Thanks for your feedback, @ChaosLabs. We’d like to address several points below:
We agree. As we mentioned above, for the aggressive option, the community would be betting on growth with the tradeoffs of exposure to the risk vectors described in our post above.
We are happy to take sAVAX out of this proposal so that the Chaos team can have more time for analysis, because the current supply of sAVAX is close to the existing supply cap.
Differences in Gauntlet and Chaos Methodologies:
Chaos’s recommendations above are largely less conservative (e.g., higher supply cap recommendations) than Gauntlet’s conservative option. To avoid community confusion, we provide a summary of the differences below.
From what we understand, Chaos’s methodology:
- Analyzes the ability to liquidate current positions given an extreme price drop
- Assumes that new supply and borrow positions will be distributed similarly to current supply and borrow activity
- Chaos’s methodology for supply and borrow caps depends on other protocol parameters, including liquidation bonus and Interest Rate Curves
Gauntlet’s methodology differs in several ways.
- Our methodology takes a holistic, conservative approach instead of focusing only on price drops. As we outline in our methodology, there are numerous other market risks, including Long Attacks, Short Attacks, and Liquidity Concentration Risks that our methodology considers.
- Gauntlet’s methodology does not assume that new supply and borrow positions follow the current market distribution. This is a deliberate choice. Importantly, there are many major risk vectors that are risk vectors precisely because they do not follow the current market distribution. For example, outsized long positions, outsized short positions, or economic attacks. These positions do not follow current borrow/supply distributions. As such, making the assumption that borrow/supply distribution follows existing market distribution underestimates market risk and does not account for these risky types of user position scenarios.
- Our methodology for supply and borrow caps is upstream of setting other parameters like liquidation bonus and IR Curves. The logic is as follows: we first set bounds on the size of the markets to protect broadly against the risks outlined above. After we set bounds on the market size, we ingest data on actual protocol usage into our risk simulations to determine other parameters like LT, liquidation bonus, etc. In contrast, if recommendations on caps were calculated depending on liquidation bonus etc., then this would result in circular logic that leads to too many degrees of freedom (e.g., the caps depend on parameters that depend on the size of the market that depends on caps).
To summarize: Gauntlet’s borrow and supply cap methodology are made without assumptions of expectation and labeling of price drops (i.e., what an extreme price drop will look like given a market cap). In addition, we are agnostic of positions on the protocol. This is because our liquidation threshold, LTV, and Liquidation bonus recs utilize simulation and current positions to analyze these critical price points for a protocol. We want our analysis of supply and borrow caps to be easily applicable to all chains and liquidity conditions.
Analyzing the borrow and supply caps as a function of liquidation bonus, LT assumptions, and IR potentially leads to parameter suggestions that end up with too many degrees of freedom. I.e., the logic could become circular and convoluted.
Next Steps
Adding an additional set of options to the community is skipping the most important and fundamental step of this proposal - which is for the community to align on assumptions and strategy in order to have a principled approach to risk management. Therefore, we propose the below:
- We will revise the Snapshot vote to be the below options instead:
- Conservative Assumption
- Aggressive Assumption
- Conservative Assumption only for smaller markets (while allowing majors room to grow)
- Abstain
- Nay
The assumptions depend on the following question: should the community consider overall liquidity (aggressive) or only consider native-chain liquidity (conservative)? This question depends on the community’s preference: does the community want the Aave protocols on Avalanche and Optimism to be safe under current liquidity conditions (in which case, Conservative is the choice)? OR, do we want these protocols to grow to increase usage and over time hope that liquidity conditions improve as a result of Aave’s presence on these chains?
The strategy and preference of the community greatly impact the actual parameter recommendations.
Whatever option the community decides on, Gauntlet and Chaos can then follow up with specific recommendations incorporating that assumption. We are happy to collaborate with Chaos here. We hope that the numbers in the original post provide some context on what the parameter recommendations would look like under conservative vs. aggressive assumptions.
@WintermuteGovernance - For Option 3, Chaos and Gauntlet may come up with different definitions as to how this exactly will look like. But a preview of what this could look like is that for the top [ X ] assets (defined by metrics like revenue generation, market cap, etc), the cap would allow room for organic growth. Before any on-chain vote, we are happy to align with the community via Snapshot. But first, the community would align on broad preference so that our recommendations can be relevant to the community’s risk/strategy preference.
To give more time to the community to review the above, we plan on putting up this revised Snapshot on Tuesday, February 14th. To clarify - this Snapshot would be a temperature check to help the community align on assumptions (which are strategic in nature), and then Gauntlet and Chaos can follow up with specific recommendations.