Apollo Summary Recommendations
- We recommend no changes to parameterization. Our simulation models estimate VaR at $0 and LaR at $3.73k. All assets’ collateral factors are effectively balancing risk and capital efficiency.
Methodology
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 explain 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’s Apollo 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 Apollo
Below are charts showing a snapshot of the largest positions on the protocol that we will continue to monitor and provide insights on.
Top 10 Borrowers’ Aggregate Positions & Borrow Usages
Top 10 borrowers’ Entire Supply
Top 10 Borrowers’ Entire Borrows
Most of the top positions have recursive position which helps to minimize risk to protocol.
Utilization Rate Since 2022-12-15
Utilization rate has been returning to levels post the USDC bug.
Total Supply on Apollo by Assets since 2022-12-15
Total Borrow on Apollo by Assets since 2022-12-15
The impact from the USDC.multi bug can be shown in the above two charts. The supply and borrow markets have improved since the bug release, but the market has not return to usage levels prior to the bug.
Risk Dashboard
The community should use Gauntlet’s Apollo Risk Dashboard to better understand the updated parameter suggestions and general market risk in Apollo.
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.
Artemis Summary Recommendations
Recommendations:
- Increase the USDC.wh collateral factor from 10.0% to 15.0%.
- Increase the WBTC.wh collateral factor from 10.0% to 15.0%.
- Increase the WETH.wh collateral factor from 10.0% to 15.0%.
The VaR is near $0 and our recommendations will leave it unchanged. The LaR is $247k and our recommendations will increase it slightly to $255k. FRAX has a VaR of $0 and a LaR of $1k. WETH.wh has a VaR of $0 and a LaR of $1k. WGLMR has a VaR of $0 and a LaR of $157k. xcDOT has a VaR of $0 and a LaR of $34k. USDC.wh and WBTC.wh both have a VaR and LaR of $0.
USDC.wh, WBTC.wh, and WETH.wh have relatively low risk from a market risk perspective. Their collateral factors can be gradually increased to improve capital efficiency. Liquidity within the Moonbeam chain has increased since initial listing to support an increase in CFs. FRAX, WGLMR, and xcDOT’s collateral factors are effectively balancing risk and capital efficiency.
For the new asset listings of USDT.xc and BUSD.wh, Gauntlet recommends the below initial parameters.
| Asset | Collateral Factor | Liquidation Incentive | Reserve Factor | Borrow Cap |
|---|---|---|---|---|
| USDT.xc | 10% | 10% | 15% | 250000 |
| BUSD.wh | 10% | 10% | 15% | 250000 |
Methodology
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 explain 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’s Artemis 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 Artemis
Below are charts showing a snapshot of the largest positions on the protocol that we will continue to monitor and provide insights on.
**Top 10 Borrowers’ Aggregate Positions & Borrow Usages **
Top 10 Borrowers’ Entire Supply
Top 10 Borrowers’ Entire Borrows
Unlike in Apollo, many of the top borrow positions in Apollo are using stablecoins to borrow volatile assets such as WGLMR and xcDOT.
Utilization Rate Since 2022-12-15
Risk Dashboard
The community should use Gauntlet’s Artemis Risk Dashboard to better understand the updated parameter suggestions and general market risk in Artemis.
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.
Helpful Quick Links
Please click below to learn about our methodologies:
Next Steps
- Gauntlet to put up an on chain proposal