(bright music)
-[Viral Acharya]: Okay, good afternoon, everyone.
Thank you for joining in.
We are a few minutes
late but that is allowed,
a full house of 180
attendees to trickle in.
So, thank you for those
who signed in early,
for your patience.
Without much ado, let
me introduce the speaker
for our third faculty insight series.
We are very luck to have Rob Engle,
who is a Professor of Finance
at the Stern School of Business
and the 2003 Nobel laureate in economics.
Rob, in my view, at least
many of us in the department
consider him to be the father
of financial econometrics,
most importantly with
his models of volatility,
stock returns, correlations
across different markets
and virtually anything that's traded.
If you want to understand
how its prices behave,
Rob always has a new
insight and an angle on it
compared to anyone else I've met.
A genius is someone who is
good at more than one thing.
Rob is good at at least
three things I know of.
Besides econometrics, Rob is
a world champion ice skater,
which some of you might not know.
But third and the most importantly,
he is a great citizen of the school.
He first set up the Volatility Institute,
which provides daily online
updates on volatility,
correlations and other
properties of financial markets.
Now he's co-heading a
version of this institute,
that's Volatility and Risk
Institute, along with Rick Berner
and the institute is working
on really interesting themes,
such as geopolitical
risk, climate change risk,
cyber risk and other risks
that we are likely to
face in the near future.
One of Rob's views of
markets that I like a lot
is that if there is risk that the risk will change
and I think Rob is gonna tell
us a lot about that today.
Especially what we have seen
in the last month or two.
Over to you, Rob.
- [Robert Engle]: Thanks, Viral.
It's great to be here and
we're all trying to figure out
what's going on in this world around us
and it's exciting to talk about it.
I'm gonna show you some things that we see
on the VLAB website,
which is a great resource,
and then a little bit on my
speculation as to what it means.
Let me share my screen
and pull up some slides
which I'm gonna use.
So this picture is
actually a family vacation,
going into the main woods,
and it's sort of my view of
the ultimate social distancing.
We're talking about social distancing now,
we might be talking
about it forever, maybe.
I don't know what is going on.
But, anyway, so this
seemed like a nice theme
for our discussion this morning.
I'd like to start by asking
a very prosaic question.
How does this COVID-19 crisis compare
with the global financial crisis?
I mean, we've all just been through that
and obviously there are a lot of things
that we'd like to know about it
but the first thing is that obviously
they're completely different causes.
One is medical and one is financial.
And so optimal policies would have to be
very different between these two.
But both of these have major
spillovers to the real economy
and that's kind of what we're seeing.
And the stock market responds by looking
at the values of firms,
all the firms in the U.S.,
for example, or in the world,
and so if these spillovers
to the real economy
are being measured by the stock market,
then we might expect to
see a lot of similarities
in the stock market.
Furthermore, there is some
inefficiency that is associated
with a big downturn or a crisis like this.
In the global financial
crisis, we think that capital
was actually wasted by too
much building of housing
and other things that
were priced as bubbles
and this is kind of a
cost that we had to spend
some amount of time overcoming.
There doesn't seem to
be that sort of capital
expenditure in this crisis
but it's more like human capital.
Labor is being destroyed
and it's human capital
is being destroyed by
the shutting of firms
and laying off of workers and so forth.
So both of these will take
time and investment to recover.
You mentioned climate change
and I just want to make comment here
because I'm very interested
in climate change
and a lot of people are asking
what's the impact of this
pandemic on climate change.
And it seems to me, once
this pandemic is over,
we're still going to be
faced with a transition
to the low carbon economy,
so the climate problem
is still going to be here
and we're losing time working on it.
But maybe we're practicing too.
We’re learning how, the social distancing is a way of reducing
our transportation,
it's a way of reducing
our industrial emissions
and maybe we'll learn a little bit
about how to be more sustainable.
One of the key things we're
seeing is the drop in oil prices
but that's partly because
of declining demand,
as I just described,
but it's also because of
increasing supply through the competition
between Saudi Arabia and Russia
and actually with the U.S.
So the net effect of this
is that the cost advantage
of renewables is shrinking and may reduce
some of our investment in renewables.
But another side of it
is that we're gonna see
a lot of fossil fuel sector bankruptcies.
And from at least a climate point of view,
that's a transition that
probably needs to happen.
In the big bailout bill, the COVID III,
there's lots of money for companies
that are being impacted by this
and many of these are in the
fossil fuel energy areas.
So I think bailing these companies out
is actually a missed opportunity
to invest in climate change.
And, finally, we notice that this pandemic
is caused by what people
call a zoonotic virus.
That is a virus that comes
from animals jumping to people.
With global warming, there is a view
that I've heard several scientists say
that we're going to be interacting
with other kinds of animals
from what we usually see
because animals are gonna
be migrating northward.
And so there are reasons to suspect
there may be other pandemics
and wonder is this just a dress rehearsal
for the kinds of things that will happen
when climate change is upon us.
Okay, so let's turn to
looking at some of the details
of what we see when we
look at the pandemic.
I like to show the map of the world
that is on the opening page of VLAB.
VLAB is this website and
this is its address up here,
which is sponsored by the
Volatility and Risk Institute
and it's updated every day
and it plots volatility
forecasts for each country
and color codes them, green if it's low
and red if it's high.
And this is what we saw
at the end of January.
This is what I showed my class
the last day of winter quarter.
It turns that just a month
later, it became a lot more red
and then today it's a lot more red.
So we're seeing high volatility
almost everywhere in the world.
And about the only
exceptions are a few places
in Africa and Mongolia and so forth.
China actually is not as red
as all the rest of the world
but we're seeing extremely
high volatility, period.
One of the additions to the VLAB website
is superimposing on this
the cases of coronavirus
and these are the circles
which are shown on this map.
And if you go to the website here,
which is going to be linked to VLAB
but at the moment you
just have to know to put
a slash covid19 here and it
will take you to this website.
And you can go through dates
and see what this looks like.
And, on the next slide, I'm
gonna show a very fast movie
of what it looks like.
This is starting January 22nd.
Oops.
And, as you can see,
over time the cases grow
and the colors turn red.
So there is some relationship
between cases and...
Let me do that again, so
you can really see that.
So I encourage you to do that
because you can do that for
different parts of the world
and really get sort of a
picture of how the infection
has grown and how the volatility goes up.
And this gets updated every day.
You can see this is
going through April 6th,
which was yesterday.
So, I'm gonna talk a
little more about what
this relationship really
looks like, shortly.
So let's just look at a couple of pieces
of information about the volatility.
Here's the Standard and Poor's 500.
It has a volatility
which is typically low,
between 15 and 20%, but here on VLAB
it's jumped up to more than
100% in the middle of March.
It's since come down
and it's now about 65%
and that was on the weekend,
yet today it's about 63
or something like that.
The implied volatility
is a little bit lower,
this is the VIX in blue
and these are both ways
of measuring volatility.
You can see it corresponds
to a dramatic decline
in the price of the S&P
on the green curve at the bottom here.
Well, one of the things to notice here
is that the VIX is
actually below the GARCH,
where, in fact, normally
the VIX is above the GARCH.
And an explanation for that
is partly that it's possible
that the VIX has more information
in it than the GARCH does
because this is a forward looking measure
by traders who are paying
attention to what's going on.
And it may be that they foresaw
some of this bailout bill
that's been this giant 2.2
trillion dollar bailout bill,
Corona III, that was signed
at the end of the month.
And so that might be why
it's a little bit lower.
It might also be that traders
haven't got a good view
as to what's going on,
or that the GARCH model
is not very good at
these kinds of extremes.
All these things are possible.
So if you look at this graph,
it shows the volatility not
just over the last two years,
which is what I just showed you,
but over the last 80 or
90 or something like that,
going back to the 30s.
And you see we have a peak
today which is over 100%,
which I just showed you,
which is actually higher
than we had during the
great financial crisis.
So the volatility today of the S&P
is really above what it was
in the financial crisis.
It's comparable to the
highest day during the 30s,
which was actually in 1929.
In November of 1929, volatility hit 117%
using the same model through
that whole sample period.
So we're seeing something
which is comparable
to the Great Depression.
The other event which is
dramatic on here, of course,
is the October 87 crash,
which is higher than any of these points,
in terms of the volatility,
but it was also very short lived.
And we don't know, of course, yet whether
this is gonna be short lived or not.
That's one of the questions
we're trying to answer.
If you look at quarterly
or at least 66 day returns,
you can see that there
are a lot of negatives
but the negative down here
is that in the first quarter
the S&P has lost about 30%.
During the financial crisis,
it lost more than that.
It was more than 40%.
And during the Great Depression
it was more than that.
So the decline has not been as great
as what we saw in the financial crisis.
And, if you look at the
drawdown from the peak,
you'll see that the drawdown
in the financial crisis
was about 55% from peak to trough.
In the internet bubble it
was about 40, you know, 45 or 48%
or something like that.
And thus far today, the
drawdown has only been
something like in the low 30s, 33%.
So, if we're gonna get
something which is as severe
as the great financial crisis,
we've got a ways to go.
We don't know whether
we've seen the bottom yet.
The market yesterday
seemed pretty optimistic.
I don't know whether we've
seen the bottom or not.
That's obviously a question
that we'd all love to know the answer to.
So, how bad can it get?
Well, once you have a volatility model,
you have a model which tells you how fast
a volatility can change.
So if you think that what's
gonna happen over time
is that you will get a string of bad news,
so that the volatility can change,
a volatility model can tell
you how bad can it get.
So, for example, if we take this model
with the high volatility we see today
and simulate it out for a
year, thousands of times
using actual bootstrap
residuals from the data,
and sometimes it will have a
whole series of bad events.
See, what are the quantiles
of that distribution?
This is like the way you
calculate value at risk
but it's calculating value at risk
a month or a year in the future
and this incorporates the
skewness and the kurtosis
of the volatility models
and the mean revision
of the volatility models.
And we do this every day on VLAB
and you can see this sort of thing.
It says that today there's a
1% chance that in a month
the stock market will
be down by another 50%.
There is a 1% chance that in a year
it'll be down maybe another 80%.
This is not a prediction,
this is a 1% chance,
one time in a hundred
it would be that bad.
So this gives you a way of thinking about
what are the worst case scenarios
from this kind of a model.
Okay, let's just look at
a couple other assets.
Crude oil volatility has risen up to 160%.
That's even more volatile than the S&P.
And it's come down a little bit here,
it's now like 130, but it's very high.
If you look across
assets across countries,
you see high volatility in
many different countries.
The U.S. is sort of in
the middle of this list
of high volatility places.
Brazil is the worst.
If you look at sectors, you see energy
has the highest volatility,
followed by financials.
If you look at the exchange rates,
the Russian ruble is the most volatile.
Going down, Mexico,
Norway, the rand, Columbia.
And if you look at commodities,
you see an interesting picture.
The highest volatility
commodity is unleaded gasoline
and this has a volatility well over 100%.
Crude oil was high.
Crude, petroleum, Brent Crude, palladium,
energy and metals, heating oil,
these are almost all the oil sector.
So we're seeing a very high volatility
throughout this range of commodities.
Finally, what about treasuries?
We have a flight to quality
that is what we expect,
so that you see actually the prices
of 10 year treasuries have risen
since the pandemic broke out
and the volatility has gone up.
The volatility is now as high as 16%,
which is high for a bond.
It's low for a stock but
it's high for a bond.
And so we think that
there is some possibility
that with this high a volatility,
bonds will have a change in value
which is fairly substantial.
You see here they could
go down by as much as 7%
over the next month or
maybe 10% over the year.
Well, that's not so much.
So that's one reason why
bonds look like a safe haven.
They're not so volatile and
they don't move so much.
They don't have such long tails.
How about correlations?
Correlations have risen
between commodities.
This is the average correlation among
all pairs of commodities
and this is a little picture
which shows what the correlation
structure looks like today
but these things have risen.
If you look at equity sectors,
these are the different
sectors of the global economy,
or I guess they're a U.S. economy,
you can see that that
correlation is much higher
than it is between commodities
but it has also risen dramatically
over the last couple weeks.
If you look at international equities,
their correlations have also risen.
So what we're seeing is
that all these asset classes
tend to be moving together.
But here's one that's not and
this is worth thinking about.
This is exchange rates.
Exchange rate average
correlations have gone down.
They're actually fairly
low but they've gone down
and, what that means, is
that not all these countries
are moving in the same direction.
But, of course, remember
that exchange rate
is a relative price,
so we're asking whether
they're all moving in the same direction
relative to the dollar and the answer
is no they're not, they're
moving in different directions.
There are some winners and some losers
and this shows up in their exchange rates.
We might be worried about systemic risk.
We have a lot of measures of systemic risk
and these are based on
something we call SRISK,
which is the amount of
capital a financial firm
would need to raise in order
to continue doing business
if there is a 40% drop in
the global equity market
over the next six months.
A lot of people think of
this as what it would cost
to bail out the financial sector
if we have a big drop in equities.
Well, we haven't had a 40% drop yet
but we're some substantial way there
and SRISK is the question
of what would it cost
if we have a 40% drop from here.
And, if you look at, this
is a plot of the world,
what you see is there were peaks
during the financial crisis,
the European sovereign debt crisis,
a peak which I think had to
do with China's slowdown.
And now we get this
other peak right up here,
you see this thing shooting
up in the air here?
It says we're now up
to about five trillion
it would take to bail out everything.
Now that actually, China has a big piece of that
and so if you look at the
same picture without China,
you see the same peak
up here but it's only
four trillion dollars
because actually China
has an SRISK of about a
trillion dollars itself
and, as a state owned
sector, it might be that
that's not going to end up
needing to be bailed out.
So where does this peak come from?
Well part of it's in the U.S.
And you can see here in the U.S.
we've got this very strong rise
in the last couple of weeks.
The background is what
we think the capacity
is of the U.S., how much SRISK we can take
without it turning into a crisis
and we're actually just
crossing this threshold today,
which is worrying.
If you look at Europe, you
can see it's not as big
a increase and that might be good news.
But maybe not either.
Why not?
Well, if you look at European banks,
the highest is BNP Paribas
and Credit Agricole,
Societe Generale, HSBC.
One of the things about these banks
is they have incredible leverage.
Look at the last column here.
This is the ratio of debt
to equity of these banks.
This is accounting debt to
the market value of equity.
This is over a hundred
for Credit Agricole,
which means that it's got
a hundred times more debt
than the value of its equity.
That tells you that a tiny variation
in the fluctuation in the
assets of this company
could bring it to bankruptcy.
This is the result of stock market values
falling dramatically,
to very close to zero,
and the reason these
banks are not bankrupt
is because there is a
very strong presumption
that the government would step in
and rescue them if necessary.
And when Lehman failed,
it had a leverage ratio
of about 50 to one and when you see banks
with a ratio of a hundred to one,
it suggests that they're highly risky
and must have implicit
guarantees from the government.
Consequently, the increase
in SRISK is minimal
due to the financial crisis,
they were already pretty much maxed out.
In the U.S., you see the leverage,
oh, this doesn't show leverage
but leverage is much lower.
But what's dramatic is what's happened
over the last quarter.
Since 12-31 'til the third of April,
SRISK went up for Citigroup
by 40 billion dollars,
for Wells Fargo by 67 billion,
for Bank of America by 73 billion.
JP Morgan went by 84 billion.
So these are enormous numbers
and they're primarily due
to the fall in the market
cap of these companies.
So this dramatic increase in the SRISK
is a result of falling
values for these companies.
Why is that?
Well, probably because their
assets are falling in value,
some of these due to the oil price shock
because they have a lot
of paper from fracking
and other kinds of fossil fuel energies
but also just from their loans
to the general real economy.
So what explains all this volatility?
Well, it's the virus and I'm
gonna show you some results
which are very preliminary
and I really wanna appreciate
Sila Alan and Ahmet Karagozoglu,
who have done lots to
help with this analysis.
If you look at the logarithm
of the cases of COVID-19
for eight countries,
you see a sort of a nice,
interesting picture.
You see sort of a linear section here
which is the exponential growth part.
You see some curvature in
some of these countries,
particularly Korea and now Italy,
which suggests they're reaching a peak
and China which has been
concave for quite a while.
So it looks like there's three regimes
with kind of a slow growth
period at the beginning,
then it accelerates and then
it gets a cap on the top.
So, if you think about
trying to build a model,
you might be interested in
trying to identify these regimes
and figure out what variables
would be useful for predicting them.
Well, it looked easier
with eight countries
than it does when you look at a bunch more
and we've got 80 countries
that we're following on VLAB
for which there are case data.
And so the problem is,
when do switch from regime to another?
What observables to agents
see that help them forecast
where this is really gonna go.
And I think we hear
nightly from the governor
about whether this thing is
starting to taper off or not.
How much curvature is
there in these curves?
So here is the dependent variable,
this is the change in
volatility from January
for each of these seven countries
and you can see it really
went up for all of them
but not the same amount.
And if you look at a
plot of the log of cases
against DVOL, you see it is
more or less positively sloped.
The more cases there
are, the more volatility
has gone up across countries.
And if you look at the returns,
you see all these markets
have shown dramatic falls
in the stock market prices,
although there is definitely
some recovery showing up here,
starting something like last week.
And if you plot, again,
cases against the returns,
you see more or less a
negative picture here.
So what this preliminary
model is gonna do,
it's gonna calculate the
logarithm of the confirmed cases.
The infection rate, which
is the rate of growth
of the number of cases, the log of cases,
and we use a lag of 14 day
because that's more or less
what the quarantine period is for COVID
and we're gonna use a curvature variable,
which is the cases of time T,
the cases 14 days ago minus
twice the cases halfway in between.
So that might pick up when it changes
from being convex to concave.
We're gonna regress those on the DVOL
and put in time fixed
effects and some spillovers.
So here's a model to look at,
which is the dependent
variable is the volatility.
Here we see log cases
is quite significant,
the infection rate is significant
and the curvature is significant,
so all three of these
variables are significant.
We get a little bit from
the U.S. infection rate,
which says that is also significant.
There are no time dummies in this model.
If you put in the global
effect, it doesn't work as well
and you get what might
appear to be the wrong sign
on the global infection rate.
Or you can just put in time dummies
and just deal with the
cases, in which case
the case variable is the
most significant one.
If you do the same thing for returns,
you see something sort of similar.
The log of cases is the
most important variable
and the USA infection
rate is also important.
If you try to put more variables in,
most of the things that we've tried,
like population and other
things, don't seem very useful.
If you put in the number of
days since the first case,
you see that the longer
this has been going on,
the lower the volatility
and you also see that the
higher the stringency index,
the higher the volatility.
Now stringency is a measure
that a group at Oxford
has put together which summarizes
whether schools are closed,
social distancing is imposed,
what part of the country that's on.
So they have done a lot
of work to try to get this
but its really sort of
an equal weighted index
of a lot of things and
it's really hard to know
whether it's the right measure or not
but it does seem to come in as a positive
explanatory variable here,
which means that the more
stringent the economy is,
the higher the volatility
of the stock market.
This might be the opposite
of what you would expect
but it is conditional on cases.
So holding case is fixed,
if you have more stringency,
you might expect that the
stock market volatility
would be higher.
Okay, so what do we make of this?
If this is a stable and
structural relationship,
which is a big if and I
emphasize how preliminary this is
and there are reasons to be worried about
whether this thing really is a stable
or structural relationship, then, anyway,
policies to stabilize the infection,
rate of infection would actually
improve the stock market.
It would put upward pressure on the level
of the stock market and downward pressure
on the volatility of it.
That's because cases turn out to be
the most important explanatory variable.
Interestingly, the more
stringency there is,
the higher the volatility,
but it turns out that
stringency only goes up
when there are a lot of cases.
So maybe it's actually
just another measure
of how severe the crisis is and therefore
has the opposite sign from
what you might expect.
So what are the conclusions from all this?
Well, I think volatility measures
are as high as during the Great Depression
and higher than the
global financial crisis.
Pretty shocking.
The drawdown has not been as great
as either of these so far.
Correlations are rising,
systemic risk is rising
and the financial impact is found
to be correlated with a
log of confirmed cases,
so really what we said
at the very beginning,
if public policy can keep the cases down,
the stock market may
respond in exactly the way
we would hope it to.
And then, once that's done,
we can go back to work
on the climate change.
So two of my grandsons worrying
about what's in the future
and whether it's pandemics or
climate change, we don't know.
I can also share with you
that as we were getting ready
to go today, my son and his
wife headed to the hospital
to deliver twin boys.
So there are gonna be two more grandsons
in this family pretty soon, so thank you.
- That's great.
Thank you so much, Rob.
That was a terrific wealth
of information analysis
that you have shown all of us.
If I could push you a little bit, Rob,
on telling us more on
the climate change issue.
So clearly it's something
that's been rising
on the data of risks
that we are now beginning
to put on our dashboards and so on.
Now, of course, there is one policy angle
which is what to do about it
but there's sort of from a
financial investor standpoint,
there's also this question
of how do you deal with this risk?
So I wanted to ask you
if you've done some work
on thinking about strategies or assets
that might help hedge climate change risk
and, if so, what have you
found so far on this damage?
- Yes, there is a whole lot interest
in investing sustainably and
we have a formulation of this
which I think is potentially
what investors have in mind,
is that they would like
to invest in a portfolio
that will do fairly well,
maybe better than the market as a whole
if the climate is really deteriorating.
And so whenever we have
news about the climate
getting worse, investors, if
they have this point of view,
would end up buying some
hedges against climate change.
And so what should they buy?
Well, I think there are lots
and lots of private offerings
but you would expect that if there's news
that the climate's getting worse,
that people would be selling
their fossil fuel energy stock
and buying maybe broad based
indices in tech and finance
and companies that you
think are going to actually
do pretty well and maybe
you would buy renewables
but the market is so small
in renewables that you can't
really do that trade in
a very large quantity.
But, in any case, we've done a lot of work
trying to figure out what
these kinds of portfolios
might look like and, today,
some of these portfolios
are actually doing extremely well
because really not for the
main reason you would expect
but they're doing well
because the oil price shock
means that the whole fossil
fuel energy is doing badly.
And so if you've shorted
that in your portfolio,
you're actually have some bright light
in the middle of a bad
portfolio performance.
But, and as I said before,
if we really succeed
in bailing them out and making them whole,
then that benefit will be gone.
It will be paid for out of
the state and our tax payers.
- There's a related
observation and a question,
Rob, from Jay Freeman.
He's referring to the
reinvesting in traditional energy
right now being referred
to as a missed opportunity
in some of your early remarks.
And he's raising the question as to
one thing we have to
worry about is whether
the renewable smart energy
projects are readily available.
Do we have staff properly trained
in the same amount of
time that it would take
to deal with the bankruptcies
of some of the traditional
energy projects.
I wonder if you have any thoughts
on Jay Freeman's question.
- Well, I'm not sure I can
give you firsthand information
but the growth in renewables
has been very rapid and enormous.
There are large wind and solar projects
in all stages of development
but I think, at this moment,
everybody is sort of
reeling from the pandemic
and so my anecdotal evidence
that I've read about
sort of suggests that
some of these projects
have been slowed down and investment money
has been slowed down by the pandemic.
So maybe there are opportunities there
and I just am not sure quite
how you capitalize on them.
- Thank you, Rob.
One of the interesting
observation in this line
by Philip Wolfers, he is
concerned that the lack
of capital and poor performance
that you've just referred to
might in fact slow down
the ESG related initiatives
in the corporations further
down their priority list.
He's referring to short term
survival taking precedence
over dealing with the stress
test of a global shutdown
because we have some other
emerging climate risks
down the line.
On the one hand it suggests we might see
even further underinvestments
in dealing with it.
I wonder on the other
hand whether it means
that there is in fact a greater role
for some sort of incentivization
of these sorts of measures.
You referred to a carbon
tax in the slide, Rob,
so maybe you can tell us a
little bit more about this.
(laughs)
- Well, I mean, I think
that one of the things
that has slowed down
the transition to carbon
in the U.S. is the fact that
people don't really think
the costs of climate change
are gonna be that great.
I mean there are some
people that are gonna bear
a lot of costs, who
have coastal properties
or properties in wildfire
areas or something like that
but that's not me, that's somebody else.
And so one of the things
about this pandemic
is if you think of the pandemic
as part of the cost of climate change,
which I don't know whether
that's gonna turn out
to be scientifically valid
but certainly one of the things
people are concerned about
is tropical diseases like
malaria and dengue fever
and so forth that are borne by mosquitoes
but we haven't talked about things
that are borne by bats or pangolins
or any kind of animals that
might be unfamiliar to us
but might appear through
this climate migrate.
Anyway, if those kind of zoonotic viruses
are gonna be more common,
then it's pretty easy
to say we wanna do whatever we can
to avoid having repeated
episodes like this.
And one of the things to
do is just to encourage
the transition to renewables,
which I think people
overestimate how
expensive that's gonna be.
There will be some losers and some winners
but basically, as soon as you
change the relative prices,
I think all these clever
fossil fuel people
that have been pouring
into fracking businesses
will figure out exactly
how to get into the wind
and solar business and the
distribution of it and so forth.
So I think our economy is
pretty flexible and willing
to invest when it looks
like the profits are there.
- Thank you, Rob.
Let me turn little bit to
some of the volatility metrics
that you showed us.
How much of the oil price volatility
that we've observed is
sort of extraordinary,
I think right now around
120% or something like that,
as per your estimate.
How much of this would be consistent
with just a GARCH model in
which point prices have crashed,
versus the additional policy uncertainty
that we have seen come to the table
because of the OPEC lack of agreement
and the constant back and forth
as to whether they are
likely to engage in cuts
or not with the U.S.
pressure on them to do so.
Is there any way to isolate
more what looks consistent
with the crash of oil
prices versus an additional
wave of uncertainty due
to policy uncertainty
that's been released into the mix?
- Well, I think that what we're seeing
is some sort of a tail event.
We're seeing things that
you would not predict
with a GARCH model but if
you simulate a GARCH model,
then you'll get events which
are this extreme sometimes
and that's kind of what I was showing
with the long-run value of risk.
And so I think if you were
to back out the residuals,
what you would see is a bunch
of negative residuals in a row
and that by chance is unlikely.
But that's what it means
to be a tail event.
And so I think what we're
seeing is that the economics
play out by having these negative
shocks to the oil business
day after day after day, as people observe
that both demand is down and supply is up
and negotiations are failing
and the frackers don't want
to join the negotiations
and each of these is an event
which has a negative
shock to the oil industry.
So they accumulate into
a really high volatility
and a big price decline.
- Thank you, Rob.
Here's an interesting question
from Mayor Richard Thomas
and I wonder if it has something
to do with the last results
you showed us on the
link between volatility
and growth in the number of cases.
Is there some way in which
the models you've shown us
can be used to gauge the right
time to restart the economy
in terms of moving from social distancing
and lockdown to opening up?
- Well, I would like to think
that there is something there.
What I'm modeling is not
the infection rate, however.
It's the result of the infection rate
on the financial economy.
I mean I could also build a
model on the infection rate
because it's the same data.
So that's a possibility.
But, of course, what we need to know
is how these policies affect this model.
And so it's not really
just a time series model.
If it's a time series model,
then I guess when the curvature is such
that you've reached the peak,
then I'm a little concerned
that that doesn't tell you
whether you can reduce your
social distancing pressure
or not, it just says it's working
but it doesn't tell you what
would happen if you stop it.
- I see.
Rob, one uncertainty that is on the minds
of a lot of investors,
modelers, and the common man
is even if we did actually get to a point
where we thought the curve was flattening
and we relaxed some of
the stringency measures,
could there be repeat
waves of the outbreak?
Is there some sense in
which we could estimate
likelihoods of these
using the sorts of models
that you just mentioned
and, fundamentally,
would these probabilities
have gone up in your models
compared to say the history
until December of last year?
- Well, most people
think that the rebounding
of the virus has to do with
the amount of the population
that's uninfected and how susceptible
they are to being infected.
So if I thought that by putting population
into these models, I'd be
able to see when the infections, the cases
were an appreciable
part of the population,
you'd see this curvature affect rising.
But at least in both the U.S. and China,
which are enormous countries,
the percentages of uninfected
people remain very high.
So it's not really that
the hope is that everybody
is gonna already be infected enough
so that they have antibodies
and the spread can't really happen.
So I don't know whether
that's the mechanism
that is leading to this
curvature that leads to a peak.
I think it's more likely
that the mechanism
is that we've shut down enough business
and done enough social distancing
that people are not becoming
infected at the same rate
but it's not that we're
running out of people,
if you know what I mean.
And so I think that
reducing social distancing,
I don't see how these models
are going to be able to
answer that question.
- Rob, we are close to the finish line
but we have time for two more questions.
I'll tell you both the questions
and maybe you can use two to three minutes
that we have left to get at them.
One question is, as a part of this,
let's use the term building immunity
against economic fallout
of these sorts of shocks,
do you see that supply chain management
may have to alter in corporations
to deal with the fact
that maybe we need to
build in some resilience.
So does it have to move away, for example,
in pharma sector, in terms of
imports from other countries?
And the last question,
which I have saved 'til now,
is if you were to talk to the president,
what would be the one fiscal policy
suggestion that you would suggest?
(laughs)
- Well, I think people who
do worry about supply chains
have a lot to think about.
I think this really will change
the way supply chains are organized.
We've moved to shorter
and shorter supply chains
because the response rate is so quick.
And now, if it turns
out that it's variable,
then I think people are
gonna have to do some
more inventory management.
But maybe there are other choices too.
I mean there may be
diversification of the supply chain
which would be an alternative
to just more inventories.
So I'm not quite sure
and I don't know where
the inventories would be,
whether they would be in primary
production of in finished production
and what's the most efficient
way to hold such inventories.
I'm not completely sure
that the supply chains
have broken down.
I'm kind of amazed at
how good they've been.
I mean, although we ran
out of toilet paper,
most everything seems to be in the stores,
at least where I am in California.
And that's without anybody on the streets.
I mean the only people
around are the truck drivers
and they're delivering stuff.
And so I am not sure
exactly what implications
are gonna be drawn about
changes in supply chains.
But I imagine there will
be some updating of that.
So what would I tell the president?
Well, I mean I think the
president should own this crisis
and he should be a commander in chief
and organize both the social
and economic response.
I think we've made a very
large economic response
but I think it's primarily
come from congress.
I don't think that the White House
has done too much on this.
But I'm sorry that the
bill does not tilt toward
renewables in energy and
allowing some fossil fuel firms
to fail without the support.
I think it's kind of
interesting to think about
the lending program which requires firms,
especially small firms,
to maintain their staff
in order to have the loan forgiven.
I think there's gonna be some interesting
cost benefit analysis of that
when workers have a choice
between switching jobs
and moving to becoming
an Amazon truck driver
or unemployed versus staying
as an at-home restaurant employee.
I don't know from a social point of view
how that actually adds up.
I think it's good for
getting cash to individuals
and I think maybe that's
the main reason for doing it
but there may be a economic
cost that come from that.
- Thank you so much, Rob, for
this terrific set of slides
on how financial metrics are panning out
since the pandemic has
hit the global economy.
Thank you also for the
stimulating conversations,
especially on climate change,
how we might have to deal
with it going forward
and some of the cost benefit analysis
of the recent programs
that have been introduced.
Thank you to all the participants as well.
I think we can all take a
round of applause for Rob.
Clap or knock on the desks
at your respective ends.
And, just to remind you,
we have a few more faculty
and forum insights in the coming weeks.
Stay tuned, we'll be with you very soon.
Thank you, everyone.
- Thanks, Viral.
Thank you, everybody.
(bright music)
