We recommend the following parameter changes:
- Decrease WMOVR borrow cap from 335,000 to 120,000
- Decrease xcKSM borrow cap from 32,000 to 19,000
- Decrease ETH.multi borrow cap from 1,500 to 700
CF and Borrow Cap Rationale:
Apollo’s VaR is $0 and our recommendations will leave it unchanged. The LaR is $102k and our recommendations will leave it unchanged. USDC.multi, ETH.multi, USDT.multi, WMOVR, FRAX, 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. With WMOVR, xcKSM, and ETH.multi’s low borrow cap usage and low liquidity on Moonriver’s DEXes, we recommend to lower their caps to reduce market and exploitation risk to the protocol.
WMOVR, xcKSM, and ETH.multi Borrow Cap Usage
Borrow Cap usage has decreased below 20% for WMOVR, xcKSM, and ETH.multi since last recommendations.
The relative large drop in ETH.multi borrow cap usage can be attributed to the Apollo’s largest borrow position account 0x6fffe084f6413fa400bdb93b951e71e190d5d18a in which they closed their $2.1M ETH borrow position and increased their borrow position in USDC.multi.
WMOVR, xcKSM, and ETH.multi 2% Depth Liquidity
2% Depth represents the sum of the 2% liquidity depth over the unique paths between this displayed tokens and stablecoins (typically USDC, USDT).
These assets 2% depth have decreased significantly since earlier in the year but recent liquidity conditions have been relatively low and flat.
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.
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
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.
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.
This will be put up for an on-chain vote by May 23rd.