Artemis Summary
We recommend the following risk parameter changes:
- Increase the xcUSDT collateral factor from 50.0% to 53.0%.
- Increase the USDC.wh collateral factor from 62.0% to 64.0%.
- Increase USDC.wh borrow cap from 2,100,000 to 2,600,000.
- Increase FRAX borrow cap from 5,000,000 to 5,250,000.
Rationale:
VaR is $0 and our recommendations will leave it unchanged. Our recommendations will increase LaR from $302k to $303k. xcUSDT and USDC.wh are relatively safe from a market risk perspective, so we can gradually increased their collateral factor to improve capital efficiency. WETH.wh, WGLMR, WBTC.wh, FRAX, and xcDOT’s collateral factors are effectively balancing risk and capital efficiency.
With no recent improvement in liquidity for ETH.wh, we do not currently recommend to increase borrow cap for this asset, even though borrow cap usage is at 97%. As for USDC.wh and FRAX, we recommend to increase borrow caps based on current market liquidity and underlying user positions.
As we make recommendations through our risk models, we keep a constant check on the market liquidity and concentration risk to the Artemis protocol. In this regard, we would like to present some key liquidity figures for Artemis assets to share with the community. Since our last post, liquidity has improved for stablecoins while non-stablecoins have experienced some gradual decline in market depth.
| Asset | Concentration Risk | Total Circulating Supply | Current 25% Depth | Current 25% Depth USD | 25% Depth on April 12th |
|---|---|---|---|---|---|
| ETH.wh | 89% | 1,860.78 | 80 | $143,602 | 110 |
| USDC.wh | 46% | 4,566,251.00 | 1,500,000 | $1,500,000 | 800,000 |
| WBTC.wh | 90% | 139.94 | 6 | $148,838 | 8 |
| xcUSDT | 16% | 1,864,992.00 | 1,300,000 | $1,300,000 | 950,000 |
| xcDOT | 16% | 2,539,465.31 | 26,000 | $137,800 | 53,000 |
| FRAX | 20% | 5,319,872.00 | 1,300,000 | $1,300,000 | 950,000 |
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
Link to chart
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
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.
Next Steps
This will be put up for an on-chain vote by May 16th.
