We thank @Gauntlet for their thorough review and useful feedback. We want to emphasize that the goal of the post was to demonstrate how a quantitative approach could be use to reason about the risk level of liquidation threshold configuration, and to show how it can be used to compare the different risk appetite that Compound and Aave has towards similar assets.
The post did not introduce our full framework on how to determine the confidence level, nor the methodology of how a recommendation is translated into an actual configuration change, which need not be automated.
We plan to present the full framework and methodology in future posts, and thus, we will only provide brief response to some of the comments:
Confidence level factor
In our post we presented a methodology for reverse engineering a confidence level factor, that linearly approximates implicit assumptions the community and risk vendors are making when determining the liquidation thresholds.
It is a trivial observation that a change in the market conditions that is not followed by a change in the liquidation thresholds will change the risk level of the system (and hence the confidence level). However it does not mean that any change in the confidence level should automatically be followed by a change in the system configuration. We will present the full methodology for that in a future post.
Many to many lending market
We will elaborate more on our methodology in a separate post.
Constant slippage and liquidity recovery
We did not present our methodology for determining the current liquidity value. However, we do not assume that current liquidity will exist also in extreme market conditions. We simply assume that the historical liquidity is the best indicator for the liquidity size during liquidations. Furthermore, the given examples of stETH/ETH and USDC/USDT liquidity nicely demonstrate the need for ad-hoc adjustments due to the curve stableswap formula model. We intentionally omitted stable to stable analysis in our original post. Our framework handles curve liquidity differently, and we will present more details in a future post.