A proposal to adjust four (4) total risk parameters, including Liquidation Threshold and Loan-to-Value across two (2) Aave V3 Arbitrum assets.
In addition, we aim to keep the community updated that the below are assets that have reached over 70% of their caps.
|Asset||Current Supply||Current Borrow||Current Supply Cap||Current Borrow Cap|
As we have mentioned elsewhere, we support increasing the wstETH supply cap.
Given there is an existing recommendation proposal here, we will keep the community informed of next steps in order to reduce governance fatigue. In the meantime, we wanted to provide transparency on our simulation and optimization results below.
These parameter updates are a continuation of Gauntlet’s regular parameter recommendations. Our simulation engine has ingested the latest market data (outlined below) to recalibrate parameters for the Aave protocol. The community has aligned on a Risk Off Framework regarding lowering liquidation thresholds.
This set of parameter updates seeks to maintain the overall risk tolerance of the protocol while making risk trade-offs between specific assets.
Gauntlet’s parameter recommendations are driven by an optimization function that balances 3 core metrics: insolvencies, liquidations, and borrow usage. Parameter recommendations seek to optimize for this objective function. Our agent-based simulations use a wide array of varied input data that changes on a daily basis (including but not limited to asset volatility, asset correlation, asset collateral usage, DEX / CEX liquidity, trading volume, expected market impact of trades, and liquidator behavior). Gauntlet’s simulations tease out complex relationships between these inputs that cannot be simply expressed as heuristics. As such, the input metrics we show below can help understand why some of the param recs have been made but should not be taken as the only reason for recommendation. The individual collateral pages on the Gauntlet Risk Dashboard cover other key statistics and outputs from our simulations that can help with understanding interesting inputs and results related to our simulations.
For more details, please see Gauntlet’s Parameter Recommendation Methodology and Gauntlet’s Model Methodology.
Supporting Data on Aave V3 Arbitrum
Top 30 non-recursive and partially-recursive aggregate positions
Top 30 non-recursive and partially-recursive borrowers’ entire supply
Top 30 non-recursive and partially-recursive borrowers’ entire borrows
Top WETH non-recursive supplies and collateralization ratios:
Top WBTC non-recursive supplies and collateralization ratios:
Aave V3 Arbitrum Parameter Changes Specification
Gauntlet’s simulation engine will continue to adjust risk parameters to maintain protocol market risk at reasonable levels while optimizing for capital efficiency.
|Parameter||Current Value||Recommended Value|
|WBTC Liquidation Threshold||75%||76%|
|WETH Liquidation Threshold||82.5%||83.5%|
Our simulations show that the LTVs and Liquidation Thresholds for WETH and WBTC can be raised to increase capital efficiency while maintaining acceptable levels of risk.
As shown in the below chart and dashboard screenshot, our simulations show that Aave can increase capital efficiency while also decreasing the risk of bad debt.
The community should use Gauntlet’s Aave V3 Risk Dashboard to understand better the updated parameter suggestions and general market risk in Aave V3.
Value at Risk represents the 95th percentile insolvency value that occurs from simulations we run over a range of volatilities to approximate a tail event.
Liquidations at Risk represents the 95th percentile liquidation volume that occurs from simulations we run over a range of volatilities to approximate a tail event.
Aave V3 Arbitrum Dashboard
- Keep the community informed of the next steps.
Gauntlet Parameter Recommendation Methodology
Gauntlet Model Methodology
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