This study measures liquidity risks in Aave, a blockchain lending protocol.
problem Liquidity risks in lending protocols, especially in Aave.
method Measurements of liquidity risks using Aave as a case study, focusing on available liquidity and market concentration.
result Liquidity risks in Aave are volatile and affect the protocol negatively, especially for repeat borrowers.
Proposes a credit scoring system for Aave accounts.
problem Scoring the creditworthiness of DeFi accounts.
method Tree-based binary classifier for predicting delinquency.
result Validated the credit scoring system for Aave accounts.
Aave community attack led to irretrievable debt and questioned decentralization in DeFi lending.
problem Decentralization vs. security in large DeFi lending protocols.
method Analyzed Avi Eisenberg's attack on Aave, showing how he artificially deflated CRV price.
result Attack led to irretrievable debt of over $1.5 million and highlighted decentralization vs. security.
Study finds Aave token network has core-periphery structure, with high decentralization predicting better returns.
problem Understanding the actual decentralization in DeFi token transactions on the Ethereum blockchain.
method Applied social network analysis to measure decentralization in Aave token transactions.
result A more decentralized Aave token network predicts higher returns and lower volatility.
Study analyzes risk management in Aave and Compound lending protocols, finding v3 better than v2.
problem Risk management in decentralized lending protocols.
method Cross-version and cross-chain analysis using fixed effects model.
result v3 protocols have better risk management, with stronger impact on L2 blockchains.
Study analyzes financial intermediation costs in decentralized lending protocols.
problem Understanding the cost of financial intermediation in decentralized lending protocols.
method Analysis of publicly available data on rates, supply, borrow activity, and accounts.
result Ex-post margins are 1% and lower for stablecoin markets.
This study compares decentralized banks and finds some lack decentralization.
problem Decentralized banks do not fully decentralize transactions as expected.
method Network analysis of transaction data from four banks using core-periphery features.
result MakerDao and Compound are more decentralized than Aave and Liquity.
This paper explores leverage staking with stETH, revealing high returns but also significant risks.
problem Leverage staking introduces risks through intensified selling pressure and cascading liquidations.
method Formal framework for leverage staking, stress tests under extreme conditions of stETH devaluation.
result Leverage staking amplifies risks, leading to intensified selling pressure and price declines.
Smoothed fitness landscape improves protein optimization.
problem Infeasibility of combinatorially large protein sequence space.
method Formulate protein fitness as a graph signal, smooth using Tikunov regularization, and optimize with Gibbs sampling.
result 2.5 fold fitness improvement over training set.
Dynamic pricing improves DeFi lending efficiency by reducing regret to logarithmic levels.
problem Static pricing mechanisms in DeFi lending protocols lead to suboptimal welfare and revenue.
method Online learning model for static and dynamic pricing models in DeFi lending.
result Adaptive supply models achieve logarithmic regret, outperforming static models.
This paper examines interest rates and market efficiency in DeFi loanable funds protocols.
problem Equilibrium of supply and demand for loanable funds in DeFi protocols.
method Review of interest rate mechanisms in Compound, Aave, and dYdX; empirical analysis of market efficiency and inter-connectedness.
result Interest rate rules in DeFi protocols do not always equilibrate supply and demand.
This study examines liquidation risks in DeFi lending markets.
problem Liquidity risks in decentralized finance lending protocols.
method Quantitative analysis of liquidation data from four major DeFi platforms.
result Current liquidation mechanisms incentivize liquidators but lead to excessive collateral sales.
Auto.gov uses RL to automate DeFi governance, improving security and profitability.
problem Manual DeFi governance is prone to human bias and financial risks.
method Auto.gov employs a deep Q-network reinforcement learning strategy for semi-automated parameter adjustments. result Auto.gov outperforms traditional governance methods by at least 14% in terms of protocol profitability. The paper proposes a machine learning framework for detecting DeFi fraud across multiple blockchain chains.
problem Early detection of financial crimes in decentralized finance (DeFi) ecosystems.
method Extracting features from different blockchain chains, employing XGBoost and Neural Network for fraud detection.
result Introduction of novel DeFi-related features significantly improves fraud detection accuracy.
DeFi lending protocols faced challenges during Ethereum's merge, but avoided major liquidations.
problem Ethereum's merge caused volatility and potential liquidations in DeFi lending.
method Analyzed AAVE and Compound lending protocols during the merge and hard fork.
result Borrowing rates spiked but no significant liquidations occurred.