Apollo Summary
Gauntlet recommends the following parameter changes:
- Decrease ETH.multi borrow cap from 7,000 to 1,500
- Decrease xcKSM borrow cap from 70,000 to 32,000
Rationale:
The VaR is $0 and our CF recommendations will leave it unchanged. The LaR is $202k and our recommendations will leave it unchanged. WMOVR has a VaR of $0 and a LaR of $120k. xcKSM has a VaR of $0 and a LaR of $79k. ETH.multi, FRAX, USDC.multi, and USDT.multi each have a VaR and LaR of $0.
ETH.multi, FRAX, USDC.multi, USDT.multi, WMOVR, and xcKSM’s collateral factors are effectively balancing risk and capital efficiency.
Gauntlet analyzes assets’ on-chain liquidity and potential risk of short market manipulation attack to assess borrow cap recommendations. To minimize risk, Gauntlet recommends decreasing the borrow caps for ETH.multi and xcKSM because of recent decreases in liquidity on the Moonriver chain and borrow cap utilization being below 20% since mid February.
xcKSM Borrow Tokens since Jan 1st
ETH.multi Borrow Tokens since Jan 1st
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 teases 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. To learn more about our methodologies, please see the Helpful Links section at the bottom.
Supporting Data
The below figures show trends on key market statistics regarding borrows and utilization that we will continue to monitor:
Top 10 Borrowers’ Aggregate Positions & Borrow Usages
Top 10 Borrowers’ Entire Supply
Top 10 Borrowers’ Entire Borrows
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
Gauntlet recommends the following parameter changes:
- Increase USDC.wh collateral factor from 50.0% to 60.0%
- Increase xcUSDT collateral factor from 10.0% to 20.0%
- Increase xcUSDT borrow cap from 250,000 to 500,000
- Increase WETH.wh collateral factor from 45.0% to 50.0%
- Increase WETH.wh borrow cap from 800 to 1,110
- Increase WBTC.wh collateral factor from 30% to 35%
- Increase WBTC.wh borrow cap from 60 to 110
- Increase WGLMR collateral factor from 60.0% to 62.0%
- Decrease BUSD.wh collateral factor from 15.0% to 10.0%.
The VaR is $0 and our recommendations will leave it unchanged. The LaR will increase from $303k to $307k. WGLMR has a VaR of $0 and a LaR of $290k. xcDOT has a VaR of $0 and a LaR of $13k. BUSD.wh, USDC.wh, WBTC.wh, WETH.wh, FRAX, and xcUSDT each have a VaR and LaR of $0.
USDC.wh, WBTC.wh, WETH.wh, WGLMR, and xcUSDT are relatively safe from a market risk perspective, so can have their collateral factors gradually increased to improve capital efficiency. FRAX and xcDOT’s collateral factors are effectively balancing risk and capital efficiency.
Borrow Amounts for WETH.wh, WBTC.wh, and xcUSDT have approached near full utilization of their respective borrow caps. Gauntlet analyzed the assets’ liquidity on-chain and potential risk of short market manipulation attack to assess the risk of increasing borrow caps on the protocol. Gauntlet recommends increasing the borrow cap for these assets in order to optimize user experience and growth while mitigating tail risks to the protocol. Here is a table with potential impact to reserves with the recommended borrow cap increases:
| Asset | Borrow Cap | Recommended Borrow Cap | Potential Increase of Annualized Reserve ($) |
|---|---|---|---|
| WBTC.wh | 60 | 110 | $8,349 |
| WETH.wh | 800 | 1,110 | $7,775 |
| xcUSDT | 250,000 | 500,000 | $1,803 |
BUSD Deprecation
As shared in the [MIP 29/30] Risk Parameter Updates (2023-02-20) post, Gauntlet is continuing to disable collateral enablement for BUSD by lowering the Collateral Factor from 15% to 10%.
| CF Reduction | .15→.1 |
|---|---|
| Estimated Liquidation ($) Impact | 0 |
| Estimated Liquidation Accounts Total Supply Impact | 0 |
| Estimated Active Collateral Usage ($) | $14k |
| Estimated Loss Annualized Reserve Impact | -$75.60 |
Methodology
This set of parameter updates seeks to maintain the overall risk tolerance of the protocol while making risk trade-offs between specific assets.
Gauntlet’s parameter recommendations are driven by an optimization function that balances 3 core metrics: insolvencies, liquidations, and borrow usage. Parameter recommendations seek to optimize for this objective function. Our agent-based simulations use a wide array of varied input data that changes on a daily basis (including but not limited to asset volatility, asset correlation, asset collateral usage, DEX / CEX liquidity, trading volume, expected market impact of trades, and liquidator behavior). Gauntlet’s simulations tease out complex relationships between these inputs that cannot be simply expressed as heuristics. As such, the input metrics we show below can help understand why some of the param recs have been made but should not be taken as the only reason for recommendation. To learn more about our methodologies, please see the Helpful Links section at the bottom.
Supporting Data
The below figures show trends on key market statistics regarding borrows and utilization that we will continue to monitor:
Top 10 Borrowers’ Aggregate Positions & Borrow Usages
Top 10 Borrowers’ Entire Supply
Top 10 Borrowers’ Entire Borrows
BUSD Collateral Usage ($) since Feb 15th
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.
Quick Links
Please click below to learn about our methodologies:
Gauntlet Parameter Recommendation Methodology
Gauntlet Model Methodology
Next Steps
- Gauntlet will put this up as a governance vote for the community to vote on.
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.