Papers for

defi protocol developers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

DeFi yield risk forecasting improves with new dataset and loss function

A Multi-Asset Dataset and Tail-Risk Framework for DeFi Yield Trading Derivatives

Abstract: Yield trading has emerged as a pivotal innovation in Decentralized Finance (DeFi) by decoupling principal from yield streams to enhance capital efficiency. Despite its significance, the field lacks fine-grained datasets for systematic empirical analysis. We address this gap by constructing the first large-scale benchmark dataset for Pendle, the pioneering on-chain interest rate derivatives protocol, spanning 24 months across 340 active markets with complete lifecycle observations. Our analysis reveals a structural market transition toward stablecoin-dominated "flight-to-quality" dynamics. To tackle the heavy-tailed nature of yield data, we propose Tail-MSE, an asymmetric loss function that prioritizes tail risk awareness through dynamic penalty weighting. Experiments across six state-of-the-art time series models demonstrate consistent improvements in volatility forecasting robustness, enabling the Tail-MSE Sniper strategy to achieve superior risk-adjusted returns in over half of all markets.

Mon 26 OctComputational Engineering, Finance, and Science
The gist
Predicting risks in decentralized finance (DeFi) yield trading is hard because the data often have rare but extreme changes. The authors created a big new dataset from Pendle, a leading DeFi platform, that tracks many markets and their full life cycles over two years. They noticed the market has shifted toward safer stablecoins recently. To better predict risks, they designed a new method that focuses more on the rare big changes in yields. Tested on various forecasting models, this method helped create a trading approach that often earned better returns relative to risk.
Open → 2610.34930v1

Trading fees can offset losses for liquidity providers in continuous markets

Asymptotic Analysis of Trading Fees in CFMM

Abstract: As the dominant trading mechanism in decentralized finance, Automated Market Maker has been widely studied in research. However, limited research has been done with the trading fees taken into consideration. In this work, we study how much trading fee Liquidity Providers(LPs) can receive from arbitrage trading when the fee rate approaches zero. We give a closed-form formula for it and our result shows that the trading fees generated by arbitrage trading can fully offset the LVR loss when the price process is continuous. We further extend our conclusions to price processes with jumps and the theoretical analysis shows that jumps are the only cause of LP loss apart from market risks. Our results provide practical guidance for AMM designers as well as LPs.

Fri 2 OctCryptography and Security
The gist
Liquidity providers in decentralized finance pools earn fees when traders arbitrage price differences. This paper studies the amount of trading fees earned as the fee rate gets very small. The authors find a formula showing that fees from arbitrage fully cover losses caused by price movements when prices change continuously. They also show that only sudden jumps in price cause additional losses for liquidity providers beyond normal market risks.
Open → 2610.03262v1