not everyone benefits from this.
Fossil fuel interests are not going tobenefit from a rapid transition away
from fossil fuels.

(57:39):
And so this has made it verydifficult to get the politics right.
And then the other problemis it's a global problem.
And so you have a bunch of countries thatare saying, hey, you guys caused this.
The US, historically,we've been by far the biggest emitter.

(58:01):
Today, of course,China is the biggest emitter,
and China's pointing the finger at us,and we're pointing the figure at China.
And meanwhile, the emissions pricing
right now around the globe is pathetic.
The global average weestimate is about $4 a ton.

(58:22):
It should be two orders ofmagnitude higher than that.
And you've got this hurricane force wind,$7 trillion subsidizing fossil fuels,
and you're trying to movein the other direction.
It just doesn't work.
So, yeah, it's a huge problem andwe've got to address it.

(58:46):
We should have addressedit just two decades ago and
we wouldn't have this problem, but
now it's really quite late andthere's inevitable impacts now.
And the only thing is howbad are they going to be?
So we still should beslamming on the brakes.

(59:06):
I tend to be optimistic that wewill soon because it's just so
insane that we're not doing it.
And it's so simple and so obvious,we need to have a price on carbon.
And until we do,the costs are just growing
quadratically, and they're huge already.

>> Jon Hartley (59:28):
Well, it's a good question.
I know in the sense that asthese sort of changes evolve,
people also adapt as well andsay certain coastal real
estate becomes less valuable orinhabitable.
It is a slow changing process, andpeople can adapt these things.

(59:50):
And that has a pretty significant impact.
Really, this has beenan amazing conversation, Bob.
And hearing about your risk managementapproach to climate policy,
I think it's very enlightening.
And your amazing career and ideas inquantitative finance, really amazing.
You're a true pioneer inquantitative finance,
and it's a real honor to beable to interview you here.

(01:00:12):
Really want to thank you somuch for joining us.

>> Bob Litterman (01:00:14):
Well, thank you, Jon.
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