►
From YouTube: Governance and Risk Meeting: Ep. 44
Description
In this call we will be joined by a special guest in the form of Alex Evans from Placeholder who will discuss a recent paper he’s released to the community.
Governance Segment
Richard Brown:
- Workflows and emergent process.
Risk Segment
- Collateral Risk: Alex Evans 1: ‘A Ratings-Based Model for Credit Events in MakerDAO’ presentation
- Monetary Policy: Vishesh Choudry: State of the Peg
We'll then spend the rest of the time in a general Q&A session.
Discussion Thread: https://forum.makerdao.com/t/scientific-governance-and-risk-w-special-guest-alex-evans-thursday-july-18-9am-pst-4-00-pm-utc/141
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
A
I
also
want
to
keep
things
quiet
as
far
as
governance
goes
because
I
don't
have
a
lot
to
go
over
and
that's
because
there's
not
a
lot
happening
in
governance.
There's
a
fantastic
amount
of
things
happening
in
governments,
and
all
of
that
is
going
to
be
we're.
Gonna
be
carpet
bombing
these
meetings
over
the
next
couple
of
weeks
of
some
presentations.
Some
requests
for
comments
with
some
some
new
form
debates
about
ways
that
were
we're,
hoping
the
community
can
come
together
and
help
us
to
optimize
this
process
because
believe
it
or
not.
A
Mcd
is
a
real
thing,
and
it's
coming
soon,
so
we
have
a
lot
of
work
to
do.
This
is
one
of
the
reasons
why
we
did
so
hard
for
us
to
get
pinned
down
for
a
date
on
what
an
MCD
happens
is
because
this
is
a
two-stage
process.
Basically,
the
first
stage
is
the
maker
foundation
puts
all
the
pieces
in
place.
A
Many
many
many
people
get
all
their
work
finished
and
then
the
work
depends
on
the
community
and
largely
people
show
up
in
this
room
the
people
there
showing
up
in
the
Governance
forum,
but
we
have
to
rely
on
the
community
to
help
us
do
a
great
deal
of
work.
We
need
to
evaluate
these
assets,
we
need
to
talk
about
the
processes
and
the
policies.
We
need
to
talk
about
what
the
voting
cadence
looks
like.
A
A
Are
they
talking
about
now,
so
I've
posted
a
few
different
links
in
the
group
chats
one
of
them.
The
discussion
thread
for
this
call
will
continue.
The
discussion
there
after
this
has
ended.
David
will
provide
us
with
a
summary
of
all
the
salient
points,
there's
a
link
also
to
the
thread
for
Alex's
model
and
there's
also
a
link
to
something
that
long
for
wisdom
is
turning
into
a
forum.
Rockstar
for
us
has
been
posting.
Is
this
the
first
to
the
second
incarnation,
I'm
hoping
it's
the
first
of
many
I
guess.
A
Yeah
we're
debating
whether
it's
weekly
or
in
forever
so
anyways,
it's
very
valuable.
Ten
thousand
foot
view
of
what's
happening
in
the
forum's
because
the
forum's
are
taking
off
in
ways
that
I
can
only
have
hoped
for
lots
of
really
interesting
government
stuff
happening
in
there
over
time.
We're
going
to
find
I
believe
that
governments
happens
in
that
form.
A
Basically,
and
then
we
talk
about
what
happened
in
that
forum
in
these
calls
when
we
resurface
some
things
and
we
prepare
the
community
for
signals
and
we
do
some
educational
materials,
but
largely
the
heavy
lifting
happens
in
those
forums.
So
if
you
haven't
gone
I
advise
you
to
check
it
out,
tons
and
tons
of
valuable
contents,
I'm
hoping
that
I'll
have
enough
bandwidth
to
get
caught
up
with
it.
Maybe
on
the
weekend.
I
want
to
talk
about
very,
very,
very
briefly
about
something
I've
discussed
in
last
week's
meeting,
and
that
is
emergent
process.
A
We
have
lots
of
things
to
do.
I've
already
touched
on
that
a
bit,
but
lots
of
things
to
do
lots
of
people
that
need
to
do
it
and
we
need
to
figure
out
how
that
works,
and
happily
the
that's
already
been
figured
out
largely
because
we're
not
the
first
distributed
team
on
the
internet
that
needs
to
coordinate.
C
A
The
open
source
the
tools
are
available,
and
so
this
is
one
of
the
things
that
I
want
to
start
discussing
more
and
more
about
how
governance
works.
How
these
calls
work
other
forms
work,
how
the
Community
Development
Group
in
general
works
and
how
that
works
is
open-source.
The
heavy
lifting
has
been
done
here,
so
we
need
to
start
internalizing
this
idea
of
open-source
governance
and
just
something
that
we'll
get
into
a
bit
more.
A
But
when
it
comes
to
the
mechanics
of
how
a
distributed
teams
that
people
work
on
the
Internet's,
especially
in
our
space,
it's
all
been
nailed
down.
So
people
talk
about
things
and
forums,
they
talk
about
things
and
chats
they
get
together
in
github,
basically
for
better
for
worse,
that's
that's
what
we
have
and
then
they
do
their
work
and
they
set
up
a
project
they
put
in
some
issues.
A
A
So
we
have
these
tools
available
to
us
so
as
we
increase
our
efficiency,
and
these
calls
are
in
the
government's
process
as
we
debate
these,
these
workflows
in
the
forums
we're
going
to
get
closer
and
closer
to
aligning
with
what
the
rest
of
the
people
in
the
space
student
that
model
I'm
hoping
looks
like
this.
We
have
a
few
different
venues
and
those
venues
are
very
clear
purposes.
We
have
a
forum,
a
forum
is
where
the
debate
happens
and
where
input
is
gathered
and
where
documents
are
initially
drafted
in
hammered
out.
A
A
A
For
us,
it's
a
flat
file
document
store,
which
versioned
its
public
has
an
audit
trail
has
all
of
these
things
that
we
need
in
the
governance
space
and
then,
what's
that
that's
been
placed
in
that
canonical
resource,
anybody
in
the
ecosystem
is
free
to
fire
up,
Gatsby
or
any
other
cool
to
ingest
that
in
their
own
websites
and
their
own
portals
or
in
their
own
tools
or
for
whatever
purposes
they
like,
and
so
we
have
this
cycle
of
requests
for
proposals.
Our
proposal
comes
out
from
some
organization
or
group
of
people.
A
Then
we
move
into
our
quest
for
comments
fares
and
then
we
move
into
this
version
control
committing
phase,
and
then
we
display
that
data
in
a
presentation
layer.
Someone
will
be
digging
into
this
a
lot
more
over
the
course
of
the
next
few
weeks,
but
just
want
to
set
the
stage
for
that.
I
am
going
to
stop
talking
very
very
soon.
So
if
you
have
a
question,
please
type
that
question
in
that
group
chat,
David
and
I
or
I
will
hopefully
get
to
it
in
an
appropriate
moment.
We'll
ask
that
question
for
you.
A
If
you
don't
have
access
to
a
microphone,
if
you
have
a
microphone,
please
pipe
up
and
ask
a
question
whenever
you
like
feel
free
to
interrupt
us
or
click
on
the
discussion
thread
for
this
call
and
if
you
want
to
have
a
deeper
discussion.
Just
add
your
question
to
that
discussion
thread
and
we
will
dig
into
it
over
the
course
of
the
next
week.
I
am
going
to
stop
there
and
Alex
I.
Think
I
want
to
potentially
turn
this
over
to
you
right
away.
If
you're
prepared.
C
A
C
C
C
C
C
Then
I'm
gonna
give
a
brief
overview
of
the
model
and
some
of
the
assumptions
behind
it
then
I'm
gonna
provide
an
example
of
how
to
estimate
parameters
in
the
model
and
then
I'm
gonna
give
a
example
of
how
to
apply
the
estimates
from
step
three
in
a
risk
management
example.
Okay,
so
some
context
to
the
motivation
here,
so
another
I
think
about
it
too.
C
I,
don't
think,
there's
a
need
for
a
lot
of
context,
I
think
Cyrus
vishesh
and
some
of
the
members
on
this
call
have
done
a
pretty
good
job
of
setting
the
framework
on
you
know
what
risk
is,
how
we
should
think
about
risk.
We
should
model
risk.
Where
are
some
potential
frameworks
that
we
can
use
to
formalize
our
understanding
over
it?
So
I,
don't
wanna,
repeat
any
of
that.
I
think
everybody
here
at
least
is
aligned
as
we
as
we
are
and
believes
that
you
know
the
risks
and
maker
are
significant.
C
They
are
important
and
that
as
community
members,
we
need
to
think
systematically
and
rigorously
about
risk
and
debate
the
best
ways
to
manage
risk,
and
so
there's
ways
in
both
the
academic,
literature
and
an
industry
that
we
think
systematically
about
risk
and
model.
A
SARS
has
been
a
good
job,
presenting
a
good
number
of
those
models
in
the
last
couple
calls.
Maybe
one
thing
that
I
can
do
here
is
give
a
little
bit
of
color
or
not
I'll
just
do
that
noise
in
European
background.
C
C
They
think
about
they're
built
on
a
theory
in
which,
if
it's
a
state
machine,
and
so
they
think
about
the
world
in
terms
of
states
and
transactions
representing
transitions
from
one
state
to
another,
and
would
be
models
allowed
to
do
that,
they
incorporate
this
idea
of
state
and
state
transition
very
naturally,
and
intuitively
with
very
few
assumptions.
The
second.
D
C
D
C
Models
are
design
historically
for
the
types
of
into
instruments
that
had
state
dependent
payouts,
and
so
when
we
come
to
NPR,
we
really
care
about.
Let's
say
how
long
it
stays
open
or
whether
alone
is
big,
nor
is
light
or
ends
up
in
some
other
state,
and
so
that
is
a
perfect
application
of
that
kind
of
framework,
because
it's
built
for
those
kinds
of
derivatives
and
financial
instruments
that
have
these
statements
and
then.
C
Are
really
adaptable
in
modular
and
it's
easy
to
change
their
assumptions
without
really
messing
too
much
up,
so
you
can
play
with
it
and
create
new
assumptions
of
your
own
and
see
what
conclusions
the
model
of
public
conclusions
the
model
change,
depending
on
how
you
alter
the
assumptions?
Okay,
so
without
further
ado
getting
into
what
those
assumptions
are.
C
That
was
good,
yeah,
okay,
maybe
that's
better.
So
there
are
so
by
the
way,
as
I
said
in
the
last
one.
Each
of
these
assumptions
I'm
listing
some
assumptions
here.
Every
one
of
these
assumptions
can
be
changed
and
can
be
altered
and,
in
fact,
I
think
it's
a
good
exercise
to
try
and
go
in
and
tweak
some
of
these
and
see.
So
we
can
have
really
any
unit
of
analysis.
Here.
We
use
individual
drug
transactions.
C
We
can
have
a
discrete
state
space
continuously
spaced
this
state
space,
a
bigger
one,
smaller
one
actually
in
the
next
slide,
I
think
I'll
contract
the
state
space
a
little
bit.
It
can
be
marked
off
and
on
Marko
homogeneous
non
homogeneous
and
we
continuous
time
discrete
time
or
whatever,
and
so
that's
really
one
of
the
core
intents
of
the
model.
So
the
way
that
this
model
thinks
about
maker
is
and
I'll
try
to
explain
it
as
simply
as
possible.
If
you
don't
understand
something,
it's
my
fault,
not
yours!
C
You
stopped
me
so
the
way
this
model
thinks
about
maker
is
it
thinks
about
makers,
a
collection
of
loans
primarily
and
the
way
that
we
describe
the
global
behavior
of
maker
is
by
looking
at
the
emergent
properties
of
that
pool
of
loans.
So
the
first
thing
that
we
need
to
do
is
that
we
need
to
find
a
model
to
explain
and
predict
how
loans
will
go
hey
across
time.
C
Okay,
so
the
first
thing
that
we
need
in
order
to
do
that
is,
we
need
to
define
what
a
loan
is,
and
so
that's
what
I
mean
here,
but
unit
of
analysis.
So
here
a
loan
is
defined
as
an
individual
draw
transaction.
So
whenever
somebody
goes
to
the
makeup
contract
and
draws
and
died,
we
call
that
a
new
loan.
Now
there
are
other
ways
to
model
this.
We
do
talk
about
some
of
the
advantages
and
disadvantages
of
each
of
them
in
the
actual
paper
that
we
have
on
placeholder
top.
C
You
see
where
you
could
use
CDP's
or
you
could
use
dye
I'm
looking
into
those
Tomatoes
I
can
go
ahead
and
read
about
those
there,
but
just
remember
that
these
are
individual.
Draw
transactions
now,
just
as
in
if
you've
ever
used
a
CP.
You
know
that
when
you
draw
when
you're
creating
you
Tiki
or
draw
some
amount
of
dye
you're
automatically
in
a
safe
State,
and
so
your
CDP
is
totally
safe
and
then
at
any
given
point
in
time
the
loan
could
be
in
one
of
horse
days.
C
It
could
be
safe
where
it
started,
it
could
be
unsafe.
In
other
words,
it
could
be
liable
to
being
liquidated
because
it's
below
150%
collateralization
threshold
and
then
it
could
be
white
or
it
could
be
bitten,
and
so
these
two
states
like
and
bitten
are
what
we
call
in
this
model
absorbing
states.
In
other
words,
if
the
loan
is
white
or
it's
been,
the
cat
go
back
to
safe
or
unsafe,
it's
stays
in
those
states
forever.
C
You
don't
need
to
really
worry
about
these
Markov
and
continues
time
assumptions
here
are
they're
discussed
in
the
paper
as
well,
but
it's
important
to
remember
that
they
exist
and
are
important.
Sometimes
okay,
so
here
in
the
top
right,
I
have
a
diagram
of
the
state
space
and
the
kinds
of
transitions
that
are
allowed
between
different
states.
So
you
can
go
from
safe
to
unsafe.
You
can't
go
from
safe
to
bidden.
You
have
to
go
through
unsafe
first,
but
you
can't
go
from
safe
to
white
from
unsafe.
C
You
can
go
to
pretty
much
any
state
and
then
you
can
go
once
you
get
to
the
white
ribbon.
You
see,
there's
no
arrows
linking
those
states.
That's
because
they're
absorbing
so
up
here
will
be
what
these
aren't
aren't
transition
probabilities
is
you
some
people
that
have
seen
discrete-time
markov
chains
would
be
familiar.
A
C
Their
what
are
called
transition
rates,
what
these
rates
allows
you
to
incorporate
a
notion
of
probability
that
probability
transition
from
one
state
to
another,
but
they
also
incorporate
a
notion
of
time,
and
the
reason
for
that
is
is
we
actually
really
care,
as
NPR
holders
and
as
community
members
about
the
amount
of
time
that
alone
spends
in
a
given
state
right?
So
if
a
loan
closes
having
been
open
for
a
month,
that's
very
different
to
a
closing
after
it
being
open
for
year.
Presumably
latter
case
it
grew
significantly
more
stability
fees.
C
So,
hopefully,
that's
clear
here
right
below
we
describe
how
these
different
transition
rates
are
calculated,
so,
first,
organizing
this
matrix
call,
the
generator
matrix
is
just
it
can
be
a
way
to
organize
the
information.
That's
the
best
way
to
think
about
it
right
now.
So
here
we
see,
for
example,
rate
from
safety
on
say:
is
this
qsu
from
safety
lights?
This
cube
SW
and
the
way
these
are
defined
is
the
diagonal
elements,
oh,
and
by
the
way,
white
and
pitting
are
all
zeros
right.
C
C
It's
just
the
sum
of
the
transition
rates
to
say
unsafe
light
and
bidden,
so
the
transition
rate
out
of
state
is
a
transition
to
you
can
only
transition
to
three
states,
and
so
the
sum
of
those
transmission
rates
is
that
present
you're
ready
to
get
here.
It's
just
convention
to
do
the
negative
of
it,
but
really
you
ignore
the
minus
sign,
that's
their
transition
rate
out
of
the
state.
So
these.
A
C
Diagonal
elements
incorporate
this
notion
of
probability,
and
so
you
take
the
transition
rate
out
of
the
state
and
multiply
it
by
the
probability
of
going
to,
let's
say
the
unsafe
state,
and
then
you
get
transition
rate
to
from
the
stage
to
the
unsafe
State.
Is
that
clear?
Does
that
make
sense,
yeah.
C
C
So
there
is
a
spot
for
it
safe,
bitten,
technically,
and
we
know
this
in
the
paper
also
very,
very
good
question.
This
should
be
zero
now.
The
reason
I
keep
it
here
is
because,
if
you
look
at
smaller
time
intervals,
sorry
larger
time
intervals
it
might
show
up
is
like
right,
something
goes
and
say
forbidden,
but
actually
went
through
own
safety
you're.
Just
not
noting
it
because
your
time
it
was
too
big,
so
I
keep
it
there.
For
that
reason,
that's
a
really
really
good
question
anything
else.
C
Okay,
so
how
do
we
use
this
model?
So
one
of
the
ways
and
I
didn't
mean
to
scare
you
with
all
these
formulas
here,
so
just
ignore
them.
I
want
to
want
to
note
just
one
thing
with
the
slide
is
every
single
one
of
these
formulas
that
represent
either
global
or
local
states?
You
can
compute
with
just
these
six
assumptions
and
actually
just
as
Cyrus
just
noted,
it's
actually
five.
You
only
need
to
give
you
these
five
rates
and
then
anything
here
you
can
compute
directly
so
with
individual
loans.
C
C
Notice,
the
parsimony
of
the
model
with
very
few
assumptions
you
can
say:
what's
the
average
time
lunch
stays
open?
What
is
the
probability
that
ends
up
in
these
peas,
or
are
these
things
here
and
then,
with
additional
assumptions
about
the
birth
process
ie
the
rate
at
which
new
loans
are
created
and
the
expected
size
of
each
loan?
You
can
now
start
to
project
things
like
at
any
arbitrary
point
in
time,
T
what
how
many
loans
should
I
expect
to
be
opened
or,
in
the
very
very
long
run
equilibrium
of
the
system.
C
At
what
level
does
the
number
of
log
statements
or
what
is
the
total
equitation
fees
between
now
and
some
arbitrary
point?
T?
Let
me
hear
from
now
how
many
stability
caves
do
I
get
between
year
2
and
year
5,
and
you
get
that
from
this
formula
here.
So
each
of
these
formulas
is
basically
these
five
assumptions,
these
five
rates
that
we
had
in
the
previous
slide
it
just
these
two
extra
parameters,
so
you
can
get
really
really
far.
You
can
get
good
estimates
of
these
parameters.
C
C
So
how
do
we
actually
go
about
making
these
estimations
and
I
will
get
into
into
a
lot
of
detail
on
the
statistics
here,
one
whose
time
constraint
to
do
before
there
is
a
note
in
the
model
that
includes
some
work
that
Jeff
has
done
was
with
a
Python
code
that
estimates
the
empirical
transition
matrix
from
on
data.
Here's
another
approach
that
include
this
included
in
the
art
code
that
accompanies
the
model
on
playschool
there,
dot
BC
and
basically
tries
to
use
covariance
to
estimate
the
conditional
transition
rates
based
on
different
values
of
the
covariance.
C
C
Just
simplifies
things:
it's
not
necessary
that
you
do
this,
but
it's
just
a
way
to
convey
the
inside
a
little
bit
more
clearly,
and
so
here
we
just
need
an
estimate
to
transition
weights
and
then
everything
else
kind
of
falls
out
from
those.
So
we
have
this
open
to
bitten
transition
rate
and
we
estimate
that
conditional
on
the
value
of
this
curve
area
for
the
collateralization
ratio-
and
this
is
a
factor
variable
that
is
coded
zero.
C
If
it's
a
collateralization
ratios
about
two
hundred
eighty
percent,
it's
coded
one
it's
between
250
and
280,
and
then
it's
coded
to
of
its
below
250
and
so
the
assumption
being
that,
as
the
collateralization
ratio
increases,
the
probably
rate
at
which
loans
are
bitten
also
increases.
Similarly,
we
go
and
estimate
this
open
to
white
break
and
that's
estimated
based
on
stability
fee.
So
again
the
hypothesis
is,
as
its
employee
peep
goes
out.
Four
more
people
are
like
screw.
This
I
don't
want
to
keep
my
gilben,
and
so
they
bite
them.
C
Okay,
and
these
you
can
think
of
this
sort
of
as
a
regression
table.
It's
a
little
bit
different
to
go
into
some
of
the
intricacies
in
the
actual
paper,
but
the
idea
here
is,
for
example,
this
to
Billy
feed.
You
see
this
seventy
two
point
four
year.
Seventy
two
point,
three:
nine.
So
if
we
were
to
increase
disability
feed
by
a
hundred
percent,
not
multiplicatively
but
add
a
hundred
percent
to
disability
by
one,
we
would
see
a
increase.
C
We
the
model
predicts
an
increase
in
the
rate
at
which
loans
are
white
by
seventy
two
point
by
starting
a
fact
that
seventy
two
point,
four,
which
means
that
all
the
loans
are
printed
to
be
wiped
out
of
the
system
within
a
day
or
something
like
that
and
then
similarly
open
to
bid.
You
see,
for
instance,
the
the
rate
of
transition
to
be
e
to
the
biggest
eight
when
the
cloud
was
a
generations
below
two
hundred.
Fifteen
percent
is
fourteen
point
three
times
higher,
so
significantly
higher.
Okay,
so
questions
on
this
I
have.
C
E
C
C
C
All
right,
good,
good
question,
okay,
so
what
can
we
do
with
this?
So
we
one
application
of
these
is
in
risk
management.
So,
what's
great
is
Cyrus
has
already
done
all
the
hard
work
of
explaining
all
this,
so
Cyrus
has
explained
expected
losses,
unexpected
losses
and
so
forth.
Here
we
focus
on
what's
called
the
conditional
loss
distribution
so
conditional
on
the
values
of
the
covariate?
Why?
C
For
a
given
pool
of
loans,
what
percentage
of
them
do
we
expect
to,
for
instance,
be
bitten?
Is
what
we're
trying
to
do
here,
and
this
is
what's
called
scenario-
stress
testing,
sometimes
so,
let's
say
for
above
280
this.
You
can
see
this
green
line.
So
the
probability
of
really
really
small
losses,
small
probability
of
large
inch
losses,
all
the
way
to
very
large
boxes
is
extremely
small.
You
have
this
peak
here,
whatever
it
is
around
17
and
that's
probably,
you
know
that
one
standard
deviation
in
there.
C
You
get
most
of
your
the
creations
happening
and
then,
on
the
other
hand,
if
the
cloud
station
reaches
below
250%,
you
see
that
the
probability
of
small
losses
is
pretty
small,
but
very,
but
these
quite
large
losses
are
pretty
probable,
and
so
you
just
impact
a
little
bit
clearly
here.
The
probability
that
your
loss
will
be
contained
at
a
sixty
percent
level
is
quite
small,
but
the
probability
that
they
are
quite
large
they're
contained
at
the
85
like
they
exceed
that
85.
This
is
quite
high.
C
So
that
is
that
that's
all
I
had
to
talk
about
today.
I
want
to
say
thanks.
The
chef's
did
a
lot
of
really
great
work
here,
not
just
on
the
data,
but
a
lot
of
the
ideas
that
went
into
the
model
so
cute
next-
and
this
is
just
one
attempt
I-
think
it's
just
the
first
and
most
imperfect
attempt
at
a
community
ritz
model,
I,
really
hope
and
believe
that
these
will
get
better
over
time.
I
will
be
available
to
answer
questions
about
these
on
the
make
a
forum.
A
C
Reach
out
directly
allocate
Kevin's
on
Twitter
I,
think
I'm
also
Alex
a
show.
That's
on
the
maker
chat,
so
you
can
reach
out
to
me
there
as
well
I'll.
Try
to
make
time
to
ask
the
collector
and
one
other
thing
here
is:
we
at
placeholder
will
be
putting
together
a
little
bit
of
a
more
formal
risk,
discussion
or
Risk
Committee
that
will
include
several
it'll,
be
smaller
sort
of
foreign
will
share
some
of
the
results
from
that,
as
we
start
to
do
that,
probably
around
the
launch
of
MCD
and
yeah.
That's
all
for
me
today.
B
C
Just
by
the
actual,
no,
in
order
to
get
the
actual
quote,
unquote
loss.
You
have
to
have
an
additional
assumption
about
what
amount
what
percentage
of
CDP's
has
been
below
what
level?
So
you
need
to
have
a
distribution
over.
Let's
call
the
recovery
rate,
and
so
all
the
CDs
are
have
been
commenting
below
on
their
percent
and
then
also
what
is
the
recovery
rate?
Should
they
be
below
2%?
And
then
you
can
actually
talk
about
dollar
or
dollar
amounts
for
how
much
the
MPR
holders
to
do.
That's
a
really
good
question.
C
So
if
you
look
at
the
data
that
vishesh
was
kind
enough
to
contribute,
it
also
includes
the
collateralization
ratios
at
bike,
every
loan
that
was
big,
and
so,
if
somebody
were
to
do
that
exercise
and
try
to
do
it
well,
that
would
be
where
I
would
start,
because
it'll
tell
you
what
the
collateralization
ratio
of
that
different
loved
ones,
and
so
that
is
one
way
to
do
it.
The
only
issues
you
really
haven't
under
collateralized
loans,
and
so
all
of
that
is
kind
of
destination.
So
you
can
try
to
project
based
on
that
distribution.
C
Well,
if
we
had
a
whole
bunch
more
like
what
is
the
quality
that
it
ended
up,
the
lower
certain
number,
that's
probably
how
I
would
do
it?
It's
just
we
haven't,
we've
been
quite
conservative
in
terms
of
collateralization
ratios
and
the
liquidation
systems
works
sufficiently.
Well,
we
haven't
seen
great
examples
of
that
happening,
and
so
it
really
is
an
exercise
in
conjecture.
D
Say
yeah,
that's
a
great
point
on.
On
the
estimation
side,
I
mean
I
think
right
now
all
risk
models
are
in
a
state
of
trying
to
deal
with
the
lack
of
meaningful
historical
data
and
actually
I
think
one
of
the
widen,
if
is
an
intentional
design,
choice
or
anything,
but
something
that
we
should
be
looking
for
in
design
choices
is
what
is
the
easiest
way
or
what
is
the
easiest
model?
D
That'll
help
us
abstract
some
of
those
those
necessary
estimations,
as
you
like
to
put
them
and
put
them
into
the
model,
because
so
that's
that's
I'm
trying
to
get
eyes.
So
if
we
wanted
to
input
some
sort
of
manual
or
expert
adjustment
into
into
this
model,
do
you
think
that's
a
is
that
is
that
doable?
D
Is
that
something
we
can
say
that,
based
on
our
understanding
of
the
underlying
collateral,
or
would
you
be
more
comfortable
just
using
this
purely
for
collateral
types
that
have
like
years
and
years
of
historical
data
like
do
you
think
this
will
work
equally
well
for
ERC
20
tokens?
Or
would
you
say
this
is
better
for
security
tokens
and
longer
historical
assets?
Well,.
C
E
C
E
C
So
for
security
tokens
that
may
be
a
little
bit
different
used
in
defi
as
much
while
with
other
tokens.
We
could,
for
instance,
look
at
compound
loans
and
how
those
have
behaved
over
time
and
whether
they've
been
liquidations
there
and
how
that
system
has
behavior
response
to
it.
So
we
can
see
some
examples.
C
Really
really
recent,
and
so,
for
example,
if
you
look
at
the
collateralization
ratio,
one
of
the
reasons
why
it's
a
fact
one
supposed
to
and
continuous
collateralization
ratio
variables
is
really
write
each
one
of
those
numbers.
You
have
your
sample
sizes
like
to
write
very
really
do
much,
and
so
you
split
it
into
just
three
big
buckets
of
collateralization
ratio.
It's
like
pretty
low
resolution,
it's
about
as
good
as
you
can
do
in
order
to
actually
get
some
significance
and
talk
about
some
results.
C
D
And,
and
actually
something
else
that
I
was
talking
with
vishesh
about
the
other
day,
was
that
the
there's
transition
probabilities?
Do
you
think
they
would
be
similar
across
different
collateral
types
or
I
mean
certain
cloud
types
they'd
be
they'd,
be
completely
different
right,
certain,
like
binary
type
of
assets,
where
one
day
they're
just
in
a
collapse
to
0
or
whatever,
but
but
for
a
particular
set
of
standard
assets?
Do
you
think
that
those
transition
probabilities
will
expect
to
be
the
same
across
different
types.
C
C
Good
job
at
I'm,
just
taking
this
directly
from
your
presentations,
one
is
the
price
class
at
the
collateral,
and
then
second,
is
the
borrower
behaviour
associated
with
borrowing
against
that
flower.
So
do
you
behave
differently
when
you're
borrowing
against
the
house
and
a
person
when
you're
borrowing
against
me
and
I
pause?
This
would
be
yes
like
that.
That
is
it's
a
different
type
of
borrow
or
the
different
kind
of
mindset
in
different
types
of
behaviour
associated
with
blindly
and
then.
Secondly,
the
price
path
of
those
two
assets
were
very
very
different.
C
What
is
more
volatile
than
the
other
right
differently,
it
was
a
content
to
the
transition
rates
would
be
different
numerically
their
estimation
shouldn't
be
that
different,
because
really
the
system
cares
about
the
state
and
it
says
what
state
are
we
in
right
now
and
what
is
what
states
could
be
being
in
the
future?
And
so
all
you
need
to
do
is
analysis
as
an
analysis,
step
back
and
say:
okay,
what's
the
probability
and
we
get
into
that
state.
D
C
E
B
E
Yeah
sorry
I
was
just
saying
it
has
to
follow
up
to
that
there,
rather
a
clarification
right
so
to
what
extent
is
the
rate,
the
relative
rate
between
different
states,
determined
by
behavior
versus
on
aggregate,
the
amount
of
liquidation.
You
would
expect
a
function
of
asset
price
volatility
right.
So
how
do
you
separate.
C
E
So
how
do
you
separate
the
effects
of
behavior
of
CDP
owners,
for
example,
rebalancing
when
asset
prices
go
up
or
shoring
up
when
they
succeed
liquidations?
How
do
you
separate
behavior
versus
asset
price
in
terms
of
this
is
something
that
happened
with
eath,
or
this
is
something
that
CDP
owners
tend
to
do,
and
so
that's,
where
I
started,
to
think
what
you
variance
yeah
I
mean
that's
where
I
started
to
think
about
the
relative
transitions.
You
know
between
one
state
versus
another
versus
the
overall
aggregate
amount
of
liquidation,
for
example,
that
you
would
expect
right.
C
So,
okay,
so
there
is
a
brief
note
at
the
end
of
the
paper
talking
about
another
information
of
NPR.
One
of
the
ideas
that
mentioned
of
these
this
type
of
model
is
very
good,
and
actually
the
original
paper
that
it
sort
of
derives
from
is
references.
This
idea
of
derivatives
with
state
dependent,
payoffs
and
that
should
immediately
ring
about
it,
sounds
a
lot
like
MTR.
It's
a
type
of
you
can
think
of
it.
C
C
You
would
have
to
have
a
way
to
figure
out
from
actually
traded
loans,
with
interest
rates
that
are
free-floating,
unlike
the
maker
to
strip
out
the
risk
premia
and
then
apply
it
to
take
with
the
empirical
transition
matrix
that
we
discussed
on
a
presentation
and
turn
it
into
risk
neutral,
matrix
and
then
based
on
that
matrix,
go
back
and
compute
what
the
true
risk
neutral
price
event
AR.
It's
now
that's
a
theoretical
exercise,
as
I
say
in
there,
because
we
don't
have
that
access
to
those
free
floating
loans.
One.
C
Interesting
that
this
type
of
modeling
allows
us
doing
some
of
the
models
that
are
assuming
presenting
over
recent
weeks.
Allow
us
to
do
is
that
actually
the
valuation
exercise
and
the
risk
management
exercise
kind
of
converging
to
one
thing
that
we're
trying
to
think
about:
how
do
we
maximize
our
risk
adjusted
returns
and
they
are
holders,
and
it
turns
out
that
the
actual
answer
to
that
it's
similar
the
answer
to
what
is
the
best
way
to
manage
risk
in
the
system.
D
C
So
in
the
JLT
paper
he
describes
this
technique
whenever
you
go
out
and
find
quick
trade
advices
for
let's
say
different
corporate
bonds
or
corporate
liability
to
different
great
way
you
go
by
and
then
based
on
those
and
the
actual
transition
probabilities.
You
do
this
recursive
procedure
where
you
strip
it
I
won't
go
into
the
details
of
this.
It's
described.
You
just
got
to
super
well
on
the
paper
and
then
you
pull
out
these
risk
premia.
E
C
Same
thing
as
a
natural
transition
matrix,
but
with
risk
mutual
probabilities,
and
then
he
take,
for
example,
under
the
risk-neutral
measure,
the
problem
with,
like
all
your
discounted,
bring
stability
fees
of
liquidation
feeds
less
whatever
you're
gonna
have
to
make
up
these
then
and
have
a
value
from
there.
Now
that's
a
theoretical
exercise.
It
is,
you
would
need
loans
that
are
extremely
similar
to
CDs,
which
don't
exist
in
publicly
traded
form
right
now,
so
you
have
to
have
you
know,
let's
say
the
single
collateral
by
and
have
to
be
hundred
fifty
percent
otherwise
would
eat.
C
They
would
have
to
have
a
keeper
reward
and
like
a
liquidation
penalty
or
ten
percent,
they
don't
have
to
the
reward
keepers.
Knowing
all
these
things,
rights,
the
Dharma
loans
and
compound
loans
are
kind
of
similar,
but
they're
not
similar
enough,
where
you
can
make
that
kind
of
risk,
suitable
argument
to
say
well
under
the
ways
that
the
market
is
pricing
risk.
In
this
scenario,
we
apply
that
same
risk
premium
here.
This
is
the
value
that
we
get.
We
can't
really
do
that.
C
A
Alright,
thanks
for
that,
Alex
I,
there's,
probably
a
lot
more
questions,
I'm,
not
sure
what
your
schedule
looks
like,
but
we
need
to
get
into
vishesha
lytx
for
the
next
15
minutes.
But
if
you
want
to
hang
out,
maybe
we
can
have
some
questions.
If
not,
we
can
bombard
you
in
the
forums,
but
thank
you
for
that.
That
was
enormously
valuable
thanks.
E
Alright,
so
just
to
bring
back
to
some
some
numbers
after
all
that
theoretical
goodness
so
this
far
is
the
the
dye
price
I
mean
so
right
now
volumes
treating
it
like
5.6
million,
it's
been
Oh
considerably
higher
than
that
over
the
past
couple
days.
So
the
the
spread
is
kind
of
waffled
around
98
99
cents
for
the
last
few
days.
So
I'll
just
touch
on
that,
but
I
mean
I.
Think
the
big
picture
is
things
are
doing.
E
Then
there
was
a
bit
of
this
dip
just
at
the
beginning
of
July
and
the
slight
trend
down
with
the
17.5%
civility
fee
increase
in
the
18.5%
sleepy
increase,
it
had
started
to
look
like
things
might
be
recovering,
then
eath
decided
to
start
to
make
some
same
price
movements,
and
so
there's
this
sort
of
double,
overreaction
and
and
I
do
kind
of
want
to
touch
on
this.
This
is
a
topic
in
the
chats
for
quite
a
while
is
how
did
we
get
into
the
scenario
and
like?
E
Is
it
actually
a
problem,
or
is
it
a
temporary
state
of
being,
and
so
with
with
the
17.5%
increase?
I
said
with
the
7.5
percent
decrease.
There
is
kind
of
initially
this
narrative
that
at
19.5%,
the
state
of
the
system
was
too
expensive.
I
was
trading
above
a
dollar
at
times,
and
so
this
really,
if
you
needed
to
decrease
that
was
probably
a
bit
premature
right.
E
So,
with
that
initial
increase
to
nineteen
point
five
percent,
there
was
some
temporary
push
up
of
the
price
and
then
a
bit
of
a
strong
overreaction
with
the
dye
price
trading
above
$1
for
just
fairly
weak.
And
so
then
it
was
two
successive
decreases,
and
so
that's
when
you
started
to
see
as
I
had
mentioned
at
the
time
more
kind
of
variability,
the
the
price
was
a
little
bit,
looser
though
it
wasn't
necessarily
lower
and
so
you'd
see
a
widened,
spread.
E
I
should
show
the
volatility
of
this,
but
at
the
end
of
the
day
that
started
to
drift
down
with
the
East
price
movement,
and
so
it
wasn't
necessarily
that
the
16.5%
to
really
cause
that
decrease,
but
rather
increase
the
sensitivity
of
the
system
to
what
was
going
on
with
the
eve
price.
So
when
there
is
sort
of
this
sharp
drop
in
dye
price
for
a
few
days,
there
is
again
an
a
strong
reaction
by
the
community.
Okay.
E
Now
we
need
to
increase
the
stability
fee
to
17.5%
and
then
again
to
18.5%
shortly
thereafter,
because
in
the
span
of
four
days
it
did
not
look
like
there
had
been
much
of
an
improvement,
and
so
this
was
again
maybe
sure
that
same
strong
double
reaction
pattern
that
I
guess
is
now
emerging,
which
I
would
caution
against
right.
So
if
you,
if
you
make
changes
thoughtfully
the
first
time,
then
you
don't
have
to
sort
of
scramble
to
unwind
them
the
second
time.
E
That's
that's
one
sort
of
governance
feedback
that
I
will
give
the
the
other
pieces
so
with
the
20.5%.
So
if
you
increase
a
lot
of
people
were
kind
of
confused
that
the
dye
price
didn't,
you
know
immediately
shoot
up
over
a
dollar
and
stay
there,
and
there
were
a
little
bit
confused
as
to
what
was
going
on
and
again.
The
stability
fee
does
not
inherently
fix
the
diet
price.
It
doesn't
inherently
bring
the
dye
price
down.
It
just
improves
the
sensitivity
of
the
system
to
what's
going
on
with
leveraging
behavior
and
by
extension
effects.
E
So
this,
like
just
increasing,
is
really
really
fun.
Five
percent
is
not
inherently
just
going
to
bring
the
diet
price
up,
although
because
what
you
effectively
need
is
people
to
buy
back
that
dye,
which
means
you
need
people
to
be
willing
to
unwind
their
leverage.
The
problem
is
some
people
given
where
the
price
of
eath
is
gone,
may
be
unwilling
to
give
up
on
that
there.
E
You
know
positions
at
this
time
or
they
may
have
been
liquidated
out,
and
thus
they
don't
have
to
buy
any
dye
because
they've
already
been
liquidated,
and
if
those
liquidations
happen
at
sort
of
effectively
lowered
eye
crisis,
then
you'll
have
kind
of
missed
out
on
some
of
that
upward
price
pressure.
So
a
little
bit
of
a
complex
process
was
a
lot
of
moving
pieces,
but
at
the
end
of
the
day,
I
think
the
takeaway
is
even
with
the
20.5%
really
fee.
E
It's
going
to
take
a
little
bit
of
time
for
this
curve
to
slowly
get
pushed
up
with
that
buying
behavior.
So
again,
I
would
caution
against
these
sort
of
strong
over
reactions,
and
just
because
you
know
things
seem
fine
at
the
19.5%
stability
fee
because
he
has
not
been
stressed
in
that
time
doesn't
mean
that
it's
necessarily
time
to
lower
the
civilian
fee
and
vice
versa.
B
E
Forced
to
know
is
I,
don't
think
the
pre-emptive
stability
feed
changes
approach,
which
is
basically
what
you're
getting
at
what's
been
discussed.
A
lot
is
necessarily
scientific
is
what
I'll
say:
I
mean
you
could
certainly
choose
to
try
to
manage
the
system
by
guessing,
what's
going
to
happen
with
the
youth
price,
but
if
you
just
try
to
manage.
E
To
the
level
of
confidence
that
you
have
so
what
I'll
say
is
if
you
think
that
the
system
does
not
have
robust
enough
transaction
going,
does
not
have
robust
enough
holistic
health
in
terms
of
level
of
leverage
amount
of
sticky
demand
amount
of
oversupply,
then
you
should
be
more
weary
of
having
a
lower
stability
fee.
So
I
think
the
problem
is
not
so
much
that
you
have
to
guess
what
each
is
going
to
do.
E
I
think
the
problem
is,
you
have
to
be
mindful
of
how
much
you're
opening
up
the
system
to
fluctuations
Neath
price
based
on
things
like
the
level
of
organic
command
right?
So
if
you
have
a
greater
degree
of
oversupply,
what
that's
effectively
saying
is
demand
is
a
is
a
non
monolithic
concept
right,
so
demand
is
basically
a
curve
of
stickiness,
and
so,
if
you
have
a
lot
of
dye,
that's
been
drawn
out
on
fairly
transient
demand
in
terms
of
people
wanting
short
term
leverage.
E
Then
you
should
be
more
weary
about
having
a
lower
stability
feed
because
one
you're
either
gonna
exacerbate
that
problem
or
to
just
be
mindful,
then,
if
eath
goes
and
decides
to
make
a
significant
price
movement
you're,
you
know
sort
of
flapping
in
the
wind
and
you're
subject
to
whatever
those
market
movements
are
to
a
pretty
significant
degree,
but
if
your
oversupply
is
really
low
right
and
you
have
strong
transaction
volume,
you
have
you
know
high
percentage
of
transaction
to
trading
volume.
You
have
you
know
not
as
much
outstanding
leverage
position.
E
That's
that's
ready
to
be
closed
out.
Then
you
can
be
a
little
bit
more
confident,
and
so
it's
it's
again,
not
so
much
about
guessing
what
each
price
is
going
to
do,
but
being
mindful
of
how
much
you're
exposed
to
it
at
any
given
point
in
time
and
so
like.
This
is
part
of
why
dyes
price
will
always
fluctuate
right,
and
this
is
a
topic
that's
been
discussed
for
quite
a
while
is
dye-
is
never
going
to
sit
exactly
at
a
dollar
the
goal
of
management
of
the
system.
E
E
So
it's
just
it's
kind
of
a
a
management
choice
and
a
level
of
awareness
rather
than
trying
to
preempt,
because
that's
really
hard
and
I
don't
think
you're
gonna
do
that
successfully
Thanks
yeah
so
to
talk
about
some
of
those
behaviors.
So
this
was
just
the
eath
price.
So
for
context,
this
really
started
to
tank
around
July
9th
10th,
which
you
know.
E
If
you
look
at
what
was
going
on
with
some
of
the
trading
volumes,
there
is
a
decent
spike,
then
and
then
a
little
bit
of
a
dip
and
then
huge
spike
a
couple
days
later.
So
this
huge
spike,
although
the
eath
price
was
just
sort
of
consist
continuing,
go
down,
was
unfortunately,
probably
a
lot
of
people
levering
back
up
some
other
people
selling
and
we'll
go
over
to
the
supply
metrics
to
sort
of
highlight
that,
so
this
is
shortly
thereafter
like
that
same
timeframe.
E
So
what
we
saw
was
overall
zoom
back
out
here,
just
real
quick.
So
overall,
this
supply
had
been
consistently
growing
for
quite
a
while
around
that,
like
90
million
92
million
level,
there
was
you
know
a
lot
of
these
problems
with
dye
price
and
it's
really
fee
was
consistently
increased.
During
that
period,
supply
came
down
to
like
82
million,
and
then
the
stability
fee
was
lowered.
As
we
just
talked
about
to
sixteen
point.
Five
percent
and
supply
has
sort
of
started
to
grow,
pretty
consistently
topping
out.
You
know
around
91
92
million.
E
That's
when
we
started
to
have
conversations
about
the
debt
ceiling.
I
think
during
this
time
period
there
was
an
accumulation
of
a
lot
of
that
transit
demand,
a
lot
of
that
leveraging
behavior
and
what
you
should
effectively
call
or
supply,
and
so
that's
where
you
started
to
accumulate
some
of
this
risk,
which
you're
now
starting
to
see
the
effects
of
so
that
a
lot
of
that
transient
demand
kind
of
went
away.
There
was
some
refinancing.
There
was
some
liquidation.
E
There
was
some,
you
know,
closing
of
leveraged
positions
and
then,
unfortunately,
eath
I
think
it
had
skyrocketed.
You
would
have
actually
seen
a
similar
effect
in
terms
of
dye
stability
and
Dyna
Christ.
So
that's
again,
another
reason
why
it's
hard
to
try
to
make
bets
based
on
what
happens
with
eath
price,
because
strong
movements
in
both
directions
can
actually
end
up
having
some
more
effects
and
so
had
the
youth
price
shot
up.
E
You
would
have
seen
a
lot
of
folks
cashing
out
potentially,
but
you
may
have
also
seen
folks
leveraging
out
and
so
it's
it
can
go
either
way.
Unfortunately,
so
yeah
with
the
20.5%
increase
that
supply
had
started
to
come
down,
but
not
cuz,
I.
Think
purely
of
the
stability
fee-
and
this
is
where
correlations
start
to
become
really
difficult,
because
the
stability
fee
was
increased
due
to
instability
and
died,
and
instability
and
I
was
due
to
eath
price.
E
So
if
you
try
to
sit
there
and
just
make
the
correlation
of
stability,
FEMA's
increased
and
then
supply
went
down,
you
might
draw
the
wrong
conclusion.
So
the
supply
went
down
primarily
due
to
those
exogenous
market
factors.
There
is,
you
know,
probably
four
or
five
million
died
that
was
wiped
during
that
time,
but
a
fair
portion
of
it
was
also
due
to
what
was
going
on
with
secondary
lending
platforms
and
a
fair
amount
of
refinancing
to
compound,
for
example.
So
it's
a
combination
of
you
know.
E
First,
your
supply
was
starting
to
go
down
with
some
liquidations.
Then
you
started
to
have
some
people
refinancing.
You
did
have
some
people
I,
think
closing
out
positions,
just
sort
of
giving
up
but
I
think
more,
so
those
people
were
probably
liquidated
out
and
so
the
remainder
probably
just
sort
of
sitting
with
their
positions.
E
So
this
and
I
think
this
funny
enough
ties
in
to
what
Alex
was
presenting
around
those
transition
states
and,
as
part
of
the
reason
now
I'm
glad
that
model
was
out
there
and-
and
you
know,
hopefully,
people
understand
this
concept
a
little
bit
better.
As
this
stability
fee
changes,
you
see
often
a
significant
impact
in
what's
going
on
with
the
circulation
of
debt,
and
so
that's
where
the
the
age
of
debt
is
pretty
decently
impacted
by
what's
going
on
with
the
profitability
of
loans.
E
The
extent
that
refinancing
is
going
on
these
since
the
liquidations
are
going
on,
etc.
So
you
know
with
the
successive
stability
increases
earlier
in
May,
the
I'll
just
go
back
for
context
here.
The
age
of
debt
had
kind
of
consistently
been
rising,
accumulating
a
lot
of
this
tracks,
with
the
sort
of
unpaid
stability
fees
graph,
so
consistently
growing
and
outpacing
extent
to
which
loans
were
being
liquidated
or
paid
back.
E
Then
you
know,
successive
Siviglia
fee
increases
that
age
starts
to
decrease
fairly
significantly
and
then
with
the
16.5%
17,
and
then
16.5%
drops
that
sort
of
started
to
stagnate.
And
then
you
started
to
see
with
a
lot
of
refinancings
and
the
success
of
secondary
lending
platforms
in
this
sort
of
May
June
timeframe.
You
did
see
some
decrease
in
the
age
of
open
debt,
so
a
lot
of
that
was
new
debt
being
created.
E
Yes,
because
the
supply
was
also
growing
during
that
time,
but
also
with
some
slight
uptick
in
the
age
of
close
debt
was
due
to
refinancing
to
secondary
lending
platforms.
So
the
combination
of
factors
what's
really
interesting
now
is
these
two
lines
have
kind
of
converged,
so
you've
actually
got
a
really
interesting,
slightly
decreasing,
steady
state
and
I
say
slightly
decreasing,
because
the
stability
fees
have
now
been
increased,
but
only
for
the
last
couple
of
weeks
and
honestly,
there
was
far
more
liquidation
going
on
in
that
time
frame.
E
So
the
effect
of
that
on
collateralization
ratio
is
pretty
interesting
and
I
actually
next
time
on
graphs,
this
against
the
eath
price.
So
you
can
see
the
impact,
but
basically,
as
the
price
had-
and
we
went
over
this-
you
know
last
week
the
eve
price
when
it
goes
down,
you
see
a
decrease
in
collateralization
ratio,
but
then
also
insert
increase
as
this
counter
behavior
and
so
what's
really
interesting
is
right.
E
Around
this
end
of
July
time
period,
you
actually
had
a
fair
amount
of
collateral
being
added
after
the
dip,
and
so
this
is
exactly
what
we're
talking
about
sort
of
that
counter
effect.
A
lot
of
people
got
scared
from
those
liquidations
and
that
dropped
beneath
price
and
so
I've
actually
added
collateral
into
the
system,
bringing
up
the
cauterization
ratio
of
it.
E
So
this
for
in
secondary
lending
platforms
and
actually
I'll
just
jump
over
real
quick.
So
this
is
something
that
we've
kind
of
touched
on
is
transaction
volume,
so
the
quote-unquote
demand
and
the
extent
to
which
dye
is
moving
around.
Why
it's
moving
around
it's
an
important
question,
so
I've
been
asked.
This
question
from
quite
a
few
people
is:
how
is
the
transaction
volume
of
diverse?
E
But
if
you
look
at
the
total
transaction
volume
on
chain,
that's
more
to
the
order
of
140
million,
and
so
this
is
and
and
I
know,
math
you'd
asked
me
this
question
on
one
of
the
chats
was
a
record
just
just
shy
of
a
record,
so
November
2018,
156
million
is
a
record
but
but
pretty
darn
close
and
so
you've
seen
this
consistent
rise
in
the
transaction
volume
of
dye
since
basically
the
beginning
of
April.
A
lot
of
those
secondary
lending
platforms
had
not
really
taken
off
in
full
force
until
May
June.
E
E
It
you
know
14
million
in
the
last
few
days,
the
amount
that
dye
was
burned
and
minted
a
total
of
like
six
seven
million,
but
the
total
transaction
volume
you
know
roughly
in
the
hundred
40s
and
then
the
amount
of
dye
that
was
moved
over
from
maker
to
compound
was
like
roughly
five
million,
so
even
even
taking
those
kinds
of
transfers
into
account,
there's
still
a
fair
amount
of
activity
over
the
best
month
bites.
So
we
saw
you
know
with
those
dips.
E
Neath
prices
there's
a
fair
amount
of
of
liquidations,
but
you
know
again
nothing
quite
record-setting.
It
was
just
a
30
percent
price
drop,
but
but
with
the
the
way
the
collateralization
ratio
has
been
going.
So
this
was
something
Alex
touched
on
is
that
liquidation
percentage
is
hugely
driven
by
what
this
collateralization
ratio
graph
looks
like
over
time
and
even
though
things
have
kind
of
dipped
and
gone
up,
etc.
Just
compared
to
the
past.
It's
it's
been
huge.
E
It's
or
that
so
I
know
at
times
going
over
so
I'm
just
to
make
one
last
point
here,
which
is
the
borrow
on
secondary
lending
platforms,
has
shot
up
with
all
this
price
volatility,
also
partly
due
to
things
like
the
insted
average
and
things
like
that,
but
also
due
to
the
stability
feet,
and
so
there's
a
lot
of
discussion
around.
What's
the
the
natural
role
in
these
secondary
lending
platforms
in
this
ecosystem-
and
you
see,
the
excess
supply
has
consistently
gone
down
over
the
past
few
weeks
and
the
utilization
rate
I
mean
UI.
E
Dx
is
lower,
but
compound
utilization
rate
is
that,
like
93
percent,
this
makes
total
sense
because,
to
the
extent
that
you
can
get
cheaper
dye
on
secondary
lending
platforms,
you
know
there's
no
reason
why
people
wouldn't
do
that.
What's
really
interesting
is
as
that
excess
supply
dissipates,
then
you
can
actually
say
that
the
supply
levels
are
closer
to
the
organic
demand
levels
and
you
can
be
more
confidently
of
less
oversupply
and
and
so
then,
when
you
start
to
talk
about
what
the
rates
should
be
and
whether
this
ability
should
be
decrease,
etc.
E
I
think
these
are
really
good
indicators
to
look
at
and
I
really
want
to
caution
against
a
lot
of
these
and
I've
been
doing
it
for
months.
Caution
against
the
like
over
simplistic
thinking
of
dye
is
down
increased
ability,
feed
eyes
up,
decreased
ability,
fee
I
think
if
we
get
off
of
that
kind
of
narrative,
and
we
actually
like
wait
more
than
a
week
before
making
changes,
it
will
ultimately
manage
the
system
better.
Okay,.
E
Healthy,
so
there's
not
a
significant
amount
of
oversupply
now
I
think
had
there
been
or
like?
Is
it
good
for
there
to
be
oversupply?
There
was
oversupply?
Is
it
good?
Obviously
not
I
healthy
is
complex.
I.
Think
having
a
buffer
on
secondary
lending
platforms
is
a
good
thing
in
that
it
does
help
protect
the
demand
function
from
a
stability
fee
that
is
potentially
too
high.
E
So
there
should
be
some
buffer,
but
I,
don't
it's
sort
of
like
you
know
you
ask
your
doctor:
should
I
eat
more
salt
and
doctors,
gonna
say
you
get
enough
salt
in
your
diet,
like
I
the
excess
supply
the
oversupply
is
gonna,
be
high
enough
to
give
you
like
enough
of
a
healthy
buffer,
I.
Think
anyways
that
you
don't
need
to
think
about
like
artificially
increasing
it
or
anything.
I
think
you
generally
want
to
manage
it
down,
and
then
there
there
always
be
some
amount
that
exists.
E
If
the
excess
supply
on
dy/dx
and
compound
went
to
zero
tomorrow,
then
I
would
say
it's
really
fee
is
probably
too
high.
You
have
no
oversupply,
and
it's
just
you
know
bone-on-bone
and
you
probably
should
decrease
the
stability
feet,
because
there's
enough
demand
out
there
that
justifies
a
higher
supply.
E
A
All
right,
it's
a
seven
minutes
after
the
hour.
It
was
that
was
a
nice
presentation
from
both
Alex
and
vishesh.
If
it
turns
out
that
there
are
more
questions
or
people
want
to
hang
out
and
just
talk
about
risk,
we
can
do
that
for
like
the
next
half
hour
so,
but
for
people
that
want
to
drop
out.
I
just
want
to
say
thanks
to
Alex
again
for
that
great
presentation.
Thank
you
for
checking
out
the
forums.
Follow
us
we're
transitioning
away
from
reddit.
A
As
I
said
at
the
top
of
the
meeting,
governance
happens
in
the
forum's,
so
please
join
us.
There
there's
a
lot
of
really
smart
people,
saying
really
smart
things
and
then
it's
worth
catching
up
on
and
please
join
us.
There
will
be
a
summary
of
this
call
and
Alex's
slides
will
be
posted
there
as
well.
Sometime
today,.
A
B
E
So
the
like,
the
one
obvious
like
criticism
or
concern
that
I've
heard
is
that
secretary
lending
platforms
are
taking
market
share
away
from
maker,
somehow
and
like
the
people
using
those
platforms
instead
of
opening
CPS
and
so
forcing
maker
into
having
higher
stability
fees,
which,
again,
as
you
can
tell
from
my
description
there,
I
don't
get
how
the
argument
follows.
I
would
say
so
if
that's
the
concern
I,
don't
see
like
a
logical
chain.
That
brings
you
to
that
conclusion.
E
That
they're
taking
market
share
away
from
maker
I
mean
to
the
extent
that
the
rates
are
cheaper
and
I.
Think
this
is
gonna.
Be
an
interesting
point
is
to
the
extent
the
rates
are
cheaper
on
those
platforms,
people
will
and
should
and
I
think
are
now.
Finally,
you
know
waking
up
and
realizing
it
go
to
those
platforms
to
obtain
the
same
asset
for
cheaper
is
the
question
would
be.
Why
does
that
supply
exist
on
those
platforms
and
and
I
think
there
are
two
different
reasons
why
that
supply
exists.
E
One
is
people
who
sort
of
capitulated
their
CPS
and,
like
you
know,
sold
their
died
and
have
no
intent
of
reclaiming
that
guy,
at
least
for
quite
a
while
or
you
know,
it's
the
expense
of
people
are
holding
on
to
die.
For
you
know
some
reason
and
you
know
have
not
decided
to
refinance
but
I,
don't
think
that's
I.
Don't
expect
that
to
be
a
significant
portion
of
the
supply
on
those
platforms,
I
think
will
primarily
be
purchased
die
that
is
being
supplied,
but
the
baura
volume.
E
E
E
I
think
you're
gonna
start
to
see
emerging
use
cases
for
different,
and
so
this
is
gonna.
Things
are
gonna
become
more
differentiated
as
they
go
I
think
about
a
month
ago.
A
lot
of
people
would
have
asked
the
question
of
like.
Why
would
I
use
one
platform
versus
the
other
and
what's
the
difference
but
I
think
those
are
going
to
become
increasingly
differentiated
and
so
like
dy/dx?
What
you
see
is
you
see
generally
higher
rates,
although
those
have
actually
come
down
fairly
significantly,
but
they're.
E
Definitely
more
unpredictable
rates
and
you've
seen
the
the
utilization
come
down
pretty
I'm.
Sorry
I'm,
looking
at
a
graph
here,
it's
unfair
but
pretty
like
negatively
correlated
with
compound
compact
utilization
and
so
what's
interesting
is
compound,
may
have
taken
some
of
the
market
share
of
dive,
borrowing
from
dy/dx
and
I.
E
E
Think
dy/dx
is,
in
large
part,
going
to
be
used
for
like
leverage,
long
positions
and
structures,
and
so
I
think
there
were
a
fair
amount
of
people
who
kind
of
given
up
on
long
positions
and
probably
too
thus
like
disproportionately
affected
to
borrow
on
dy/dx
versus
other
platforms,
because
I
think
UID
X,
as
its
brand
is
going
to
be
more
used
for
a
more
expensive,
shorter
term
plays
or
at
least
more
unpredictable
shorter
term
plays.
That
probably
are
for
a
greater
degree
of
leverage.
E
You
know
like
going
directly
3x
or
something
we
do
any
xou
iOS
has
the
easiest
method
of
going.
You
know
directly
to
a
3x
leverage,
long
position,
so
compound
I
think
is
going
to
be
basically
the
cheaper
version
of
maker,
at
least
it's
how
a
lot
of
people
are
gonna.
Think
about
it
right
now.
So,
to
the
extent
that
supply
exists
on
compound
it
is
gonna,
get
eaten
up,
but
I
I,
don't
think.
E
That's
necessarily
bad
is
kind
of
my
point
and
and
I
think
it
is
actually
probably
a
net
economic
positive
because
we're
what
was
happening
with
that
supply,
you
know
if
it
wasn't
being
borrowed
on
compound.
It's
not
like
people
are
drawing
dye
out
now,
just
go
lend
it
on
compound,
because
that
makes
no
economic
sense.
If
people
are
that
will
go
down
over
time
as
people
smart
enough,
but
I,
don't
think
that's
a
reasonable
explanation.
What's
been
wrong
right.
B
E
E
Yeah,
so
this
is
the
hard
part
doing
like
calls
versus
asynchronous
chats,
and
things
like
that
is
really
so
quickly
that,
like
some
of
this
I
haven't,
had
a
chance
to
to
convey
this
on
the
call
since
then,
but
like
it's,
definitely
been
discussed
in
chats
that
you
know
this
idea
of
okay
stability
was
increasing,
19.5%
is
gonna,
moon,
yeah
I.
Don't
think
that's
an
unreasonable
guess
no,
but
if
there
is
no
oversupply,
there's
obviously
gonna
be
a
supply
crunch.
E
Obviously,
a
liquidity
crunch
on
dye,
and
so
the
price
is
very
likely
to
move
up,
but
the
problem
is
if
that
supply
starts
to
decrease.
This
is
where
I
talk
about
quote
unquote,
bone-on-bone
like
if
that
supply
starts
to
decrease,
because
the
man
starts
to
decrease
and
people
want
to
start
using
best
instead
of
something
else.
E
That's
when
I
think
you
should
be
concerned
and,
and
so
like
you
don't
want
the
stability
fee
to
be
50%,
because
then
you're
self,
selecting
to
where
only
people
who
are
going
insanely
levered
and
are
really
into
going
levered.
Ond
I
would
use
the
system
and
that
I'm
not
sure
that
self
selection,
like
selects
for
a
very
stable
demand
function
and
so
like
I,
am
basically
cautioning
against
increasing
this
really
thing
yet
again
and
I
made
the
point
prior
that
going
from
seventeen
to
eighteen
point.
E
Five
percent
happened
too
quickly
because
going
from
19
to
sixteen
point,
five
happened
too
quickly
and
we're
just
like
hyper
overreacting
in
both
directions
and
we're
gonna
get
into
a
crazy
loop.
Because
then,
if
you
create
a
more
unstable
demand,
function
where
people
are
holding
that
die,
are
putting
it
into
leveraged
positions
but
are
not
necessarily
buying
it
or
trading
it.
Maybe
they're
borrowing
it
from
compound
or
who
knows
what?
Then
I
think
you
could
get
into
a
scenario
where
dye
prices
further
depressed
and
then
people
are
like.
E
Oh,
if
increase
is
sleepy
again
and
that's
like
a
weird
sort
of
counter
scenario
to
get
into
where
you
create
your
own
demons
right
so,
and
this
is
very
hard
to
scientifically
show
but
like
to
what
extent
is
the
current
state
of
dye?
Or
you
know,
you
know,
for
the
past
few
weeks
a
function
of
the
reaction
of
what
had
happened
to
the
19.5%
of
degrees
and
then
like
it
start
to
become
a
really
complex,
like
psychological
equation
of,
had
it
been
increased
only
to
19
percent
instead
of
1925.
E
Would
people
have
been
less
over
reactionary
to
dropping
it
to
16.5,
creating
less
of
a
need
to
increase
it
to
20
point
5
or
22
for
that
matter?
And
so
it's
like
you,
you
have
to
really
visualize
how
these
things
play
out,
because
you
could
create
your
own
problems
and
so
I
think
the
20.5%
increase
was
even
maybe
a
little
bit
premature.
Definitely
now
increasing
to
22.5
would
be
premature,
so
yeah
I
mean
in
Seibel.
As
a
point
here
is
you.
E
This
is
a
problem
of
governance.
People
could
just
decide
whatever
and
not
listen
to
anything
I'm
saying
here,
but
you
know,
data
doesn't
necessarily
have
to
drive
how
people
make
decisions
I'm
just
suggesting
what
might
not
be
supported
by
that
data
in
the
event
that
maker
holders
want
to
want
to
make
scientific
decisions.
E
The
20.5%
stability
fee,
you
decrease
the
profitability
of
leveraged
transient
demand
on
die,
it's
more
likely
than
not.
Then
it's
very
significant
trading
going
one
dive
for
eath
that
would
be
buying
volume
and
so
logically
yeah
it's
more
likely
to
improve
the
dye
price.
Okay.
So
just
like
long
term
you're
asking
yeah
I
mean
obviously,
as
transaction
volume
increases
long
term,
then
you
have
a
stronger
demand
function
that
can
drive
up
your
supply
more
sustainably.
Then,
if
you
have
lower
transactions,
oh
yeah
I
mean
I.
Think
that's
a
pretty
intuitive
conclusion.
B
B
E
Presumably,
you
have
more
liquid
order
books,
you
would
also
have
more
trading
log,
and
so,
if
there's
more
trading
volume
and
I-
and
you
have
you
know
higher
stability
fee,
then
again,
presumably
a
bigger
percentage
of
that
trading
volume
is
going
to
be
purchasing,
and
so,
unless
you
know
again,
unless
the
underlying
asset
is
doing
something
crazy
at
the
time.
But
what's
what's
important
thing
is,
as
your
transaction
grows
as
your
order
books
become
more
liquid.
E
A
Irish
I
think
things
are
naturally
winding
down
a
bit,
so
maybe
we
can
stick
a
fork
in
it.
There
I
thinks
I'm
ready
for
your
involvement,
snacks
overreaches
stuck
around
for
the
additional
Q&A
session
final
reminder:
please
check
out
the
forums
and
get
engaged
lots
of
interesting
things
are
happening,
and
this
is
the
opportunity
to
have
you
say:
alright,
thanks,
I'm,
ready.