5 papers
This paper characterizes the approximate Nash equilibrium of competing decentralized exchanges setting dynamic fees, revealing that while the two-regime fee structure persists, competition shifts the fee-switching boundary to a weighted average of oracle and competitor rates, ultimately reducing slippage for strategic traders while having activity-dependent effects on noise traders.
This paper introduces RAmmStein, a deep reinforcement learning framework that optimizes liquidity provision in concentrated Automated Market Makers by solving an impulse control problem via a Hamilton-Jacobi-Bellman quasi-variational inequality, thereby significantly reducing rebalancing frequency and gas costs while maximizing net returns through regime-aware, mean-reversion-informed decision-making.
This paper employs a global game model to demonstrate how the interplay between public and private information, large sales, and collateral quality influences stablecoin depegging risks, revealing that greater private signal precision can paradoxically enhance stability in strong fundamental regimes and explaining the resilience of opaque stablecoins.
This paper extends Kyle's (1985) seminal insider trading model to an infinite-dimensional, many-asset setting, deriving a parsimonious equilibrium with closed-form solutions for trading strategies, cross-market price impacts, and information efficiency.
This paper establishes that the optimal rebalancing trajectory for Dynamic Weight Automated Market Makers corresponds to a geodesic under the Fisher-Rao metric (SLERP in Hellinger coordinates), thereby proving that the recursive AM-GM bisection heuristic used in prior work lies exactly on this loss-minimizing path.