[Applause]
[Music]
okay there's no penalty post-op but
so if stable points are a centerpiece of
decentralized projects which is rapidly
growing and increasingly complex in just
two years it's grown to five hundred
million dollars in assets and it's
evolved into a pretty complicated system
of composability between a number of
different levels and in the financial
world complex system is usually come
with complex roots so an example that
everybody's aware of is the 2008
financial crisis where we had some
contracts that were intended to really
stabilize the system but ended on that
make a huge destabilizing force but
decentralized finance and blockchain is
different right the whole idea is to try
to correct some of the properties from
the traditional financial system well it
turns out that defy India has and
complex risks so here is a noob it's one
of the first stable points in USD which
has a major competitive Angeles December
hears steam dollars which has struggled
to maintain its peg over the last year
here's BIP et Cie a synthetic asset
trying to track Bitcoin which is also
collapsed in more recent news this past
summer synthetics suffer an Oracle
attack and a month later in Tehran
another stable client suffered two
oracle attacks the problem with these
systems is this we really have sort of
little formal understanding of how they
work they end up having some complex
feedback effects there's no a truly
stable asset it's efficiently accessible
within these systems
sexual early trying to create in the
first place with steroids and involves
complex interaction in though different
agents so in this case in this talk
we're gonna go through a little bit
about understanding stable coins sort of
motivating how they're different from
existing currency models and then go
through a high-level overview of our
paper which introduces a new model to
try to understand these systems so a
stable point aims to be a protocol that
stabilizes the market price or
purchasing power of the coin to become a
more usable and adoptable cryptocurrency
and there are two main types of state
points one is the custodial point where
some custodian holds the reserve assets
off chain this is something with each
other which reintroduced as counterparty
receive back into cryptocurrencies and
then there are now just oil systems in
these systems which is what we're
selling is being involve on chain
mechanisms to try to target stability so
example here this maker downs dive and
this is like the systems we saw in the
slides earlier they end up having very
similar designs currently are very bad
luck we don't really understand these
systems very well and so
to potential failure so let's go through
a simple example of one of these systems
this is a simple contract for difference
so here we have a contractor preparing
to read agents a stable plan holder and
speculators let's say they each put in
want to use their current worth $100 for
a contract so now there's two e's over
$200 as time goes for the price oracle
updates the contract with the new price
of ether but six eighty dollars and then
the contract service returning $100
worth of ether now one point you Bobby
be there to the stable one holder either
a Mainer to the speculator but of course
it's interesting a price point which is
different the speculator wanna make a
property and that's what they're really
betting on that's why they are taking
these systems so this is similar to a
forward contract importantly except the
prices really like fixed in terms while
the payout is in this crystal core and
you can't really settle in u.s. dollars
blockchain so that's one limitation and
in these markets
they usually they're usually a heavy
frictionless for doing converting
between
Virginia risky assets and yet and in
many jurisdictions it may not be
possible and so if you're a stable part
holder and there's a settlement event it
may actually be undesirable for you
because now you're holding a risky asset
and to maintain a stable my position
would involve buying back into stable
clients at a similar stable point and
there's no guarantee that a good market
for that continues to exist in all
environments so this is motivated some
of the new designs which is what we're
focusing on in our modeling work to make
it make stable points that perform in
this sort of way
but don't have sort of a set expiration
date so in these systems you again have
a speculator let's say they currently
lock one Eve for the hundred dollars
into a contract and they create fifty
stable coins against or so amounts up to
some other collateral effect so if you
think in terms of balance sheets this is
what the speculators balance sheet looks
like and then they can take those fifty
stable coins go to a stable plan market
and trade for about 0.5 and I say about
because it depends on what the market is
willing to bear and that come turns out
to be pretty complex to understand and
this table by market works through some
idea of arbitrage
but importantly it's not real arbitrage
because it depends on sort of
assumptions about how the system
continues and then at any time the
speculator can repurchase those fifty
stable ones to unlock their collateral
but they're essentially getting is a
leveraged position to make the leverage
bet on this risky
and at some time at some wife they may
want to exit that lecherous position and
then the other side of the statement
market is taken by these staple gun
holders who buy the stable coins that
want to maintain a stable position for
some sort or potentially in the future
for purchasing reasons
so now as time involves a price Oracle
again updates its contract to tell what
the true price of Defense and get the
collateral value decreases below some
threshold then the speculator can be
partially liquid in him to try bring
them back within that threshold and so
you can see two main sort of areas of
weaknesses of all instances one is
around the price Oracle which if you can
manipulate it you can connect the entire
system and the other is around the
stable point market which if it
collapses it also breaks down the system
and then at any point there could be
some I did like this global settlement
where this is getting back to what would
happen in the original contract for
difference the contract has settled the
stable and holders in exchange they're
stable points for either at the last
oracle price and the remainder it goes
to speculators but this is meant as sort
of a last resort sort of thing and not
intended to really take effect so an
important question in these models is
can we use the existing literature
currency peg models to try to understand
fees and unfortunately the answer is no
so in traditional currency peg models
we're really looking at a game played
between
holder's and there's a government issuer
which is not really a player they're
sort of mechanically committed to to
maintaining stability for its own city
but now in the stable point systems
we're really replacing the government
issuer with decentralized speculators
who are now players linking so they
issue and withdraw stable coins stop - -
their profits over the long term they're
not there importantly not committed to
maintaining any idea all right
and the best part you hope for is that
protocols will assigned and the peg is
maintained through incentives with some
high probability so this motivates the
model of this that we're building which
it's we're building of a model of the
ascetic we put forward in the previous
slides and that behind those pictures
and so we have two agents these stable
point holders were seeking stability and
provides row at the demand side of
elasticity and speculators who choose
leverage bets that are vacuuming the
silver and there's madison's ether which
is risky an exogenous price and stable
one which has our doggedness price that
we're trying to understand which is over
collateralized in in the
and then there's a stable point market
which clears by setting the demand with
the supply in the target terms in this
case a simple list of dollars and this
is similar in concept of how clear is
not yes well and so in our paper we're
focusing on understanding how
speculators make to see so in our model
the speculators decide how to change the
stable point supply to maximize their
next period expected returns subject to
some constraints so this is some idea
idea of honest behavior in these systems
of these speculators because assistant
designers really intend what are the
systems to be maintained this idea of
arbitrage if the price of the stable
point is trading too high or too low
then there's some idea of incentives for
these stable point issuers and
speculators to change the supply to
bring them back in line with the target
but as we mentioned earlier the stock
should really to arbitrage that depends
on some assumptions the systems continue
performance so these speculators are
then that they're subject first to a
liquidation constraint and this is
inherent to the protocol it says how
over collateralized their positions have
to be and then in our model they're also
subject to a risk constraint which is
self imposed by the speculator it's
sense how much to the speculator want to
avoid liquidation so some examples of
how to formulate this might be
risk constraint which is consistent with
idea like a margin of safety you want to
avoid liquidations with some probability
but importantly we can consider a number
of other formulations in our law so from
our model were able to back out some
analytic laws also help us understand
these systems so the first is that
there's a bound to the speculators
ability to maintain the market it looks
something like it's not really important
to rest on the second result is that
speculators case limits on how quickly
they can reduce coverage even if they're
bringing in capital for outside of the
system and this leads to what we call
deleveraging spirals where a speculator
can end up having to research a stable
point at an increasing price as
liquidity dries up in the market so
let's look through intuitively how this
sort of works so here we're looking at
to measure the price of the stable point
so measures of demand and supply in the
stable point market and on the far side
the collateral in the system and so now
if there's a liquidation collateral is
used to reduce the supply by buying back
stable coins which leads to an imbalance
between supply and demand and to make up
for that that leads to increase in price
of a stable coin and a decrease in the
demand
thanks back your wine but now if we're
looking at starting at this state if
there's another wave of liquidations
because the price is higher there's less
liquidity in the market the more
collateral needs to be used to buy back
the same amount of supply to its
epicenter facts and so you have sort of
a potential apple amplification effect
to how much collateral and how quickly
it's used our third analytical result it
describes when these systems are stable
versus unstable if we had a couple more
assumptions we contracted we show that
if the leverage constraints for these
speculators is never acted then the
system converges to a steady-state where
we have a stable price and zero variance
and important observation here is that
this steady state may not have a price
of $1 maybe below put there potentially
be above and then sort of outside of
this stable or each other when this
constraint activated then we conjecture
the volatility is bounded above zero
with high probability and the reason
here is that sort of when you're outside
of this this region you're more likely
to remain outside because of this
feedback effect which leads to something
like a kink and the probability
distribution at this about G and so we
can actually see some of these effects
in real data this is just a preliminary
analysis of this here on this side here
we're seeing
and either decline in December of last
year leading to a large reduction of the
supply of dye and an accompanying
increases in the price of dumping
consistent with potentially an early
stage of a deliverance by troll and so
if that continued for a while we might
see more extreme effects after that and
then on the far side here in sort of
more normal times for diet you can see
that the price the certain normal
trading price of diet can be below the
$1 target but to serve in line with our
stable original results and sort of
effects from liquidations can cause
spike spike increases to the trading
process of technical our system lets us
our model also lets us do some
simulations so in these simulations we
can see these stable and unstable
regions as well we can also look at the
effects of different speculator
behaviour volatility and sort of
survival rates of the system I'll leave
that to sort of more details in the
paper to watch check it out another
important thing from our results is that
they can lead to new attack examples so
first an important thing is that
attacking a stable one is a different
idea from a traditional the focus here
is not on breaking the willingness of
the central bank to maintain a peg its
instead involving manipulating the
interaction of a number of speculators
and there are three sort of attack
primitives that allow these attacks to
happen first is that these delivering
spirals can lead to your arbitrage like
trance around
these to be supplemented with the fact
that real implementation is stable at
arbitrage to try too hard automate these
regulations and then third miners have
ability to censor reorder transactions
to extract problems so the first attack
we consider if there's either decline
attacker and manipulate the market to
trigger and profit from these
liquidations so this is similar to a
short squeeze like attack on existing
speculators it can be supplemented also
with a bribe to miners to try to freeze
collateral chopped up transactions and
it sort of works like this so the
attacker would buy stable points early
sort of try out put it in the market
during the ether decline liquidations
are triggered and then the attacker can
potentially earn a spread by selling at
the post of liquidation prices and also
potentially the mentor as a new
speculator attacks by prices which is
good for speculator position
intentionally and start looking at this
in our model through some examples could
potentially be very profitable the
second attack is after their spiritual
attacker trying to re-argue blotching to
trigger and profit from spiraling
liquidations and so the idea here is
that a change in the transaction
ordering can cause more liquidation and
extractable value for the speculator
which leads to the fact that perverse
incentives for these attacks
for miners is if those rewards are
greater than the mining rewards then
there's an incentive for the
to do this technically so what is this
kind of look so in the current state of
the system we might have a timeline of
the transaction history and say this is
the Oracle price view and now the
attacker are and then there are
liquidations in the current timeline
here and now the attacker wants to
branch the watch chain here and
importantly they can inherit the price
because those transactions that are
designed they can inherit the
liquidations as well and place
themselves in the position to perform
those and benefit from those and in fact
they can reorder and censor transactions
to trigger even more liquidations and
put themselves in the position of the
proper doses
[Music]
so they're there also some design
insights that come forth from our work
so one idea is that if we're trying to
figure out how to design these the focus
should be on the widening a stable
region and limiting the severity of the
unstable region and there are some
specific certain considerations
I prefer diet particularly so there are
fees in the die system which can
actually have the effect of amplifying
these deleveraging spirals but there's
potentially ways that this could be
reformatted in a counter cyclical
cyclical way so that's work that's more
a good key mechanism could also reduce
speculator earth behavior and also
reduce the effect of spirals and also
there are potential better uses for a
last resort idea of mk9 insurance to try
to do quality to leverage expertise I
talked about that a little bit more okay
a key factor in how severe these
deleveraging spirals can be is how
exchangeable was the stable point to use
alternatives on the sighting system so
things like stable fiat currencies or
stable Fiat back to crypto currencies
and the highest exchange ability is to
lower the feedback effects but it
introduces an idea of shut numbers
because we're really relying on these
flows outside the system to help to
stabilize the system and in many
jurisdictions this
being optional so there's a couple sort
of open questions that I'm continuing to
look at along these lines the first is
expanding the strategy space of the
speculator is an attack there's
understanding what else it could be
possible in this space and understanding
governance now our fluorisis how that
relates to the interests as well and
then connecting back to the first slide
understanding sort of composability of
the risks between all these protocols
but you can have layers upon layers of
de letters that could potentially occur
and then eventually we want to learn how
to design more crash resistant systems
so that wraps up the talk here
so key takeaways you can take away
either a stable kind of lateral yeah
potentially be consumed a lot faster
because of his deleveraging sort of
effects and this can lead to you
arbitrage like trades around
liquidations and new attack incentives
to consider so there are a couple more
resources here as we want to learn more
and you can follow me to see some of the
subsequent work that i'm working out
[Music]
yeah yeah that was acting to
understanding how the demand side works
yeah more of artistic oh no that's not
quite right like that motivated team the
external Lord as the system you go back
like four best nights okay
actually I think we have to wrap up but
come talk to me right now
[Applause]
you