We develop a model of stable assets, including non-custodial stablecoins backed by cryptocurrencies. Such stablecoins are popular methods for bootstrapping price stability within public blockchain settings. We derive fundamental results about dynamics and liquidity in stablecoin markets, demonstrate that these markets face deleveraging feedback effects that cause illiquidity during crises and exacerbate collateral drawdown, and characterize stable dynamics of the system under particular conditions. The possibility of such `deleveraging spirals' was first predicted in the initial release of our paper in 2019 and later directly observed during the `Black Thursday' crisis in Dai in 2020. From these insights, we suggest design improvements that aim to improve long-term stability. We also introduce new attacks that exploit arbitrage-like opportunities around stablecoin liquidations. Using our model, we demonstrate that these can be profitable. These attacks may induce volatility in the `stable' asset and cause perverse incentives for miners, posing risks to blockchain consensus. A variant of such attacks also later occurred during Black Thursday, taking the form of mempool manipulation to clear Dai liquidation auctions at near zero prices, costing $8m.
“The paper presents a model to assess the risk of market collapse in decentralized stablecoins, such as Dai. It demonstrates that stablecoin markets face deleveraging feedback effects that cause illiquidity during crises and exacerbate collateral drawdown. Furthermore, the paper suggests design improvements to decentralized stablecoins aimed to improve long-term stability.”
“This paper analyzes the economic behavior of crypto-collateralized stablecoin systems such as MakerDAO during sharp downturns of underlying assets, analogous to the events of March 2020, ‘Black Thursday.’ Specifically, the authors build a model of stablecoin issuer and stablecoin holders’ risk preferences. Using this model, they identify a positive feedback during market collapse, between increasing stablecoin issuer repurchase demand, increasing stablecoin prices and reducing liquidity, forcing issuers to pull increasing amounts of collateral out of the system in order to close each incremental collateralized position. These deleveraging dynamics are then translated into 2 scenarios in which a market participant could attack a decentralized stablecoin system while making a positive return.
With their model, the authors then suggest several mechanisms for mitigating deleveraging spirals via modifications to the fee model and use of the endogenous asset as “last resort” collateral.”
“This paper presents an economic model for the stability of stable coins (e.g., Dai) available as smart contracts in Ethereum. The paper presents the setting of noncustodial decentralized stablecoins (i.e., stable coins governed by a smart contract) and propose a mathematical model between two types of players (stablecoin holder and speculator) and two types of assets (Ether and stablecoins). The authors characterize the dynamics of the market through this model and compare the results with available data from currently deployed stable coins as well as their own simulations. Finally, the paper discusses possible attacks in the current ecosystem of stablecoins and possible countermeasures.”
“The paper focuses on an important and timely problem, that of stability in noncustodial stablecoins, and provides a convincing analysis of dynamics and liquidity in stablecoins markets.”
“The author’s model selection strikes a good balance between including enough information to replicate the deleveraging dynamics observed in March 2020, while keeping the number of actors and behavioral considerations to a minimum.
The choice of modelling this problem as a sequence of one-period optimization problems lends itself well to reconstructing discretized market dynamics of blockchains.
The paper is backed up by real market data that’s used to support the model under the adverse market conditions of interest. The authors also ran simulations based on their mathematical model and compared in silico results to historical market data.”
“The paper presents an interesting economic model for a currently deployed system. [It contained] Thorough evaluation through mathematical characterization of the model, comparison with real data and simulations, [as well as an] interesting discussion in possible attacks and mitigations”
“There was little introduction of the simulations that were run to corroborate their results. Their mathematical model was quite comprehensive, but from the text it wasn’t entirely clear how this model was translated into the simulated results depicted in figure 4 and elaborated upon in section 5.”
“When comparing the results of this model to data from Maker in March, there are real-world dynamics which may be confounding. Specifically, Maker was affected by Ethereum’s global mempool flooding, causing unusual behavior such as an $8M liquidation for 0 DAI. This information does not invalidate the author’s model, in fact it may amplify the feedback effects described. It is worth acknowledging some of the other factors at play during this time period if it is to be used as the primary basis of real-world validation.”
“Most of the examples of the paper are quite dated, which makes sense since the original release was in June 2019. The author/s partially overcome this limitation by focusing on the Maker/Dai system and discussing the big liquidation events of Black Thursday in March 2020.”