Executing on decentralized exchanges (DEXs) provides a higher level of security for clients’ funds. Clients can execute their trades directly from their own wallet, using smart contracts, and keep custody of their assets. This higher level of security comes at the cost of “gas fees”, which users of a blockchain have to pay to validators for verifying transactions. In this paper, we analyze the effect of gas fees and network speed on execution cost and liquidity distribution on the largest DEX, Uniswap v3. Specifically, we use the entry of Polygon, a scaling solution to the incumbent Ethereum, as an exogenous shock to gas fees reduction and speed increase. We expect that lower gas fees and higher speed on Polygon should lead to higher concentration of liquidity around the market price. Indeed, it becomes easier for liquidity providers to revise their positions and re-post liquidity around the market price. Our preliminary findings show that, whereas overall market depth on Polygon is lower compared to Ethereum, liquidity is indeed more concentrated around the market price. This higher liquidity concentration is especially important for execution of smaller trades. Indeed, we find that price impact for smaller trades (up to $10K) is lower on Polygon, compared to Ethereum.
On November 22nd 2022, the lending platform AAVE v2 (on Ethereum) incurred bad debt resulting from a major liquidation event involving a single user who had borrowed close to $40M of CRV tokens using USDC as collateral. This incident has prompted the Aave community to consider changes to its liquidation threshold, and limitations on the number of illiquid coins that can be borrowed on the platform. In this paper, we argue that the bad debt incurred by AAVE was not due to excess volatility in CRV/USDC price activity on that day, but rather a fundamental flaw in the liquidation logic which triggered a toxic liquidation spiral on the platform. We note that this flaw, which is shared by a number of major DeFi lending markets, can be easily overcome with simple changes to the incentives driving liquidations. We claim that halting all liquidations once a user's loan-to-value (LTV) ratio surpasses a certain threshold value can prevent future toxic liquidation spirals and offer substantial improvement in the bad debt that a lending market can expect to incur. Furthermore, we strongly argue that protocols should enact dynamic liquidation incentives and closing factor policies moving forward for optimal management of protocol risk.
[Draft available on request] In this paper, we provide a model to explain equilibrium pricing of interest rates on Defi lending protocols.We pin down the fundamental sources of risk that explain the cross-section of interest rates.
Using the devaluation of the TerraUSD peg as a case study, this column shows how algorithmic stablecoins are vulnerable to speculative attacks when the system is under-collateralised. The authors point to solutions – stable collateral and over-collateralisation – to stabilise the peg.
We assess the market risk of the lending protocol using a multi-asset agent-based model to simulate ensembles of users subject to price-driven liquidation risk. Our multi-asset methodology shows that the protocol’s systemic risk is small under stress and that enough collateral is always present to underwrite active loans.
The paper study the mechanisms that govern price stability of MakerDAO's DAI token, the first decentralized stable coin.Using data on the universe of collateralized debt positions, we show that DAI price covaries negatively with returns to risky collateral. The peg-price volatility is related to collateral risk, while the stability rate has little ability to stabilize the coin. The introduction of safe collateral types has led to an increase in peg stability
I combine data from Airbnb and Zoopla and examine how the prices of individual houses evolve over time as Airbnb penetrates the market in Greater London.
I develop an estimation methodology that allows for three-sided heterogeneity. I implement this on matched panel data. I use machine learning in the classification step of the estimation of a Markovian model of worker mobility and apply it to novel data on police departments.
Using novel data combining the religion of the final pre-colonial ruler with literacy outcomes of Hindus and Muslims in India between 1881 and 1921, we show that religious groups resisted modern education due to foreign occupation.