►
From YouTube: Governance and Risk Meeting: Ep. 41
Description
The theme for this call will be 'Collateral Risk'
Governance Segment
- Richard Brown: I have nothing to talk about. Let's focus on Risk.
Risk Segment
Monetary Policy: Vishesh Choudry
Collateral Risk: Cyrus Younessi - ''Quantitative Modeling: The Single CDP Credit Risk Model'' presentation
Weekly Narrative: Matthew V Rabinowitz
We'll then spend the rest of the time in a general Q&A session.
Discussion Thread: https://redd.it/c61gk7
Podcast version: https://soundcloud.com/makerdao/sets/governance-and-risk
Summaries: https://community-development.makerdao.com/governance/governance-and-risk-meetings/summaries
Transcripts (Delayed by 48 hours) : https://community-development.makerdao.com/governance/governance-and-risk-meetings/transcripts
Foundation Proposal: https://medium.com/makerdao/foundation-proposal-v2-f10d8ee5fe8c
Governance and Risk Framework: https://community-development.makerdao.com/governance/governance-and-risk-framework/part-one
Website: https://makerdao.com
Twitter: https://twitter.com/makerdao
Reddit: https://www.reddit.com/r/MakerDAO/
Chat: https://chat.makerdao.com/home
Email: info@makerdao.com
A
Okay,
welcome
everybody
to
the
June
27th
edition
of
the
scientific
governance
and
risk
meeting
at
maker
Dow.
My
name
is
Richard
Brown
I'm,
the
head
of
Community
development's
I,
have
a
few
things
that
I
like
to
say
at
the
top
of
each
a
call
and
I'm
going
to
continue
to
say
them.
I've
posted
a
link
in
the
group
chats
it
has
it's
a
link
to
our
discussion
thread.
So
every
week
we
have
these
calls.
These
calls
lasts
for
an
hour
an
hour
and
a
half.
A
We
talked
about
a
lot
of
really
interesting
things
too
many
interesting
things
to
actually
dig
into
each
one
of
them.
So
we
continue
these
discussions
in
our
subreddit
and
we
we
also
post
summaries
of
the
calls
there
for
people
who
weren't
able
to
attend,
which
are
wonderfully
detailed
and
recently
have
included
pictures
as
well.
A
So
if
you
want
to
get
caught
up,
if
you
want
people
to
get
caught
up
with
the
things
that
you
said
and
asked
follow
that
thread,
because
we
for
some
reason
the
conversation
continues
in
these
calls,
particularly
though
we
have
tons
and
tons
and
tons
to
go
over,
but
we're
also
extremely
interested
in
hearing
from
the
community
and
the
voters.
So
if
you
have
a
question
that
you
want
to
have
answered,
please
type
it
in
the
chat
at
the
side.
A
If
you
have
access
to
a
microphone,
please
just
jump
in
and
ask
questions
whenever
they
occur
to
you,
we're
pretty
good
about
handling
interruptions
if
it
turns
out
that
were
in
the
middle
it'll
flow,
though
we'll
circle
back
afterwards,
but
we
love
the
back-and-forth.
So
please
speak
up.
If
you
have
something
to
say
traditionally
in
these
calls,
I
spend
the
first.
What
I
usually
claim
to
be
five
minutes
talking
about
governments,
governance,
it
rarely
ends
up
being
that
efficient.
A
Happily,
today,
though,
everybody
spared
that,
because
I've
been
racking
my
brains
to
think
about
what
I
can
talk
about
and
I
probably
shouldn't
be
looking
for
reasons
to
waste
time.
In
these
calls,
so
here's
the
short
answer-
governance.
It
seems
to
be
working
the
way
it's
supposed
to
which
is
cool.
We
for
the
last
three
or
four
weeks
the
voters
have
signaled
for
no
change.
The
stability
fee
seems
to
be
doing
what
it's
supposed
to
do.
B
C
All
right
he's
everything
all
right.
So
all
in
all,
it's
been
a
pretty
interesting
week,
I
think
just
to
touch
on
the
state
of
the
peg
real,
quick.
It's
been
fairly
steady,
there's
been
kind
of
a
slight
drift
and
a
slight
increase
in
invariance
over
the
past
month
or
so,
but
nothing
to
be
worried
about
and
I
think
this
was
part
of
the
discussion
around.
C
The
time
cutoff
was
at
around
14
million.
Since
then,
the
trading
volume
has
come
up
to
about
16.
Almost
17
million
and
dye
price
is
actually
slightly
higher
than
this
now
so
I'll
just
show
that
real
quick,
so
it's
been,
there's
been
a
lot
of
trading
activity.
A
lot
of
I
think
good
trading
activity
for
dye
I
think
a
fair
amount
of
selling
eath
for
dye.
C
So
what's
interesting
is
there's
been
some
transaction
activity
in
both
directions
as
what
I'm
getting
at,
but
dye
price
has
been
fairly
strong
throughout
that
so
I
think
we're
gonna
continue
to
see
this
dyes
not
as
much
of
a
glass
draw
as
it
had
been
in
the
past,
and
so
even
in
the
the
large
selling
periods.
The
dye
price
doesn't
shoot
up
quite
as
dramatically
as
it
had
in
the
past
and
then
in
those
selling
periods.
C
Think
indicative
of
the
large
amount
of
trading
volume
in
the
sense
that
when
you
have
those
huge
spikes
in
trading
volume,
there's
some
slippage
on
protocols
like
you
know,
swap
there's.
You
know
a
lot
more
depth
to
the
order
book.
So
the
price
moves
in
more
granular,
increments
and
so
I
think
you
tend
to
see
this.
This
graph
kind
of
fill
out
a
little
bit.
C
So
that's
been
pretty
interesting
to
watch.
You
can
see
there's
at
least
in
the
past
24
hours,
a
larger
section.
That's
been
trading
above
it
all
over
than
below
$1,
but
I.
Think
that's
just
because,
given
all
the
pressures,
there
was
more
eath
selling
than
an
eighth
for
die
selling
than
anything
else,
so
that
helps
to
push
it
up,
but
it'll
have
a
pull
out.
I'm
guessing
so
yeah
I
mean
I.
Think
this
this
graph,
there's
not
much
to
say
the
eath
price,
has
been
consistently
rising.
C
C
What's
interesting
is
generally
when
there's
that
profit
booking
happening
you'll
see
have
a
commensurate
decrease
in
dye
supply,
or
at
least
you
know,
commensurate
rise
in
collateralization
ratio,
because
people
get
scared
of
those
liquidations
and
they
punch
in
a
little
bit
more
collateral,
but
also
the
price
went
down
so
in
nominal
terms,
all
the
collateral
decreasing
value
as
well,
so
those
those
kind
of
bounce
out
we're
in
the
extremely
short
term.
You
would
see
a
bit
of
a
drop
in
collateralization
and
then
you
will
see
it
rise
up
as
kind
of
a
counter
effect.
C
So
about
five
days
ago
there
was,
you
know
two
and
a
half
million
that
was
burned
and
two
million.
That
was
minted.
Then
the
next
day
there
was,
you
know,
a
million
that
was
minted
and
you
know
relatively
little
burned
and
then
in
the
past,
24
hours
about
three
million
plus
men
and
then
about
two
million
wiped.
So
you
know,
even
though
net
the
supply
has
not
changed
dramatically
quickly.
It
is
a
consistent
increase.
There's
been
a
message:
yeah
I
have.
D
A
I
have
a
quick
question
as
to
something
that
I
think
would
be
an
interesting
indicator.
So,
whenever
I
see
like
large
wipes,
I
always
go
on
dice,
Nats,
calm
and
check
out
the
the
burner
and
I
know
that
if
the
burner
didn't
increase
by
a
lot
of
mkr
that,
like
huge
amount
of
wipe,
that
is
like
very
short-term.
Do
you
think
that
that's
something
that's
like
useful
in
any
way.
C
Yes,
and-
and
maybe
I-
can
give
you
a
preview
something
interesting
right
at
the
end
here,
so
the
in
terms
of
the
fees
that
are
accrued
and
and
I
think
that
that'll
help
illuminate
sort
of
that
question
around
accrued
fees
versus
short-term
debt,
getting
paid
back,
but
actually
we'll
touch
on
that
just
now,
so
the
age
of
the
debt
that
is
getting
paid
back.
That
has
been
consistently
cratering.
Since
you
know,
just
just
after
the
nineteen
point,
five
percent
increase.
C
What
we
saw
was
the
age
of
open
debt
was
consistently
decreasing,
so
a
lot
of
young,
a
lot
of
new
debt
being
created
and
a
fair
amount
of
old
debt
being
paid
back
that
had
slowed.
As
of
the
sixteen
point,
five
percent
increase,
but
with
the
past
few
days
what's
been
going
on
with
eath
Christ
there's
been
a
lot
there.
There
hasn't
been
that
much
more
old
debt
being
paid
back,
but
there
has
been
a
fair
amount
of
new
debt
being
created.
So
you
know
that
does
kind
of
actually
touch
on
your
question.
D
D
C
So
open
open
die
days
is
on
average.
The
denominator
is
the
amount
of
open
debt
and
denominator
for
closed
died,
ASIS
the
total
amount
of
closed
debt,
which
includes
both
debt
repaid
and
debt
liquidated,
and
then
the
the
numerator.
So
is
just
the
total
number
of
days
that
the
open
the
debt
that
is
currently
open
has
been
open
and
then
for
the
closed.
It
was
by
the
time
it
was
paid
back
or
liquidated.
How
many
days
had
that
debt
been
outstanding.
C
D
C
So
if
I
zoom
out
here
actually
what
you'll
see
is
there
had,
in
the
past,
been
a
more
dramatic
increase
in
both
right.
So
as
the
died
supply
was
growing,
there
was
sort
of
a
consistent
amount
there
and
it
actually
seems
to
be
a
trend
where
there's
kind
of
this
steady
state
of
how
old
debt
gets
before
it
starts
to
get
paid
back
is
how
I
would
interpret
it,
so
that
kind
of
has
consistently
persisted
and
it
had
slowed
down
recently,
except
in
you
know
the
past
three
weeks.
D
This
thing
is,
it
only
goes
up
basically,
which
makes
sense
positive
in
the
early
earlier
days
in
Eustis,
so
all
the
debits
being
paid
back
to
renew
while
we're.
Presumably
this
doesn't
go
up
forever
right,
like
that,
like
we
haven't
seen
periods
except
for
like
really
tiny
ones,
where
the
thing
has
gone
down.
C
Right
so
I
would
not
expect
the
closed
the
closed
debt
age
to
decrease
for
quite
a
while
and
and
it
actually
theoretically,
it
could
and
I
have
to
double-check
on
this,
but
I
think
it
could
continue
to
increase
without
ever
needing
to
decrease,
in
the
sense
that
if
the
dye
supply
continues
to
to
grow,
the
average
age
could
just
kind
of
level
out
for
debt
getting
paid
back.
It
doesn't
necessarily
have
to
decrease
that
would
be
kind
of
like
if
it
hits
this
like
equilibrium,
steady
state,
but
this.
D
C
Some
refinancing
but
I
think
a
fair
amount
of
repayment
of
old
debt,
which
is
what
you
what
you
really
want
to
see
out
of
the
system.
If
you
saw
this
open
died
days,
just
continually
rising,
that's
in
some
sense,
an
accrual
of
risk
or
at
the
very
least,
kind
of
a
delaying
of
revenues
for
maker
holders,
which
is
not
good.
C
D
Can
what's
interesting
the
drastically
increasing
eath
price
in
this,
as
well
like
the
the
timing
of
the
19.5
stability
fee
and
was
its
correlated
to
the
bullishness
of
CVP
holders?
And
so
we
kind
of
to
me
what
I
saw
in
the
increasing
stability
fee
was
one
of
the
first
leading
indicators
of
a
bull
market
or
juvenile,
rising
eath
price,
as
the
stability
feels
like
a
proxy
for
the
sentiment
of
the
people.
So
so
that's
kind
of
those
things
are
gonna,
be
correlated
I.
Think.
C
Yeah,
a
hundred
percent
I
don't
mean
to
claim
that
this
is
completely
the
stability
fee,
but
all
things
kind
of
go
hand
in
hand
and
no
one,
like
item
exists
in
a
vacuum.
So
one
of
the
interesting
things
is
the
stability
fee
also
has
an
impact
on
what
happens
with
secondary
lending
and
so
to
some
extent.
You
know
you
saw
an
increase
from
you
know.
Maybe
two
and
a
half
million
two
million
died
being
supplied
on
secondary
lending
platforms
to
about
fourteen
in
that
same
sort
of
time
span.
C
This
is
a
shorter
time
span
graph,
but
it's
still
that
May
to
June
period,
and
then
you
saw
the
amount
of
dye
being
borrowed
on
those
platforms
as
well
rise
to
about
eleven
and
a
half
million
from
basically
nothing.
You
know,
maybe
one
to
two
million
and
so
to
some
extent,
10
million
plus
die
was
roughly
refinanced.
C
So
see,
I
just
kind
of
touched
on
that
is,
you
do
see
almost
every
month
like
clockwork
kind
of
this
dip
in
the
percentage
of
drawers
that
are
happening
on
new
CDP's
versus
old.
So
it's
almost
like
a
cycle
that
I
don't
want
to
get
too
cute
here.
So
what's
interesting
is
during
that
same
time
frame
of
this
run-up
we
saw
far
fewer
draws
happening
on
new
CDP's
versus
old.
C
A
D
A
quick
question
about
the
graph
right
above
this
one,
yet
another
lower
one
one
more
down.
Yes,
so
new
draws
versus
existing
CDP's.
Are
you
counting
that
as
the
first
draw
on
a
new
CDP
and
then
the
second
draw
after
that
falls
into
the
red
category
of
existing
CDP's
or?
What's
what's
the
specifics
there
yeah.
C
D
C
That
number
just
ran
up
from
you
know,
simple
math,
but
then,
as
people
saw,
the
price
start
to
rise
up,
they
started
to
remove
some
collateral
from
the
system.
Now,
I'm,
guessing
with
the
drop
in
eath
price,
you'll,
see
the
opposite
trend
where
people
will
sort
of
counter
the
drops
and
liquidations
and
you'll
probably
see
this
go
back
up,
but
I
don't
want
to
call
my
shot
too
early
on
that
I'm
going
to
pause
for
questions
or
if
I
ran
over
time.
C
C
I
did
so
the
secondary
lending
platforms
as
I
shadowed,
the
total
borrow
and
supply
volumes
both
have
run
up
since
the
mean
they've
been
running
up
for
the
past
week,
or
so
the
borrow
has
outpaced
to
supply
a
little
bit,
and
so
those
rates
have
come
up
a
little
bit.
Actually,
I
can
go
to
that
graph.
That.
C
C
Now
the
implication
for
that
on
stability
fees
is
curious,
because
if
there
exists
this
pool
of
loans
that
people
have
kind
of
walked
away
from
or
tepees
they
don't
care
about,
and
those
are
the
recurring
fees
that
might
never
get
realized.
Those
would
skew
the
numbers,
but
it's
really
hard
to
filter
those
out.
A
A
D
B
B
And
this
is
kind
of
the
mini
outline
for
the
quantitative
stuff
so
to
book
credit
risk.
Next
few
weeks,
we'll
talk
a
little
bit
about
liquidation
ratio,
which
is
really
just
a
continuation
of
stability
fee
discussions
in
general
for
this
week
and
going
forward
it's
a
bit
difficult
at
times
for
me
to
kind
of
figure
out
what
the
right
level
of
detail
is
or
the
presentations.
So,
if
anything
is
too
detailed,
please
ask
for
clarification,
or
if
it's
not
detailed
enough,
please
I
encourage
you
guys
to
jump
in
with
questions
as
well.
B
Okay,
so
quick
recap:
from
last
week
we
had
our.
We
calculate
our
risk
rating
from
our
due
diligence
report.
We
still
haven't
seen
what
to
do
with
that.
Yet
we
talked
a
little
bit
about
how
the
risk
premium
has
attached
to
a
CDP
as
some
function
of
its
loss,
distribution
and
expected
loss
being
the
average
or
mean
of
that
loss.
B
B
B
In
in
practice,
due
to
just
the
difficulties,
it's
often
just
used
as
a
substitute
and
then
you
can
try
to
analyze.
How
could
it
behaves
over
time?
A
simple
example:
company
in
the
energy
sector
wants
to
borrow
money
right.
They
want
to
issue
some
debt,
one
way
to
calculate
the
probability
of
default
for
such
a
company's
to
just
look
at
all
companies
in
that
sector.
Over
the
last
50
years,
right
say:
you
have
like
thousand
companies
in
the
energy
sector
and
20
of
them
defaulted.
B
B
You
can,
of
course,
improve
on
this
with
fundamental
analysis
and
this
kind
of
dives
into
credit
ratings,
where
they
basically
compile.
All
this
data
do
the
due
diligence,
everything
and
package
it
up
into
a
rating
these
ratings
can
from
these
ratings.
You
can
often
derive
the
metrics
that
you're
looking
for
so
interesting
question
is:
does
this
work
with
CD
piece?
Can
we
just
look
at
CDP's
over
the
past
year
and
a
half
right?
B
Okay,
so,
as
I've
alluded
to
many
times
before,
probably
gonna
explore
a
variety
of
models.
My
preference
is
to
start
kind
of
with
an
academic
model,
in
my
opinion,
helps
build
up
a
lot
of
the
intuition
behind
this
stuff
talk
a
little
bit
about
why
it's
not
so
great
see
what
we
can
save
improve
it
iterate
it
and
just
kind
of
keep
doing
this
over
and
over
again.
So
in
some
ways,
today's
presentation-
it's
not
does
not
reflect
the
realities,
but
it
might
so,
let's
see,
let's
just
dive
right
into
it.
B
B
B
B
We'll
also
kind
of
define
the
whole
space
of
user
behavior
just
by
F.
For
now
we're
probably
not
going
to
talk
too
much
about
user
behavior
today,
because
I
don't
think
we'll
have
time,
but
essentially
we
have
to
account
for
the
fact
that
users,
as
I
mentioned,
can
add
or
subtract
collateral
or
pay
down
or
draw
or
die.
B
This
is
important
because
see.
Dps
are
not
static.
So
if
you
have
a
CDP
that
has
a
liquidation
price
of
200,
I
I
think,
as
we
all
know,
as
we
approach
that
200
price
point,
users
will
tend
to
take
action
that
will
kind
of
push
their
liquidation
price,
lower
they'll,
either
pay
back
debt
or
they'll,
add
collateral,
and
this
is
just
important
for
modeling.
B
The
debt
is
a
function
of
stability,
fee
time
and
user
behavior,
so
stability
fee
and
time
I
mean
by
that
is
kind
of
the
accrued
stability
fees
over
time
and
the
user
behavior
is
whether
they
are
drawing
or
wiping
died.
In
addition
to
standard
user
behavior
of
trying
to
avoid
the
quotations,
there
are
some
technical
edge
cases
as
well
if,
in
particular,
the
Oracle
security
module,
which
has
a
one-hour
price
feed
delay.
B
B
When
so
the
time
rising
of
CDP's,
we
all
know
they're
open-ended
right,
but
it's
helpful
to
just
kind
of
assume
a
one-year
forward-looking
period,
somewhat
arbitrary,
but
also
fairly
standard,
and
it
doesn't
really
change
the
analysis
too
much
and
we
know
that
CDP's
are
default.
Our
CDP's
default
when
they
hit
the
liquidation
ratio.
L-
and
this
notation
here
at
the
end,
is
basically
saying
the
first
instance
at
which
the
price,
or
the
first
instance
at
which
the
collateralization
ratio
crosses
the
liquidation
ratio
is
when
default
occurs.
B
Okay,
few
more,
the
CDP
is
liquidated
with
probability
Q,
and
so
this
is
the
probability
that
that
t1
threshold
is
breached
before
our
1
year
time
horizon.
So
this
is
definitely
something
critical
that
we're
interested
in,
and
we
know
that
this
probability
depends
on
the
evolution
of
the
collateralization
ratio,
which
in
turn
depends
on
the
value
of
the
collateral
and
the
amount
of
die
debt,
which
in
turn
depends
on
this
stuff.
We
talked
to
before
asset
pricing,
user
behavior
at
liquidation.
B
B
As
we
all
know,
there's
this
represents
the
slippage
adjustment
for
when
you
liquidate
the
collateral,
the
less
slippage
there
is
the
higher
the
higher
you
amount
of
the
debt
that
you
recover
and
then,
of
course
we
have
it.
We
have
this
amount
offset
by
the
liquidation
penalty
as
well
or
I
should
say
added
back
in
feeling.
Rr
is
the
risk-free
rate
from
traditional
world
plays.
B
It
plays
a
role
and
our
credit
spread,
which
is
essentially
what
we're
solving
for
is
to
find
R
is
minus
R
and
we'll
ignore
p
sr
adjustments
for
now
and
the
expected
loss
of
a
single
CDP,
which
is
what
ultimately
we're
trying
to
solve
for
here
is
beast.
Is
the
street
variables,
probably
all
good
any
questions
so
far
on
kind
of
any
of
these
are
pre
model
specifications.
B
Okay,
so
we
already
know
we
want
to
solve
for
probabilities
losses
and
exposure
amounts,
and
we
need
to
develop
some
intuition
around
the
asset,
price
and
user
behavior
standard
approach
to
modeling
an
asset
price
is
to
apply
a
what's
known
as
a
stochastic
process
which
we'll
talk
about
in
a
second
and
just
helps
us
understand
the
asset
in
a
variety
of
ways.
It's
not
perfect,
but
it's
fairly
standard
and
it's
has
quite
a
history
behind
it
and
then
to
model
to
model
user
behavior.
We
can
analyze
historical
CDP
stats
and
go
from
there.
B
Fish
and
Alex
have
done
a
lot
of
great
work
in
this
area
and
I
hope
and
I
think
we'll
be
able
to
explore
that
and
a
little
bit
later
as
well,
and
in
some
instances
you
can
just
look
at
user
behavior
directly
and
maybe
even
bypass
collateral
assets.
Alright,
so
the
way
I
like
to
think
about
it
is
you
can
look
at
the
CDP's
loans
by
themselves
or
you
can
try
to
pick
the
CDP
apart
and
look
a
little
bit
like
deeper
at
its
underlying
assets.
B
I
think
there
are
advantages
and
disadvantages,
though,
and
in
in
some
cases
we
can
even
do
a
hybrid
to
kind
of
to
clarify
that
a
bit.
So,
if
you
think
about,
if
you
think
about
that
corporate
debt
example
I
used
a
few
slides
ago,
all
right-
it's
not
always
easy
or
even
possible
to
actually
model
the
underlying
assets.
That
is
behind
that.
B
Sometimes
you
can
only
just
look
at
it
from
the
from
the
debt
level
and
try
to
model
how
the
deputy
Hank
behaves
over
time
or
historically,
in
some
cases
such
as
our
CDP's
with
eath,
you
can
actually
model
the
underlying
asset,
which
I
think
gives
us
an
advantage,
but
there
could
very
easily
come
a
time
where
the
collateral
that
is
put
into
our
into
CD
piece
is
just
a
representation
right.
It's
not
something
that
can
be
modeled
directly
or
easily.
B
Okay,
so
we're
gonna
talk
a
little
bit
about
that
stochastic
model
for
the
eath
price.
That
I
was
talking
about,
and
I'm
gonna
impose
a
bunch
of
assumptions
right
now
and
then
relax
them
slowly,
because
it's
a
lot
to
do
it
all
at
once.
So
I'm
going
to
ignore
user
behavior.
For
now
the
act,
the
ability
for
users
to
add
or
remove
collateral
at
will
at
any
time
and
to
add
or
subtract
die
at
any
time.
It
makes
things
really
complicated.
B
It's
caught,
honnestly
causes
a
lot
of
headache
just
adds
a
lot
of
complexity,
which
I
mean
is
is
what
makes
maker
out
such
an
awesome
protocol
right,
it's
extremely
flexible,
but
it
becomes
a
little
bit
difficult
to
analyze.
Ving
assume
no
slippage
for
the
collateral
amount,
so
essentially
soon
as
liquidation
is
triggered,
you
can
recover
everything.
B
Returns
are
normally
distributed,
I'll
get
into
what
that
is
for
next
slide,
no
early
liquidations,
which
seems
like
a
silly
assumption
and
asset
price
cannot
gap
up
or
down
and
okay.
So
it's
actually
the
reason
why
I've
imposed
all
these
seemingly
random
assumptions
is.
If
you
go
through
all
this,
then
you
can
create
a
nice
little
model
called
the
Merton
model,
which
is
almost
a
50
year
old
model
by
now-
and
this
is
actually
part
of
the
part
of
the
pioneering
stages
of
modern
options-
pricing
theory.
B
Basically,
if
you
assume
that
the
Claudel
asset
behaves
a
certain
way,
you
can
do
interesting
stuff
with
it.
So
this
equation
up
here
at
the
top
is
it's
known
as
a
stochastic
differential
equation,
and
essentially
what
it's
saying
is
is
that
an
asset
price
can
be
reduced
to
in
every
small
time
increment.
B
It
will
move
a
little
bit
in
accordance
with
its
historical
mean,
plus
some
random
component,
that
is
scaled
by
the
volatility
and
and
that
random
component
is
is
quite
literally
just
almost
think
of
it
like
as
an
rng,
and
essentially
you
can
simulate
thousands
of
different
price
paths
for
collateral
asset
where
each
price
path
is
a
potential
likely
symp
simulation
for
the
evolution
of
this
asset
price.
So
you
can
say
he
goes.
B
You
know
you
can
say:
ether
will
tend
to
go
up
over
time
according
to
some
some
expected
return
or
some
mean,
but
then
of
course,
there's
this
volatility
component,
which
will
cause
it
to
veer
and
all
sorts
of
ways,
and
so
we
can
do
is
we
can
calculate
these
sample
pads
and
then
let's
say
we
compute
thousands
of
them,
and
then
we
can
merely
just
calculate
the
number
of
them
which
end
up
below.
Are
we
liquidation
threshold.
D
So
Syrus
is
this:
where
are
these
the
returns
that
are
that
you
were
making
assumptions
about
their
distribution?
This
is
nothing.
This
is
the
return
of
the
underlying
asset.
Yes,
you
mean
these
yeah,
so
the
turns
are
normally
distributed.
This
wooden.
This
would
mean
the
logs
of
the
returns
are
normally
distributed,
so
the
returns
themselves
would
be
log.
B
Okay,
so
few
things
we
can,
we
can
glean
from
this
from
this
kind
of
model.
One
is,
as
I
mentioned,
we
can
calculate
the
frequency
of
the
paths
which
end
up
lower
our
threshold,
which
will
help
us
determine
the
probability,
distribution
and
actually
the
same
time.
We
can
just
calculate
how
much
loss
each
particular
path
is
associated
with
and
essentially
get
knock
out.
B
B
First
assumption
that
we
can
relax
is
no
early
liquidations,
that's
kind
of
an
easy
one
to
get
right
off,
and
here
we
can
kind
of
see
the
same.
The
same
style
of
model,
but
essentially
as
soon
as
the
paths
breach
the
threshold
liquidation
is
triggered
and
that's
more
in
line
with,
with
the
model
that
we're
used
to
screen.
One
should
not
have
an
arrow
to
of
it.
B
And
let's
get
let's
get
rid
of
this
asset.
Price
cannot
gap
up
or
down
as
well,
and
so
we've
modified
our
process
a
little
bit
by
adding
a
jump
variable
which
essentially
means
that
the
asset
price
can
kind
of
gap
up
or
down
at
any
random
moment.
And
this
y
variable
here.
This
dy
is
essentially
a
what's
known
as
a
Poisson
process,
which
essentially
means
that
you
know
a
event
will
occur
with
a
certain
frequency
and.
B
And
so
one
interesting
question
is
how
to
actually
calculate
the
frequency
of
these
big
jumps
up
or
down.
This
is
something
we've
been
doing
a
little
bit
of
research
on
essentially
poring
through
historical,
historical
data
for
the
markets
and
trying
to
say,
okay.
How
many
times
has
Eve
had
a
sudden
crash?
What
was
the
magnitude
of
the
moves
and
so
forth
and
then
going
from
there?
So
that's
some
interesting
research
we've
been
doing
on
the
side
happy
to
chat
about
on
the
chat
on
the
chat.
B
B
Again,
simply
amplify
that
would
be
the
kind
of
the
eath
versus
some
Fiat
backs
table
coin,
which
everybody
knows
has
a
chance
of
having
a
sudden
plummet
right.
So
if
we
wanted
to
incorporate
some
likelihood
of
an
asset
collapsing,
we
wouldn't
be
able
to
glean
that
from
the
historical
data,
and
this
is
where
we
would
incorporate
our
qualitative
information
for
collateral
asset.
So
we
can
essentially
use
that
to.
B
B
So
this
first
one
behavior
super
important
which
we
will
get
to.
Hopefully
next
call
slippage
as
well
when
it
comes
to
calculating
the
liquidation
ratio
and
evaluating
liquidity,
the
markets
as
well.
This
third
one
is
also
fairly
easy
to
relax
essentially
requires
using
its
own
interest
rate
stochastic
model,
and
these
last
two
are
a
little
bit
contentious.
So
we'll
talk
about
that
time
as
well.
B
So
the
next
steps
after
this,
what
we,
what
we've
done
so
far,
is
single
last
set
for
a
single
CDP
and
we
want
to
evolve
this
to
single
asset
with
multiple
CDP.
So
right
now
we
have
thousands
of
CDP's
each
with
their
own
debt,
throne,
collateral,
throne,
liquidation
price,
and
we
can't
do
a
full
simulation
one
by
one
per
CDP.
It's
just
not
feasible,
so
what
we
would
have
to
do
is
take
distribution
of
CDP's
and
model
them
as
a
whole,
and
that's
a
lot
of
the
work
that
we've
been
doing.
B
Furthermore,
we
don't
want
to
ideally
have
just
one
liquidation
ratio
bucket,
but
you
might
want
to
have
CDP's
with
the
150%
liquidation
ratio
that
have
a
certain
stability
feat,
but
another
set
of
CDP's
that
have
a
200%
liquidation
ratio
that
might
have
a
lower
stability
fee
because
they've
committed
to
keeping
the
higher
collateral
reserve.
So
these
are
all
things
we
can
do
with
with
the
model
that
I've
just
described.
B
Require
modeling
actual
collateralization
ratio,
as
opposed
to
the
asset,
which
is
just
a
few
small
tweaks,
still
a
lot
of
research
to
do
in
terms
of
kind
of
sharpening
our
corners,
but
I
think
we're
getting
there
for
sure,
and
so
essentially,
everything
we've
done
so
far
is
to
calculate
that
expected
loss
which
what
is
the
check?
What
is
how
much
do
you
expect
to
lose
on
a
particular
CDP
or
group
of
CBeebies
right
and
big
question
is
how
to
get
from
this
to
the
actual
stability
fee
calculation.
B
So
the
easiest
way
to
think
about
that
is
to
just
let's
first
take
a
quick
minor
step
back
and
let's
look
at
a
hypothetical,
risk-free
CDP.
So
imagine
a
CDP
that
you
know
takes
out
a
hundred
I'd
debt
or
stability.
If
you
have
five
percent
right,
it'll
pay
back
105
in
the
year,
and,
let's
assume
that
that
that
stability
fee
pay
back
is
completely
independent
of
the
collateral
asset
price,
no
matter
what
feed
goes
to
zero
you're
still
getting
back
one
hundred
and
five
died.
B
B
B
B
B
B
B
Was
this
all
worth?
It
maybe
definitely
a
good
amount
of
complexity
here
and
a
good
amount
of
assumptions
in
particular,
in
particular
the
log
normal
distribution
of
the
asset
price,
which
is
what
underpins
the
stochastic
model
under
prices,
the
tails
and
that's
how
you
end
up
with
outside
moves
that
you
don't
expect.
B
A
lot
of
historical
data
and
in
many
ways,
looking
at
empirical
models,
would
be
better
in
some
regards,
because
I
think
it
could
have
a
better
tail
distribution,
but,
as
I've
mentioned,
it
doesn't
necessarily
factor
in
the
right.
The
right,
qualitative
risk
characteristics,
so
I
think
some
I
think
some
ideal
blend
could
be
combining
the
empirical
with
some
sensible,
qualitative
risk
adjustment
and
then
interesting
enough.
There's
also
a
thing
called
extra
models
where
you
can
just
defer
to
somebody
who
claims
to
know
for
their
talk.
B
A
All
right,
thank
you
service.
You
Nessie,
there's
a
lot
to
digest
here
and
if
people
have
questions
they
want
to
absorb
some
of
this
stuff
surface,
it's
in
the
chat
at
the
side
we
have
Ernie's.
This
is
a
slight
segue
to
talk
about
signaling
in
debate
and
these
deeper
discussions,
because
this
is
a
past
that
maker
Dow
is
going
to
be
heading
down
more
and
more
as
the
world
and
crypto
gets
more
complicated,
particularly
in
these
government's
calls,
and
particularly
as
we
begin
to
shift
focus
more
towards
risk
right
now.
A
The
deep
discussion
venue
that
we
have,
unfortunately,
is
reddit
and
reddit
isn't.
This
may
come
as
a
surprise
to
people,
but
it's
not
designed
for
deep
and
thoughtful
discussion.
So
what
we're
going
to
do?
What
we
have
done
is
we're
going
to
release
very
soon,
a
forum
for
the
maker
dev
ecosystem,
and
in
that
forum
we
have
numerous
top-level
categories:
those
chocolate
categories
movie
and
/,
our
community
governance
and
risk
and
x
y&z
as
well,
and-
and
this
is
going
to
be
where
we
began
to
really
get
serious
about
debates.
A
First,
the
week
action
is
called
the
first
week
of
July
it's
over
optimistic,
but
that's
coming,
and
so
I
wanted
to
put
that
out
there
that
if
you
don't
have
a
question
right
now,
you
haven't
missed
the
boat.
We
recognize
the
need
that,
for
a
long
term,
very
detailed
debating
mechanism
with
some
some
light,
reputation,
management
and
polling
built
in
and
that's
been
built,
it's
being
last
week's
being
applied
and
will
be
released
to
the
community
first
week
of
July
I'm.
A
D
B
B
A
Work
takes
a
lot
idea.
I
can
sell
so
here's
the
idea
that
we're
we've
we've
constantly
been
debating
the
fact
that
we
have
a
limited
number
of
levers
in
this
ecosystem,
but
the
Libra
that's
been
exposed
right
now
is
the
stability
fee.
There
was
a
lot
of
back
and
forth
a
lot
of
very
considered
discussions
about
whether
we
should
be
had
playing
with
more
libras
than
that.
I
think
that
we've
seen
that
the
measured
discussion
was
correct.
We
fixed
things
as
a
community
with
the
stability
fee,
but
the
the
market
conditions
have
changed.
A
Inventory
levels
presumably
have
recovered
to
healthy
levels.
Secondary
lending
platforms
have
arisen
to
takes
on
the
pressure
off
of
us.
We
are
no
longer
in
an
extended
bear
market,
while
maybe
it
just
started
in
the
last
hour
again
so
who
knows,
but
it
up
until
today,
we
weren't
in
an
extended
bear
market.
A
B
C
C
Think
some
of
the
answers
are
pretty
clear
which
is
okay.
Let's
assume
we
don't
want
to
hit
it.
Let's
assume,
there's
relatively
low
risk
in
increasing
the
debt
ceiling.
If
we
are
not
going
to
hit
it
now,
the
the
one
caveat
is
if
the
debt
ceiling
is
increased
and
somebody
goes
ahead
and
aggressively
votes
down
the
stability
feet.
That's
your
risk
scenario.
So,
given
that
both
of
those
things
are
like
under
the
control
of
this
governance
process,
presumably
there's
some.
C
You
know
higher
hivemind
intelligence
here
that
realizes
don't
do
both
of
those
things,
because
in
the
scenario
that
the
stability
fee
is
artificially
depressed
or
inflated
from
external
market
factors
and
those
external
market
factors
change
like
oh
I,
don't
know
a
sudden
drop
in
price.
Then
you
might
be
worried
about
people
deciding
to
lever
up
dye
price,
potentially
taking
hit
and
thus
deciding
hey.
We
need
to
react
and
make
a
change
to
stability
so
that
that
kind
of
reactionary
behavior
in
response
to
market
forces
is
riskier
when
there's
a
higher
debt
ceil,
because
then.
C
Yes,
so
you're
you're
trading,
one
risk
for
another
and
the
risk
that
you
are
reducing
is
the
risk
that
you
hit
the
debt
ceiling
and
you
have
to
work
harder
to
unwind.
Some
of
the
negative
effects
that
get
bubbled
up
from
doing
the
risk
that
you
are
gaining
is
the
risk
that
the
governance
process
kind
of
lets,
a
fox
into
the
hen
house,
reduces
the
stability
feet
and
somehow
somebody
decides
to
bully
the
situation
and
overnight
mints
a
bunch
of
die
either
crashing
the
die
price
or
creating
a
whole
bunch
of
other
negative
effects.
Yeah.
B
C
C
C
A
Right
well,
I'm,
just
saying
that
there
potentially
there's
an
order
of
operations
here
where
you
you
make
the
system
healthy
with
the
primary
cleaver,
which
is
the
stability
feat
and
once
you've
established
or
if
you
have
some
kind
of
a
monitoring
system
that
can
tell
you
the
inventory
levels
are
healthy.
Dude
is
the
stability
fees
being
appropriate
and
that
fetching
user
behavior
or
secondary
lending
platforms?
Whatever,
then
it's
safe
to
raise
and
lower
the
stability
there
I
mean
the
debt
ceiling,
but
the
debt
ceiling
is
not
the
primary
go
to
risk
brown
bear.
C
C
The
more
excess
supply
sits
on
secondary
lending
platforms,
the
less
worrisome
it
should
be
that
you're,
you
know
nearing
the
debt
ceiling
now
as
that
supply,
because
that's
kind
of
your
buffer
range
as
that
comes
down
yeah,
you
have
the
the
chance
of
grinding
bone
against
bone
and
and
getting
some
pain
out
of
that,
but
I
don't
think
we're
quite
there.
I
do
think.
C
It's
definitely
worth
starting
to
raise
that
conversation,
starting
to
be
aware
that
that
may
be
something
we
want
to
do
soon,
because
even
at
the
current
stability
fees,
the
supply
has
been
increasing.
Now
this
supply
on
those
secondary
lending
platforms
has
also
been
increasing.
So
it's
it
may
be
a
little
bit
misleading
to
just
look
at
the
dye
supply.
C
E
In
there,
so
that
one
rogue
actor
can't
decide
them
into
a
bunch
of
dyeing
crash
the
parts
you
want
you
want
to
leave
enough,
you
don't
want
to
have
so
much
of
a
buffer
where
we
make
this
debt
ceiling.
500
million
and
the
current
outstanding
dye
is
80
and
if
you
lower
the
stability
fee,
you're
just
taking
unnecessary
risk.
C
Right
and,
and
so
the
stability
fee,
I
actually
look
up
as
kind
of
like
a
speed
adjustment,
so
when
the
civility
fee
is
higher,
the
supply
moves
slower
and
when
the
civility
fee
is
lower
in
terms
of
in
response
to
demand
and
and
when
the
civility
is
lower.
It
moves
faster
now,
if
also
not
just
demand,
but
also
moves
faster
and
slower
in
response
to
eath
price
changes.
C
So
the
one
caveat
I
would
say
is
it
doesn't
necessarily
have
to
be
this
like
malicious
rogue
actor
or
whatever
it
could
just
be
eat
and
eath
could
be
our
rogue
actor
and
make
significant
price
changes
drop
to
260,
whatever
I
don't
care
and
cause
a
bunch
of
different
people
to
act
on
behalf
of
market
forces,
which
is
basically
the
same
effect.
It's
like
a
rogue
actor.
D
C
E
Get
it
but
the
point
in
general
and
I'm
about
to
do
a
little
presentation
about
this
is
that
you
know
in
single
collateral
die
the
fundamental
lever
to
control
the
supply
and
demand
was
the
stability
fee
right?
The
multi
collateral
died
to
some
extent,
that's
going
to
shift
away
and
it
should
shift
away.
So
it's
changing
the
changing
underlying
risk
premium
for
a
specific
collateral
shouldn't,
be
the
method
to
control
supply
and
demand.
C
Yeah
and
and
I'll
echo
kind
of
probably
there's
some
redditors
who
are
groaning
right
now.
The
changing
the
civility
fee
doesn't
necessarily
solve
your
problem
right.
So,
even
if
you
hit
the
debt
ceiling,
you
jack
up
a
stability
fee,
it
doesn't
necessarily
mean
that
it's
going
to
solve
the
underlying
friction
that
you
run
into
from
hitting
the
debt
ceiling.
So,
like,
let's
say
this
movie
fee
was,
you
know,
14
13
percent.
You
hit
that
debt
ceiling,
because
somebody,
you
know
minted
a
bunch
of
died.
C
People
can
continue
to
source
daya,
attic
Lee
from
secondary
lending
platforms,
to
the
extent
that
that
supply
exists.
But
if
you
increase
the
stability
fee,
you
may
also
reduce
the
supply
that
exists
on
the
secondary
lending
platforms,
and
you
may
not
necessarily
reduce
the
dye
supply.
It
sort
of
depends
on
what
the
rates
are,
and
so
it
could
solve
the
problem.
It
doesn't
necessarily
solve
the
problem,
and
this
is
where
I
think
some
people
have
voiced
some
frustration,
that
the
stability
fee
is
a
bit
of
a
blunt
instrument,
totally
true
I.
C
E
And
a
danger,
or
the
scary
part
right
now,
is
all
the
thinking
we've
constructed
thus
are
on
under
single
collateral
die
and
how
we
use
the
stability
fee.
You
know
it's
somewhat
changing
I
mean
when
you
go
into
this
multilateral
stuff
that
we're
rotating
into,
and
this
is
kind
of
like
a
little
segue
to
you
know,
as
this
whole
learning
process
goes
on,
it's
painfully
humbling
and
I'm
going
to
say
thank
you
very
much
to
to
vishesh,
Cyrus,
Alex
and
other
guys
in
the
maker
chat
and
read
it
or
some
of
this
stuff.
E
You
know
from
the
conversation
we
had
last
week,
you
know
kind
of
took
some
of
the
commentary
that
folks
had
and
kind
of
digested
what
were
my
failings?
What
were
the
pieces
that
I
were
basically
missing
on
that
and
you
know
kind
of.
Let
me
just
share
this
kind
of
reconstructed
back
to
what
I
thought
was.
You
know
where
we
started,
which
is
single
collateral
die
where
we
only
have
aetherium
and
we
or
liquidation.
We
know
our
collateralization.
E
We
know
our
debt
ceiling,
it's
changeable
and
the
risk
premium
is
what's
changing
dozens
and
dozens
of
times
will
be
your
husband
and
that's
what
we're
using
as
our
fundamental
collateral
package.
You
know
as
it
kind
of
here's
my
fourth
grader
drawing
again,
you
know,
as
that
relates
where
you've
got
a
risk
premium
and
that
interest
rate
is
what
we're
using
to
balance
back
and
forth
and
how
we're
trying
to
swing
back
and
forth
to
try
and
stabilize
the
price.
E
It's
not
the
world's
best
graphic
but
I
hope
the
message
kind
of
comes
across
you
know,
but
the
idea
behind
it
when
we
switch
over
to
a
multi
collateral
area
where
we're
going
to
have
you
know,
insert
your
name
of
collateral.
That's
the
liquidation
ratio
will
be
static
for
that
package,
so
will
the
collateralization
ratio
so
the
debt
ceiling
for
the
most
part,
but
the
risk
premium
for
it.
You
know
it's
going
to
get
set
by
a
risk.
E
Team
member
it'll
be
modified,
maybe
weekly,
but
it's
conditions
based
on
the
risk
of
the
collateral
package
as
a
whole.
Not
not
anything
to
do
it
shouldn't
have
anything
to
do
with
the
price,
as
it
relates
to
the
price
of
died.
It
should
only
be
risk
driven
and
risk
driven
to
the
point
where
this
collateral
package,
as
an
aggregate
as
compared
to
the
whole,
should
be
viewed
in
effect
as
being
riskless.
In
effect,
a
insurance
wrapper
around
a
collateral
of
whatever.
E
That
type
is
if
it's
ether
with
X
liquidation
ratio
and
Y
collateralization
ratio,
those
are
going
to
be
different
and
we
set
the
risk
premiums
accordingly,
but
we
really
shouldn't
have
to
change
them
that
frequently
conditions
on
the
ground
that
derived
the
risk.
So
in
Cyrus's
comment
about
putting
an
energy
company
and
putting
their
debt
as
a
function
of
collateral.
You
know
conditions
on
the
ground.
If
oil
prices
go
to
$5
a
barrel,
okay,
there's
a
high
chance
that
company
might
default.
E
The
risk
premium
should
get
adjusted
accordingly,
but
that
still
doesn't
change
anything
to
do
with
it
shouldn't
have
anything
to
do
with
the
price
of
die.
Yet
it's
purely
de-risking,
a
collateral
package
to
the
point
where
it's
risk
less
so
the
corollary
to
this
it's
kind
of
in
an
MCD
world
where
we're,
in
effect
using
the
DSR
and
waiting
against
the
riskless
offset
of
this
litany
of
riskless
collateral
packages.
E
Some
of
them
meant
died,
some
of
them
burned
I
and
the
DSR
is
what's
used
to
mop
up
the
risk
lifts
die
that
was
minted
off
of
them,
which
is
kind
of
a
little
bit
of
a
tow
into
what
we
discussed
last
week
about.
If
you
die,
that's
being
minted,
isn't
riskless,
then
you
start
injecting
funky.
You
injection
risk
into
the
system,
because
you
can't
really
tell
if
the
DSR
is
mopping
up
using
a
riskless
tool
to
mop
up
the
dye
that
was
coming
from
a
risky
source
to
keep
them.
E
In
balance,
the
risk
premium
has
to
be
set
by
risk
team
people
that
know
that
this
goes.
This
will
kind
of
T
into
the
end
of
this
conversation
of
like
what
is
our
like.
What
are
we
actually
going
to
be
voting
on
in
six
months
and
a
year
and
three
years,
and
how
will
we
be
able
to
handle
the
quantity
of
risk
premium
voting,
and
will
we
even
be
voting
on
that
or
we
just
be
voting
on
the
recommendations
or
the
risk
team?
E
So
it's
because
of
each
collateral
package
when
they're
dialed
in
or
tuned
and
they
are
basically
riskless
using
the
DSR-
is
warranted
because
the
riskless
nature
is
offset
each
other.
So
you
know
and
for
all
the
electrical
engineers
out
there
are
engineers.
This
is
my
poor
man
starting
circuit
concept,
where,
at
the
end
of
the
day,
you
have
your
DSR,
which
in
effect
absorbs
whatever
the
collateral
that
comes
out.
E
E
E
Risk
teams
and
being
some
type
of
median
or
average
or
weighted
average
based
on
some
system.
I,
don't
know
and
saying
you
know:
five
people
like
almost
like
a
shard
of
the
decision-making
and
then
taking
the
some
type
of
computation
computation
saying
that
there
are
the
400
collaterals
we've
got
hanging
out
there
here
are
the
recommended
slight
modifications
this
week
and
then
either
vote
for
it
or
you
veto
it
or
suggestion
outstanding
question:
I,
don't
know
the
answer
for
that
one.
So.
E
It
today
it's
more
of
a
question
legitimately
in
six
months.
How
can
we
the
the
biggest
concern
that
I
think
all
of
us
should
have
is
those
risk
premiums
that
are
char
are
going
to
be
assigned
to
a
given
collateral
package
that
Cyrus
and
team
will
compute
and
spend
valuable
time
figuring
out
what
it
should
be?
How
do
we
maintain
them
and
yeah.
A
And
that's
it!
Okay!
Well,
let
me
start
answering
some
of
these
theoretical
Smurfs
on
these
hypotheticals
with
some
theoretical,
so
you,
the
idea,
is
that
we
don't
know,
but
it's
not
like
we're
unprepared.
We've
been
discussing
precisely
that,
basically
internally
from
for
the
last
eight
to
twelve
months,
because
there's
no
Oh
prop
is
that
complexity
is
how
to
measure
this
governance
process.
That
we've
been
experimenting
with
for
the
last
41
weeks
is
to
determine
exactly
what
that
picture
looks
like
and
to
get
the
community
educated
and
engaged
enough
to
help
us
to
discover
the
answer.
A
A
We
need
to
figure
out
where
these
discussions
happen
and
then
we
need
to
figure
out
whether
we
need
to
optimize
or
whether
we
don't
and
all
those
things
we
can
can
and
have
come
up
with
elaborate
plans
to
to
anticipate
and/or
guess
about
how
that
might
turn
out
and
to
talk
about
how
we'd
like
to
have
that
turnout,
but
it's
ultimately
not
up
to
us.
So
the
idea
is
that
and
I
session
on
this
briefly
earlier
that
we
have
a
forum
coming.
A
That
forum
is
going
to
be
the
place
where
we
figure
out
the
answers
to
questions,
and
so
we'll
take
things
like
your
your
most
recent
weekly
narrative
Matthew,
which
I
loved
and
we'll
give
it
my
private
place
in
the
forums
or
in
the
Governance
forum
we
will
have.
This
is
the
questions
about
how
we
address
packages
or
tranches
of
risk
parameters
and
how
how
often
do
we
we
bite
these
things
off.
A
A
A
So
I
know
I
realized.
That
was
a
bit
of
a
non-answer,
so
you
think
you
have
you
raised
a
lot
of
great
questions.
We
agree
that
those
are
great
questions.
We
have
some
ideas,
but
the
solution
to
the
finding
those
the
answers
to
those
great
is:
we
need
to
improve
our
signalling
methods.
We
need
to
release
the
the
next
version
of
the
portal,
which
allows
us
to
do
multiple
polls
at
once.
Instead
of
being,
you
know
completely
dominated
by
a
weekly
stability,
people
pull
and
we
need
to
have
long,
deep
discussions.
E
I
think
the
greatest
concern
of
the
entire
project
is
how
to
maintain.
You
know.
The
call
it
tranches
package
is
what,
if
we're
going
to
demonically,
that
you
know
that
those
risk
premiums
are
priced
consistent
with
the
risks
compared
to
the
system,
and
they
maintain
the
exponential
nature
of
the
risks
that
they
actually
have.
We
start
having
too
much
apathy
towards
that,
and
just
a
little
bit
of
ignoring
the
conditions
on
the
ground
and
I
mean
I'm.
E
A
And
this
this
is
something
that
we
I
mentioned,
that
we've
been
having
these
discussion.
So
we
talked
about
governance
all
the
time
inside
maker
down.
Then
we
get
to
talk
about
it
publicly
for
an
hour
on
thursdays,
but
one
of
the
things
that,
like
one
of
the
most
often
requested,
floated
or
suggested
future
plans
for
for
governance,
is
that
the
reality
situation
is
that
not
everybody
in
our
ecosystem
is
either
willing
able-
or
you
know,
has
the
time
to
be
deeply
engaged
and
and
weigh
all
the
evidence.
A
So
people
are
keen
for
delegates
there's
some
kind
of
a
delegation
system
or
some
kind
of
mechanism
where
we
identify
people
that
are
trusted
enough
to
make
some
of
these
high-level
decisions
we
already
have
a
model
for
that
model
is
the
risk
teams
that
they
get
together
they
surface,
they
collect
the
data,
they
they
create
the
models
they
present
their
their
frameworks
and
then
munity
ratifies
those
and
potentially,
as
that
ecosystem
or
is
that
model
expands
gets
more
refined.
Then
we
can
start
applying
that
to
other
things.
So
maybe
there's
a
utility
token
trench.
A
D
A
That's
that's
basically
well
I
shouldn't
say:
basically
that
is
literally
what
the
whole
system
is
designed
to
do.
So
if
you
go
back
in
and
I
recommend
that
people
do,
this
is
read
the
foundation.
Principles
are
the
risk
currency
risk
framework
that
Stephen
published
for
us.
You
or
you
can
just
skip
to
the
third
one,
but
that's
what
it's
all
about.
We
do
scientific
governance
in
risk.
A
But
it's
it's
a
it's
an
evolutionary
process
and
I
think
that
we've
cautioned
I
hope
that
we've
been
good
about
this
message
is
that
I
can't
really
iterate
strongly
enough?
How
interested
how
focused
may
occur,
Dao
is
the
maker
foundation
is
on
decentralization.
That's
that's
our
core
principle.
In
order
to
do
that
responsibly
and
effectively,
we
need
to
do
that
in
steps.
A
A
Is
there
if
there's
I'm
going
to
Cyrus
make
you
the
host?
If
you
want
to
continue
sure
and
then
just
make
sure
you
stop
the
recording
before
you
shut
down
the
call,
because
the
last
time
I
discovered
that
there
was
three
and
a
half
hours
of
black
screen
I
had
to
edit
I,
don't
want
to
do
it
again.
Alright,
thanks
everybody
for
joining
great
discussions
again
as
usual,
we'll
be
posting
summaries
and
recaps
and
videos
and
audio
to
Twitter
the
subreddits
and
the
discussion
thread.
B
D
Well,
I
mean
if
you
look
you're
just
looking
at
equities,
for
example,
I
mean
their
returns
for
the
past
ten
years,
having
been
normally
distributed
right.
So
why
would
you
use?
But
so
once
you
start
looking
at
the
fundamentals,
let's
say
you
know:
interest
rates
are
low
and
there's
qED's,
so
you
then
part
of
the
fundamentals
and
say
well
I,
don't
think
the
returns
would
be
normally
distributed
throughout.
I
assume
that
for
my
models.
B
C
I
also
think
like,
if
we're
thinking
about
what
are
the
inputs
to
modeling
a
potential
price
path
for
an
asset.
It's
it's
one
thing
to
be
able
to
say:
I
know
what
the
fundamentals
are,
and
so
I
can
tell
you
how
this
asset
is
going
to
do
over
a
1
year,
2
year,
whatever
period
that,
if
you
can
do
that,
you
don't
need
to
look
at
any
kind
of
simulation
of
a
normal
price
path
or
log
normal
price
path.
C
Now
it's
not
right.
It's
not
going
to
be
perfect
to
be
able
to
say,
hey,
I've
got
this
estimation
of
a
long,
normal
price
path
and
I'm
going
to
assume
that
that's
the
case
right
we're
not
doing
that
either.
What
you're
saying
is
under
various
potential
assumptions
that
could
produce
various
log,
normal
price
paths.
This
is
what
the
expected
losses
would
be
and
if
you're
modeling,
all
of
the
reasonable
potential
outcomes-
and
you
are
taking
sort
of
an
average
of
what
all
the
potential
loss
values
would
be
under
different
circumstances.
C
What
you're
really
saying
is
given
a
historical
asset
volatility
given
certain
assumptions
about
the
probability
of
a
price
crash,
etc.
What
am
I
going
to
expect
in
terms
of
my
losses
at
the
end
of
the
day
across
all
the
different
potential
outcomes,
and
then
the
way
you're
arriving
at
those
outcomes
is
by
saying
within
each
of
those
iterations
in
that
simulation,
you've
produced
a
log,
normal
price
path,
because
there's
really
no
better
intermediate
alternative.
Does
that
make
sense.