Simple Economic Risk Approximation

Transcript

grown from a founder of B protocol on a simple economic risk approximations good luck yeah thank you yeah okay thank you everyone for participating in this last session of the day so my talk is about simple economic risk approximation but also mostly about sort of finding a unique swap for Muller for landing market and along to Value ratios so just some quick background on you know what is risk management economic risk management and non-to-value ratios so basically there are you know like

multiple parameters that one has to be set in the leading Market in this talk I will mostly focus on the loan to value ratio which means that basically if you put some collateral how much depth you can borrow against it uh well obviously in the permission setting the goal is to make sure that you always have some incentive to repair your loan because no one can you know there are no other means to come in you know kind of nicely ask you to okay please repay uh the only guarantee here is the collateral without it we will end up in some bed depth or other bad incident event

and the fact is that you know risk management not not only in defy in crypto in general but also in tradeify is notoriously known for being you know something which is both very political and very kind of human driven work we saw bankruptcy recently uh yeah okay maybe I first should say that you know like the Inception of Bitcoin roughly came because of the 2008 crisis where credit score writing agencies you know kind of give a very positive ranking for subprime loans and then can crypto but of course also

with crypto we saw with the three Aero bankruptcy Genesis trade bankruptcy they just say okay maybe let's not you know liquidate three error because you know if you liquidate them we will lose a lot if you want liquidate them we might profit and they bounce back and we all know how it happened you know how it ended likely with a bankruptcy of Genesis Trading uh with safety eggs you know it's more like a criminal activity and fraudulent uh intent but eventually there you know they just say okay let's not liquidate Alameda let them have whatever position they want and you know it also ended badly but yeah but even in regulated markets you know this is kind of notoriously

a bad thing and again the 2008 subprime crisis is like the prime example but we see such examples uh like on a yearly basis and yeah and then come defy with the promise of okay everything is transparent on chain but yeah but the reality is that most of the fine dining markets right now are sort of mostly on chain Banks where the Dao has to take the trading decision which which is even worse in a sense because you know they die the Dao acts very slowly it's very hard to understand the intent of the Dao it's even harder to predict

how it will work and this is example from the weekend of the USD City pegging so there was a proposal by gunland to the avidau to stop and freeze usdc liquidations The Proposal eventually didn't accept wasn't accepted uh also I should you know kind of mention here that the criticism here is not about Gauntlet it's very likely that deciding to freeze liquidation would result in you know better situation for Ave however you know freezing liquidation already brings us to things which are very similar to

Genesis trading and FTX and others so okay now everything is transparated transparent everything is Unchained but things are still you know unpredictable you interact with the landing Market Without Really knowing you know how it will behave because the Dao can just you know decide to do whatever they want and yeah and then the question is can we do differently and you know like the kind of the entire defy ethos and promise is less less replaced as much as the functionality as possible with a smart contract

so here at the bill protocol what we propose is to have kind of a uni swap formula only fall onto value ratios and in the future also for other risk economic risk parameters so basically here what we propose is something that kind of gets volatility and liquidity numbers from somewhere and you know this somewhere is be protocol and then giving these numbers it just calculate a recommended loan to value ratios where you know we can get additional objective parameters such as debt ceiling and liquidation incentive directly from the learning Market

and another important key factor here is some kind of confidence level so obviously at the end of the day you know the kind of objective metrics but also if you look at assets like Luna USD and others uh you know parameters like volatility won't tell you much given that you know like the price is packed for almost a year and then completely get Crush so we do give give some room yeah sorry so we do give some room for some user specified input which is kind of the you know non-measurable parameters that you want to take into account

and then we just output loan to value ratio the minimum loan to value ratio that you can set to the platform and this loan to value ratio is something that right now you know those are paying you know a lot of money for risk vendors to do actually also paying us to calculate these kind of things and by kind of delegating it to a Smart contract we potentially get you know sub-optimal results however it's a self-executing self you know smart contracts uh so we do get also a lot of benefits and you know completely take out of the equation all the political and other factors and make everything

more predictable another kind of byproduct of this formula is that okay if we have you know kind of objective market conditions plus some confidence level then we can get loan to value ratios but we can also apply it to existing Landing markets who you know with some very complex stress testing simulations already already calculated the loan to value ratio but we can reverse engineer it and get what kind of confidence level they had in mind and by doing so we are also able to compare now you know the confidence level between different

Landing markets so this is a joint walk with a block analytica where the risk vendor for maker Dao what we did here is we took compound and have a long to Value configurations we took the objective Market parameters and then we showed you know the confidence level for every asset there so so you can see that you know maybe unsurprisingly you know like rap Bitcoin and Link have roughly the same numbers at least for other a lower number but lower number here actually means higher tvl likely because you know East is more reliable and then like in uni we see like a big

distinguish between maker and compound and this is also likely because all these LTV parameters are kind of configured you know once every few months is because they require very heavy governance proposal and this is something that all also a smart contract configuration could avoid you know having this tedious governance process and just configurate everything on chain uh yeah so this is still very much a work in progress and this work sort of have two parts to it one part is okay how to actually calculate in you know

how to build like a robust simulation models for the LTV and here we intentionally prefer Simplicity over accuracy just because we want to have everything on chain in a totally predictable way uh and you know like avoid all the politics that come with no decision and then the other Factor here is how to actually put you know this data this input data on chain because parameters such as Dex liquidity Market volatility are not readily provided by chain link and here we plan to build some kind of consensus model to put this data on chain

so this effort is currently led by Billy protocol and a race risk Dao is kind of the service arm of the protocol but we are also working with other risk vendor also if you are a landing Market if you're a risk vendor you know reach out to us we can work on new formulas we can work on formulas for your specific learning Market we can you know build smart contracts to automatically configure your LTV ratios Etc So yeah thank you thank you [Applause] um