A proposal to adjust two (2) total risk parameters, including Loan-to-Value and Liquidation Threshold, across one (1) Aave V3 Arbitrum asset.
- Increase WBTC LTV from 73% to 74%.
- Increase WBTC Liquidation Threshold from 78% to 79%.
Given that these changes above are relatively minor, we will not move forward with these changes in order to reduce governance overhead, unless the community voices otherwise. However, we did want to publish this post so that the community has transparency into the results of our simulation engine.
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 WBTC non-recursive supplies and collateralization ratios:
Aave V3 Arbitrum Parameter Changes Specification
Gauntlet’s advanced simulation engine is designed to dynamically adjust risk parameters. This ongoing process is crucial to ensure that the protocol’s market risk is kept within acceptable boundaries, while at the same time promoting optimal capital efficiency.
Based on our latest simulations, we’ve identified an opportunity to enhance capital efficiency specifically for WBTC. Our proposed adjustments are designed to achieve this goal without introducing an excessive level of market risk to the protocol.
|Parameter||Current Value||Recommended Value|
|WBTC Liquidation Threshold||78%||79%|
As stated in the Simple Summary, we will not move forward with these parameter changes. The below aims to provide transparency to the community on the risk simulation results.
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.
- As stated in the Simple Summary, we will not move forward with these parameter changes in order to reduce governance overhead, unless the community voices otherwise.
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.