DeFi yield risk forecasting improves with new dataset and loss function
A Multi-Asset Dataset and Tail-Risk Framework for DeFi Yield Trading Derivatives
Computational Engineering, Finance, and Science
Summary
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.
What this means in practice
- •For defi protocol developers: Use the Pendle dataset and Tail-MSE loss to improve yield volatility prediction models for safer protocol design.
- •For quantitative finance teams: Implement Tail-MSE loss in risk models to enhance forecasting of tail events in yield trading derivatives across multiple asset markets.
- •For cryptocurrency portfolio managers: Deploy the Tail-MSE Sniper strategy to potentially increase risk-adjusted returns by focusing on tail risk in DeFi yield markets.$Commercial implications: Enables a trading product that targets improved returns by managing extreme yield risks in decentralized finance portfolios.
Authors
Huaiyu Jia, Jieshun You, Jingyu Liu, Yizhi Luo, Shuo Sun
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.