We appreciate Block Analitica and RiskDAO’s suggestion. Below we share some feedback to provide the Aave community with further context.
Summary
The methodology provided makes several assumptions around overall market behavior to create an automated mechanism for constructing liquidation thresholds. As a result, it cannot capture important market behaviors that profoundly affect the risk profile of a many-to-many lending platform such as Aave. This can create downstream risk for Aave. So, this amounts to a choice for Aave where the protocol and its community can trade-off between risk and automation. Gauntlet has researched extensively on much of these market behaviors that drive liquidation behavior - below, we will
- Show how these simplifying assumptions and the resulting downstream conclusions can inaccurately conclude that parameters such as liquidation threshold can be loosened without adding excess risk. This may create an inadequate model for the risk profile for Aave.
- Highlight our experience and research around some of the market behaviors assumed in this methodology.
On the Confidence Level Factor
The analysis above seeks to algorithmically compute an asset LT based on a new parameter called the “confidence level factor” c. This confidence factor can depend on the risk appetite and the qualitative properties of the collateral/debt pair. The confidence factor can be increased or decreased based on the community’s preference and perception of the pair.
- This approach may conflate community perception for what an asset’s quality as collateral is, with its actual quality as collateral as characterized by holder distributions, on-chain liquidity, historical and implied volatility, and potential for bad debt.
Validating the correctness of the confidence factor is also challenging. The community will not be able to adapt confidence level factor to changing market conditions, thus potentially leading to stale parameters, despite the LT computation being automatic.
- The underlying aspects of what drive confidence factors can change abruptly. Suppose asset A had a 3 confidence factor. The next day, A drops 20% and liquidity halves 50%. LT automatically adjusts due to the market event, but there is missing feedback for the value of c itself - how does community know that 3 is still the confidence factor to stick with?
- The underlying paper assumes constant liquidity as price decreases, as well as liquidity recovering at constant speed to derive a method to find the confidence factor. We discuss our experience with liquidity in extreme conditions below.
On many to many lending markets
The analysis in the original post is fixated on a [one nonstable collateral - one stable debt] baseline pair to provide some initial insight for the confidence factor. New nonstable collateral - stable debt pairs are then scaled relative to their volatility difference to the baseline. While this framework allows deep analysis into one particular pair, it doesn’t consider the many-to-many nature of Aave v3. The complexities of the relationship between lending parameters and liquidation dynamics explode when multiple assets are introduced.
Suppose a user is supplying $15000 MKR and $15000 SNX and borrowing $18500 USDC, and a price trajectory where at t1 MKR has idiosyncratically dropped 19%, and at t2 MKR has idiosyncratically dropped 28% (SNX has had no change). Consider the two scenarios where scenario 1 is MKR has its current LT (70%), and the second scenario is where we increase the LT of MKR to 80% due to the community’s desire to decrease the confidence factor for MKR / increase LT for MKR. The SNX LT remains the same at 65%. Increasing the LT of MKR implies there is belief that MKR can handle more liquidations during downturns.
| scenario | MKR_LT | HF_t1 | HF_t2 | liquidated_collateral |
|---|---|---|---|---|
| 1 | 0.7 | 0.99 | 1.05 | MKR |
| 2 | 0.8 | 0.935 | 0.994 | SNX |
- In scenario 1, MKR is liquidated at an earlier timestamp, when MKR has only dropped 16%. MKR is chosen to be liquidated due to being more profitable than SNX. Under scenario 2, liquidation is delayed to when MKR has dropped 28%. At this later timestamp, SNX may be liquidated instead because at this timestamp MKR liquidity has significantly dried up. Essentially, increasing the LT for MKR may shift the liquidation volume onto SNX during more extreme price drops.
The liquidation potential of an account is a weighted consideration of all assets supplied and borrowed by the account. The order in which collateral is liquidated and debt is repaid depends on which collateral/debt pair is most profitable. This profitability calculation is a direct output of the slippage and liquidity of the collateral/debt pair. As a result, analyzing risk purely from a single collateral to single debt lens does not capture the second-order effects of LT increases across all assets, which can involve change in liquidation composition at more extreme price drops. This can result in excessively loose parameter recommendations for a many-to-many protocol like Aave.
On some of the assumptions presented in the methodology
Naturally, simple methodologies require simplifying assumptions. When calculating confidence factors, we address some of the assumptions used in the original post.
Constant Slippage
Part of the computation for the confidence factor is rooted in an assumption that slippage of the liquidated collateral asset, as price decreases, remains constant. Above is the pool balance of stETH on Curve, as well as the stETH/ETH price during June 2022. As liquidity declines, the potential for price shocks increase, since similarly sized trades will trigger larger price impact.
Liquidity recovering
Another part of the computation of the confidence factor is that liquidity recovers at a constant speed. The chart above shows a rolling combined 2pct depth of WETH to USDC and USDT on mainnet. For a simple comparison, the 5bp fee pools for ETH/USDC and ETH/USDT account for $260mm of 24hr volume and $340mm TVL, while the ETH/DAI pool has $8mm TVL and $6mm 24 hr volume.
Liquidity shocks are unpredictable and can be triggered by a number of exogenous factors. Moreover, liquidity shocks can lead to depressed liquidity relative to previous levels for an extended period of time.
The above graph shows the liquidity for USDC during the SVB incident. Again, during extreme conditions, we’ve noted how liquidity dries up extremely quickly and does not recover.
Conclusions
BA and RiskDAO’s model here provides a simple solution for setting protocol parameters in an automated way. In the average case, this could be a benefit to Aave. However, Aave would end up taking on more risk here than in other solutions that rely on more nuanced off-chain logic to better model tail risks. This puts community members in a tough position - we know that there could be a small benefit, but there is a chance of an outsized cost. Curious to hear what others think as well.