Apollo - Summary
We recommend the following parameter changes:
- Increase USDC.multi collateral factor from 64.0% to 66.0%.
- Increase USDT.multi collateral factor from 42.0% to 44.0%.
The VaR is $0 and our recommendations will leave it unchanged. The LaR is $477k and our recommendations will leave it unchanged. FRAX has a VaR of $0 and a LaR of $23k. USDC.multi has a VaR of $0 and a LaR of $448k. USDT.multi has a VaR of $0 and a LaR of $83. WMOVR has a VaR of $0 and a LaR of $5k. ETH.multi and xcKSM both have a VaR and LaR of $0.
USDC.multi and USDT.multi are relatively safe from a market risk perspective, so can have their collateral factors gradually increased to improve capital efficiency. ETH.multi, FRAX, WMOVR, and xcKSM’s collateral factors are effectively balancing risk and capital efficiency.
Reserve Factor Recommendations
- Gauntlet is putting up Apollo’s reserve factor recommendations again as a result of the previous vote not reaching quorum.
Our recommendation for reserve factors would be to standardize them for the purpose of consistency and to align with market rates. We recommend using a reserve factor of 15% for stablecoins and 25% for volatile assets as this matches the reserve factors used by similar protocols and would result in minimal change across the different protocol assets. We do not expect the Reserve Factor to require changes frequently, though if reserve levels change, we will consider adjusting RFs again. The current proposed changes are summarized in the table below.
| Protocol | Asset | Current RF | Proposed RF | Current Reserves | Current Supply |
|---|---|---|---|---|---|
| Apollo | MOVR | 20% | 25% | $167,247 | $3,060,531 |
| xcKSM | 25% | 25% | $72,672 | $1,918,220 | |
| FRAX | 20% | 15% | $226,203 | $8,130,201 | |
| USDT.multi | 20% | 15% | $95,338 | $2,519,001 | |
| USDC.multi | 15% | 15% | $282,184 | $16,204,339 | |
| ETH.multi | 25% | 25% | $126,813 | $9,779,502 | |
| BTC.multi | 25% | 25% | $15,949 | $87,691 |
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
Utilization Rate Over Time
Borrow Cap Utilization
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 30.0% to 40.0%.
- Increase WETH.wh collateral factor from 25.0% to 35.0%.
- Increase BUSD.wh collateral factor from 10.0% to 20.0%.
- Increase USDC.wh borrow cap from 412,000 to 516,000
- Increase WETH.wh borrow cap from 275 to 400
- Increase WBTC.wh borrow cap from 20 to 30
- Increase BUSD.wh borrow cap from 250000 to 325000
The VaR is $5k and our recommendations will increase it by $66. The LaR is $251k and our recommendations will decrease it by $55k to $196k. FRAX has a VaR of $5k and a LaR of $154. USDC.wh has a VaR of $3 and a LaR of $0. WETH.wh has a VaR of $5 and a LaR of $0. WGLMR has a VaR of $0 and a LaR of $247k. xcDOT has a VaR of $154 and a LaR of $4k. BUSD.wh and WBTC.wh both have a VaR and LaR of $0.
BUSD.wh, USDC.wh, and WETH.wh are relatively safe from a market risk perspective, so can have their collateral factors gradually increased to improve capital efficiency. FRAX, WBTC.wh, WGLMR, and xcDOT’s collateral factors are effectively balancing risk and capital efficiency.
Borrow Amounts for USDC.wh, WETH.wh, WBTC.wh, and BUSD.wh 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 them to increase the borrow cap for these assets in order to optimize user experience and growth while mitigating tail risks to the protocol.
For the new asset listings of USDT.xc, Gauntlet recommends the below initial parameters which align with our previous recommendations.
| Asset | Collateral Factor | Liquidation Incentive | Reserve Factor | Borrow Cap |
|---|---|---|---|---|
| USDT.xc | 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 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
Utilization Rate Over Time
Borrow Cap Utilization
Individual Account Analysis
We want to notify the community about account 0xb4B7c91a46C7d4BFFe99D2ce4b056a5B280ed8e3. This account has a large position representing 99% of WBTC.wh supply on Artemis. This account increased its supply balance from 301 (~$7.2M) to 641 (~$15.2M) WBTC.wh, earlier this week. The supply balance for this account represents 92% of the circulating supply of WBTC.wh. Currently, there are no borrow amounts on this account.
Single Account Supply Breakdown Time Series
Risk Dashboard
The community should use Gauntlet’s 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
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