Impact Measurement
At Gauntlet, we aim to provide clients with the most optimal recommendations for their protocol based on simulations of a wide range of possible conditions and outcomes. As part of this process, it is important for us to also verify that the recommendations we are providing are having the desired effect. In this article and later follow-ups, we will share some of our work on Impact Measurement, where we seek to create a quantifiable track record to evaluate the performance of our products.
Risk Management Goals
To introduce how we think about risk management, the goals of our recommendations are centered around two variables: 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. To see why this is the case, let’s consider the basic functions of a lending protocol and how these relationships emerge.
The purpose of a lending protocol is to match suppliers who are willing to lend a certain asset with borrowers who wish to take the other side of the trade and provide sufficient collateral. The collateral guarantees that the borrower will repay the loan since it can be sold quickly at a discount should the margin of safety fall below a threshold.
By setting this collateral factor and discount prudently, protocols can ensure loans are resolved in an orderly manner without incurring excessive losses, but setting them too conservatively also impairs the functionality to users. Since high collateral requirements directly limit how much users can borrow, it reduces the capital efficiency of the protocol by hurting utilization and revenue. Our philosophy in designing the risk management product is to achieve maximum capital efficiency given an acceptable level of market risk. In the context of 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 are focusing 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 back to an acceptable level.
Risk-On Impact: Increase Capital Efficiency
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. Due to the sometimes complex ways in which protocols incentivize lenders and borrowers, it is occasionally profitable for users to borrow and supply the same assets within the same protocol. Because this practice of recursive borrowing seeks to benefit from arbitrage, it is reasonable for these participants to use as much capacity as they can to maximize earned income. This means that 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 carefully track user behavior and calculate our impact for the two groups separately. The predictable actions of recursive borrowers means that we can ascribe an increase in usage more easily to a specific change, while non-recursive users require more careful analysis to justifiably attribute the effects. 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.
Risk-Off Impact: Reduce Risk
During a risk-off regime, we are seeking 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 the protocol, 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 by.
While we aim to tighten risk parameters gradually to allow users to reduce utilization in an orderly way, we are aware of customer concerns of parameter changes causing uncertainty for users with near-threshold accounts. To allay these worries, we have developed a strategy for communicating the potential edge cases with the community in a timely way to reduce the risk of market disruption. In a further follow-up, we will discuss in more detail how we approach risk-off regimes and some examples of how it works in practice.
