Simple Summary
A proposal to adjust six (6) total risk parameters, including Liquidation Threshold and Loan-to-Value across three (3) Aave V3 OP assets.
Given there is an existing recommendation proposal here, we will keep the community informed of next steps to reduce governance fatigue. In the meantime, we wanted to provide transparency on our independent simulation and optimization results below.
Abstract
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.
Motivation
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 OP
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 OP non-recursive supplies and collateralization ratios:
Top WBTC non-recursive supplies and collateralization ratios:
Top WETH non-recursive supplies and collateralization ratios:
Aave V3 OP 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 |
|---|---|---|
| OP Loan-to-Value | 30% | 35% |
| OP Liquidation Threshold | 40% | 45% |
| WBTC Loan-to-Value | 70% | 71% |
| WBTC Liquidation Threshold | 75% | 76% |
| WETH Loan-to-Value | 80% | 80.5% |
| WETH Liquidation Threshold | 82.5% | 83.5% |
The OP token has seen limited adoption at its current parameters. Users have supplied 105k OP tokens ($234k USD) as collateral and borrowed $40.8k of assets against those tokens, just 2% of the $2M isolated debt ceiling. Gauntlet’s simulations use the latest on-chain data, measuring the impact of parameter changes on existing supply and borrow positions. When an asset has a relatively low supply on Aave, the immediate impact of any parameter change is limited. Moreover, our analysis showed that OP has sufficient on-chain liquidity to justify a higher LTV and Liquidation Threshold, even accounting for potential future growth in borrowing against the token. We recommend raising the LTV and Liquidation Ratio for OP by 5 percentage points each to increase capital efficiency while maintaining enough buffer to liquidate OP collateral even in severe market conditions.
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.
Risk Dashboard
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 OP Dashboard
Next Steps
- Keep the community informed of the next steps, and will then initiate a Snapshot vote.
Quick Links
Analytics Dashboard
Risk Dashboard
Gauntlet Parameter Recommendation Methodology
Gauntlet Model Methodology
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.