Lending protocol with fixed rates reduces risks using market-driven interest
Performance Guarantees for Permissionless Lending
Cryptography and Security
Summary
Current lending platforms often set interest rates through a central authority, which can cause risks and manipulation. The authors propose a lending system where borrowers and lenders compete by making offers, naturally finding fair interest rates without a central boss. Their method locks in fixed interest rates for the life of each loan, reducing surprises and risks. They also provide math tools to understand how likely borrowers are to default or have their collateral liquidated using real price data from Bitcoin.
What this means in practice
- •For decentralized finance developers: Implement lending protocols where interest rates emerge from competing offers without central authority control, minimizing manipulation and risk.$Commercial implications: Enables building safer decentralized lending platforms with calculable risk for users, improving trust and adoption in DeFi markets.
- •For financial risk managers: Assess and manage risks of loan repayment shortfall and collateral liquidation by modeling asset price dynamics more accurately for crypto-backed loans.
Authors
Nanqiao Wang, Jia Yuan Yu
Abstract
We propose a lending protocol with far fewer risks than existing pool-based lending protocols (e.g., Aave, Compound). In our protocol, lenders and borrowers embody the spirit of decentralization by making competing interest-rate offers, resulting in the discovery of the prevailing interest rate through tâtonnement-like dynamics as opposed to taking rates partly determined by a central authority (e.g., Aave, Federal Reserve). We eliminate the risk of manipulation by creating loans with a fixed interest rate throughout the loan---like US Treasury bills. In particular, we further derive performance guarantees probabilities of repayment shortfall and liquidation by modeling the value of the collateral as a random walk, thus allowing participants to more efficiently manage their risk. To do so, we use characteristic functions and Fourier inversion to derive exact integral representations of the liquidation and repayment shortfall probabilities under the most general assumptions. We illustrate empirically this method by using Bitcoin as collateral and fitting its historical prices to Gaussian, Student-$t$, and symmetric stable Paretian distributions.