Risk Parameter Updates for Aave V2 ETH (2022-10-06)
A proposal to adjust five (5) total risk parameters, including Liquidation Threshold, Loan To Value, and Liquidation Bonus, across two (2) Aave V2 assets.
This proposal is a batch update of risk parameters to align with the Moderate risk level chosen by the Aave community. 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 V2
Top 30 borrowers’ aggregate positions & borrow usages
Top 30 borrowers’ entire supply
Top 30 borrowers’ entire borrows
Top STETH non-recursive supplies and collateralization ratios:
Top WBTC non-recursive supplies and collateralization ratios:
Aave V2 Parameter Changes Specification
Gauntlet's simulation engine will continue to adjust risk parameters to maintain protocol market risk at safe levels while optimizing for capital efficiency.
We have ingested the most recent Aave and market data, including user positions, prices, volatility, and liquidity for all assets, including stETH, WETH, and WBTC. We then ran simulations to stress test the protocol in times of high volatility.
As shown in the dashboard screenshot below, our simulations show that Aave can increase capital efficiency while decreasing the risk of bad debt using these parameterization recommendations.
The community should use Gauntlet's Aave V2 Risk Dashboard to understand better the updated parameter suggestions and general market risk in Aave V2. Gauntlet has also launched the Aave Arc Risk Dashboard.
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 V2 Dashboard
The proposal sets the liquidation bonus, LTV and liquidation threshold ratios by calling
configureReserveAsCollateral on the
LendingPoolConfigurator contract at
0x311Bb771e4F8952E6Da169b425E7e92d6Ac45756, using the address and parameters specific to each token.
The full list of parameter updates can be found in the forum.
Copyright and related rights waived via CC0.
By approving this proposal, you agree that any services provided by Gauntlet shall be governed by the terms of service available at gauntlet.network/tos.
Your voting info
12 Oct 2022, 03:10 UTC +00:00
12 Oct 2022, 03:10 UTC +00:00
14 Oct 2022, 19:10 UTC +00:00
15 Oct 2022, 23:53 UTC +00:00
Paul Lei, Jonathan Reem, Nick Cannon, Nathan Lord, Watson Fu, Tony Salvatore, Sarah Chen