Reserve Factor Recommendations
- In addition to our recommended analysis of changes to collateral factors, borrow caps and supply caps, Gauntlet has also conducted an analysis of Apollo and Artemis’ reserve factors. Below is our analysis and a table of our recommended changes.
Reserve Factor Analysis
The choice of reserve factor in a lending protocol determines how much of the interest paid by borrowers is given to suppliers versus the protocol’s reserves. The reserves of a protocol have historically been used primarily to cover insolvencies, as well as funds for making strategic investments.
For Apollo and Artemis, the yield that suppliers earn is a combination of this interest paid out by borrowers as well as token distribution incentives paid out in MOVR, GLMR, MFAM, and WELL tokens. At the moment, the majority of this “supply net APY” comes from token distribution. So while lowering reserve factors would increase the interest rate for suppliers, it would only have a marginal impact on the net APY.
Our simulations currently suggest a VaR of 0 for all assets, so we don’t anticipate insolvencies due to market risk in the near future. At the same time, the reserves for all assets cover at least 1-4% of the current supply (with the exception of wormhole assets) which is currently expected to maintain efficient reasonable protection against future insolvency. Even still, Apollo and Artemis are protocols that could benefit from growing their reserves in the future. The token distribution incentives are expected to be more helpful in incentivizing supply than reductions in reserve factors, so a big decrease in reserve factors is unnecessary. That being said, significantly increasing reserve factors is not expected to be worth the potential impact of a decreased TVL (though this is a low risk).
In the future, Apollo and Artemis may benefit from reducing reserve factors if there is a plan to phase out token distribution incentives or reduce the protocol’s reliance on them.
Recommendations
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 reserves get depleted either due to insolvencies, hacks, or contributor payments, we will consider reevaluating these parameters. These 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 | |
| Artemis | GLMR | 30% | 25% | $111,434 | $8,072,906 |
| xcDOT | 30% | 25% | $91,858 | $5,314,627 | |
| FRAX | 25% | 15% | $113,172 | $8,951,300 | |
| USDC.wh | 15% | 15% | $182 | $1,826,299 | |
| ETH.wh | 25% | 25% | $291 | $1,444,725 | |
| BTC.wh | 25% | 25% | $125 | $6,507,800 |
Apollo Recommendations
- Increase the USDC.multi collateral factor from 64.0% to 66.0%.
The VaR is $0 and our recommendations will leave it unchanged. The LaR is $174k and our recommendations will leave it unchanged. FRAX has a VaR of $0 and a LaR of $69k. USDT.multi has a VaR of $0 and a LaR of $35. WMOVR has a VaR of $0 and a LaR of $59k. xcKSM has a VaR of $0 and a LaR of $45k. ETH.multi and USDC.multi both have a VaR and LaR of $0.
USDC.multi is maintaining acceptable levels of market risk for the protocol, so its collateral factor can gradually be increased to improve capital efficiency. ETH.multi, FRAX, USDT.multi, WMOVR, and xcKSM’s 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 positions which helps to minimize risk to protocol.
Utilization Rate Since
Utilization rates has been returning to levels post the USDC bug
Total Supply USD on Apollo by Assets
Total Borrow USD on Apollo by Assets
Total Token Supply on Apollo by Assets
Total Token Borrow on Apollo by Assets
The impact from the USDC.multi bug can be shown in the above two charts. The supply and borrow balances in USD and Token have rebounded since the bug release.
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 Recommendations
- Increase the USDC.wh collateral factor from 15.0% to 30.0%.
- Increase the WETH.wh collateral factor from 15.0% to 25.0%.
- Increase the WBTC.wh collateral factor from 15.0% to 25.0%.
- Increase USDC.wh borrow cap from 317,000 to 412,000
- Increase WETH.wh borrow cap from 210 to 275
- Increase WBTC.wh borrow cap from 15 to 20
The VaR is $0 and our recommendations will leave it unchanged. The LaR is $101k and our recommendations will leave it unchanged. WBTC.wh has a VaR of $0 and a LaR of $3k. WETH.wh has a VaR of $0 and a LaR of $483. WGLMR has a VaR of $0 and a LaR of $97k. FRAX, USDC.wh, and xcDOT each have a VaR and LaR of $0.
USDC.wh, WBTC.wh, and WETH.wh. are maintaining acceptable levels of market risk for the protocol, so we are recommending to increase their collateral factors to improve capital efficiency. As these are newly listed assets on the protocol, Gauntlet recommends larger increases in CF in order to improve capital efficiency in a timely manner. FRAX, WGLMR, and xcDOT’s collateral factors are effectively balancing risk and capital efficiency.
For the new asset listings of USDT.xc, Gauntlet recommends the below initial parameters which align with our previous recommendations. For further listing of
| 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 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 Artemis are using stablecoins to borrow volatile assets such as WGLMR and xcDOT.
Utilization Rates
Borrower Cap Utilization
Artemis Borrow Usage by Asset
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.
Next Steps
- Gauntlet will be putting up this to vote as an on-chain proposal
- Gauntlet will continue to monitor RFs and reach out if we notice any signs of increased risk or if changes are needed.
Quick Links
Please click below to learn about our methodologies:
Gauntlet Parameter Recommendation Methodology
Gauntlet Model Methodology
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