Split the Yield, Share the Risk: Pricing, Hedging and Fixed rates in DeFi

2025-05-28

AI Summary

This paper addresses key challenges in decentralized finance (DeFi): high yield volatility, unmanageable interest-rate risk, and the absence of effective price discovery mechanisms. To tackle these issues, we propose the first formalized framework for yield tokenization—decomposing interest-bearing assets into principal tokens (PTs) and yield tokens (YTs), thereby enabling explicit risk separation. Methodologically, we develop a novel stochastic differential equation (SDE)-based dynamic pricing model for YTs, grounded in no-arbitrage principles; design a multi-curve automated market maker (AMM) supporting heterogeneous risk preferences; and implement a modular fixed-rate lending protocol. Our framework effectively mitigates interest-rate manipulation risks in lending pools, enhances capital efficiency, and—critically—establishes, for the first time on-chain, a verifiable fixed-income infrastructure alongside a robust, transparent yield discovery mechanism.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Search and Optimization: Distributed Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

We present the first formal treatment of emph{yield tokenization}, a mechanism that decomposes yield-bearing assets into principal and yield components to facilitate risk transfer and price discovery in decentralized finance (DeFi). We propose a model that characterizes yield token dynamics using stochastic differential equations. We derive a no-arbitrage pricing framework for yield tokens, enabling their use in hedging future yield volatility and managing interest rate risk in decentralized lending pools. Taking DeFi lending as our focus, we show how both borrowers and lenders can use yield tokens to achieve optimal hedging outcomes and mitigate exposure to adversarial interest rate manipulation. Furthermore, we design automated market makers (AMMs) that incorporate a menu of bonding curves to aggregate liquidity from participants with heterogeneous risk preferences. This leads to an efficient and incentive-compatible mechanism for trading yield tokens and yield futures. Building on these foundations, we propose a modular extit{fixed-rate} lending protocol that synthesizes on-chain yield token markets and lending pools, enabling robust interest rate discovery and enhancing capital efficiency. Our work provides the theoretical underpinnings for risk management and fixed-income infrastructure in DeFi, offering practical mechanisms for stable and sustainable yield markets.

Problem

• Modeling yield token dynamics with stochastic differential equations
• Developing no-arbitrage pricing for hedging yield volatility
• Designing fixed-rate lending protocols for DeFi risk management

Innovation

• Yield tokenization via principal and yield components
• No-arbitrage pricing framework for yield tokens
• Modular fixed-rate lending protocol integrating AMMs