4 papers
Online Learning of Scale Parameters in Score-Driven Filters
Fabrizio Lillo, Giulia Livieri, Gianluca Palmari
Score-driven filters update a time-varying parameter by multiplying a scaled log-likelihood score by a scale parameter that controls the magnitude of the update. We name this scale…
Mitigating Adverse Selection in Concentrated Liquidity AMMs with Dynamic Fees: An Agent-Based Model Approach
Daniele Maria Di Nosse, Fabrizio Lillo
Automated Market Makers based on concentrated liquidity, such as Uniswap v3, significantly improve capital efficiency but expose Liquidity Providers (LPs) to adverse selection cost…
Trading in the Sunshine or in the Shade: Market Impact and Adverse Selection on Hyperliquid
Davide Barone, Fabrizio Lillo
Sunshine trading theory predicts that publicly disclosing trading intentions can reduce adverse selection and attract liquidity provision, lowering execution costs. Evidence is sca…
Optimal execution with deterministically time varying liquidity: well posedness and price manipulation
Gianluca Palmari, Fabrizio Lillo, Zoltan Eisler
We investigate the well-posedness in the Hadamard sense and the absence of price manipulation in the optimal execution problem within the Almgren-Chriss framework, where the tempor…