Through Dynamic Risk Parameters, Gauntlet provides parameter recommendations to optimize capital efficiency and manage risk for the Compound protocol. We are publishing a multi-part series on measuring the impact of parameter changes. This Impact Measurement series creates a quantifiable track record to evaluate the performance of our products. Part 1 can be found here and provides a high-level framework around Impact Measurement. Below is a summary of Part 1 - we welcome feedback from the community.
To introduce how we think about risk management, our recommendations’ goals are centered around risk and Capital Efficiency. Since improving along one of these dimensions is typically accompanied by sacrificing some of the other, our recommendations usually involve an inherent trade-off.
Our philosophy in designing the risk management product is to achieve maximum capital efficiency given an acceptable level of market risk. In measuring impact, this translates into two distinct regimes in which our recommendations have different aims. During a risk-on regime (e.g., when market conditions are favorable), we focus more on the capital efficiency side because risk is well within the tolerable range. Conversely, in the risk-off regime, we are sacrificing some capital efficiency to reduce protocol risk to an acceptable level.
To measure our impact in a risk-on cycle, we are looking to see whether borrowers increase their utilization as we expect while making sure insolvencies remain at or near zero. Since this depends on borrower behavior, we separate borrowers in our analysis between those who react quickly to parameter changes and those who move more gradually. Recursive borrowers are usually a large part of the group of borrowers reacting quickly to parameter changes. Since non-recursive users have external reasons for borrowing that may not depend as heavily on parameters, they may not react as quickly to changes in their account limits.
In our model for Impact Measurement, we track user behavior and calculate our impact for the two groups separately. In a follow-up article, we will share data from impact calculations on both groups of users and explain our thinking behind some of the details.
During a risk-off regime, we seek to reduce protocol exposure to potential losses due to volatile market conditions. Since excessive risk is unacceptable regardless of whether or not an issue occurs, we cannot rely on realized metrics to determine our degree of risk reduction. While aiming to maintain actual losses at or near zero, we also look at the expected impact of a severe market crash on Compound, which we seek to reduce to below a level acceptable by the protocol’s community. On the other hand, setting protocol parameters more conservatively also affects capital efficiency, so we must watch utilization to evaluate how much it is being reduced.
While we aim to tighten risk parameters gradually to allow users to reduce utilization in an orderly way, we are aware of customer concerns about parameter changes causing uncertainty for users with near-threshold accounts.