Roundup: Interest Rate Curves

To better manage risk and reward trade-offs for our lending partners, Gauntlet has researched and developed methods for interest rate curve optimization. Since interest rates determine the cost of borrowing and returns earned by suppliers, they are very important to maintaining a stable balance of protocol users. This post aggregates our theoretical work in the area, along with more practical demos, and FAQs. As our approach to interest rates develops further, we aim to keep this post updated with useful background information on our solution and methods.

1. In the paper titled “When do dynamic DeFi rate curves reduce capital efficiency?”, we detail some of the challenges around PID-type interest rate controllers. We note automated controllers may improve protocol performance under some conditions, but their unique mechanics also introduce new risks. The paper introduces a type of price manipulation called yield scraping, through which a strategic user can extract disproportionate gains from suppliers in a PID-controlled lending environment.
2. The Interest Rate Curve Optimization post introduces our practical framework for interest rate optimization and the factors we consider when making recommendations. While balancing borrowers and suppliers through the adjustment of interest rates is complex, we have observed predictable market behaviors that can help guide the optimal actions protocols should consider.
3. In [Demo] Interest Rate Curve Optimization we present an in-depth review of the methodology behind our interest rate recommendations. In designing our interest rate framework, we looked to identify the drivers of elasticity, over-utilization risk, and unusual user behavior in response to interest rates. The demo explains the reasoning behind the factors we used and why they are impactful.
4. Our first interest rate curve recommendations can be found in the Aave forum for the Ethereum v2 market. By adjusting borrow and supply interest rates for two stablecoin markets (USDT, TUSD), this Aave Request for Comment (ARC) aimed to achieve a more optimal balance of utilization while reducing tail risks in a highly uncertain market backdrop.