Hello Compound residents! As the part of Compound Sandbox initiative, WOOF! would like to communicate with the community to follow up with the progress, receive feedback and gather insights. We will provide the updates on a bi-weekly basis here and on Community calls.
Gauntlet is actively reviewing the request for input and will provide guidance on the methodology for parameter definition and management here on the forum for the community to consider. Our focus will be on aligning recommendations with Compound’s established risk framework while addressing the unique challenges introduced by the PID controller.
We’ll share a detailed response outlining our approach during the week of January 10th.
We appreciate the WOOF! team for initiating this discussion to better understand the methodologies used to determine various risk parameters across the protocol. Below, we have provided responses to the questions outlined in the document.
Section 1: Determining Collateral Parameters for a Market
1. borrowCollateralFactor
What factors influence this parameter?
Gauntlet’s models evaluate how price volatility may affect collateral value. Assets with higher volatility generally warrant a lower collateral factor to protect the protocol from short-notice drawdowns.
Assets with deeper liquidity can handle larger liquidation volumes without incurring excessive slippage or market impact. Gauntlet’s models incorporate on-chain volumes to measure potential liquidation efficiency.
If the borrow asset is highly correlated with collateral assets, Gauntlet adjusts collateral factors accordingly.
If there are unique risks (e.g., stablecoins with depeg risk or governance tokens with concentration risks), these feed into Gauntlet’s simulations, often resulting in more conservative collateral factors.
How do you account for asset-specific volatility, liquidity, or other risks?
Market shocks, liquidity, and asset-specific risk events are tested to assess how quickly the positions might become undercollateralized.
2. liquidateCollateralFactor
How is this value calculated relative to the borrowCollateralFactor?
Typically, the liquidateCollateralFactor is set higher than the borrowCollateralFactor so that borrowers have a buffer.
Gauntlet’s policy often is to place the liquidateCollateralFactor at some premium above the borrowCollateralFactor. This premium is determined by liquidity conditions, historical liquidation success rates, and historical volatility.
What additional premiums (e.g., risk or volatility premiums) are considered?
If an asset is prone to sharp intraday price drops, Gauntlet’s methodology increases the delta between the borrowCollateralFactor and liquidateCollateralFactor.
3. liquidationFactor
What methodology is used to define the share of collateral available for liquidation?
Gauntlet models the cost a liquidator might incur during a sell-off. If the liquidation penalty is set too low, the liquidator in the event of large slippage might be disincentivized to liquidate. If it is too high, it provides poor UX for borrowers due to high penalties.
Historical liquidations, intrinsic risk profile of the asset and DEX liquidity are used to inform how much collateral typically needs to be seized to fully cover outstanding debt.
Furthermore, the borrowCollateralFactor and liquidateCollateralFactor are calculated downstream from liquidationFactor, refer to the formula below.
How do you account for market depth and liquidation slippage
Aggregated data from DEX are used to estimate price impact of large liquidations, then factored into simulation-based parameter settings. Also see our analysis that dives in deeper regarding the liquidation mechanisms
4. supplyCap
How do you determine the upper limit for the supply of an asset in a market
Assets with higher DEX slippage often have tighter supply caps to reduce the risk that the protocol becomes too reliant on a relatively illiquid collateral.
If a single large depositor can drastically skew the market’s risk profile (e.g., by holding 20%+ of the supply), a stricter supply cap can mitigate sudden liquidity crunches.
Gauntlet examines the asset’s typical usage as collateral. For example, stablecoins may have a higher capacity because of relatively stable liquidity conditions, while volatile tokens tend to have lower caps. Furthermore circulating supply for respective tokens are noted to prevent concentration risk.
Are there specific metrics (e.g., market capitalization, daily trading volume) that guide this value?
See above
Section 2: Determining Market-Specific Parameters
Interest Rate Curve Parameters
1. supplyKink and borrowKink
How do you determine the utilization threshold at which the interest rate curve shifts from the low slope to the high slope?
Kinks are primarily set at the industry standard, for example - 90% for stablecoins. Should liquidity significantly increase, there may be justification for raising the kink further. Currently, kinks are calibrated to strike a balance between offering competitive rates and mitigating liquidity crunches during periods of high demand.
Furthermore, Gauntlet looks at how a given market historically hovers around certain utilization ranges. The kink should align with a percentile threshold of typical utilization.
Are there specific metrics (e.g., average utilization rates, historical data) that guide this decision?
Both average utilization rates and historical utilization guide the kink values. Furthermore, trade-offs between capital efficiency and rate volatility also play crucial factor in determining the kink value.
Also see our analysis around IR curve recommendations to gain further insights into the rationale:
2. supplyPerYearInterestRateSlopeLow and borrowPerYearInterestRateSlopeLow
What factors define the slope of the interest rate curve below the kink?
Based on utilization - if the utilization falls under the kink, slope1 can be adjusted to recalibrate APRs below the kink.
How do these parameters balance incentivizing supply and borrowing?
The negative reserve growth should also guide setting slope1 at a level that promote supplies and borrows while not diminishing the reserves
3. supplyPerYearInterestRateSlopeHigh and borrowPerYearInterestRateSlopeHigh
How is the steep increase in interest rates above the kink calculated?
Primarily focused on establishing maximum borrow APRs to prevent liquidity crunches, even under peak utilization, while keeping them balanced to avoid excessive volatility in post-kink APRs.
If utilization trends above kink, the protocol must disincentivize further borrowing or strongly incentivize new supply. Gauntlet’s adjusts supplyPerYearInterestRateSlopeHigh and borrowPerYearInterestRateSlopeHigh to bring this desired outcome.
Are there specific risk models or stress scenarios that inform this parameter?
For risk management, we employ a “bounded competition” to ensure rates remain competitive enough to maintain market share while incorporating sufficient safety margins. This includes:
Analyzing the correlation of utilization spikes across protocols to assess systemic risks
Maintaining rate differentials that allow for gradual rather than sudden capital movements
4. supplyPerYearInterestRateBase and borrowPerYearInterestRateBase
What considerations affect the base interest rates for suppliers and borrowers?
Base rates determine the minimum borrower rate that is worth considering for the protocol. Base interest rates are carefully calibrated to balance reserve growth and borrowing costs. For stablecoins, a base rate of 0.015 is used to ensure reserve growth without creating undue pressure on borrowers. Lower rates lead to stronger reserve growth, while higher rates (e.g., around 0.03) result in a shallower low slope, increasing borrowing APRs at mid-level utilizations. This ensures the protocol maintains a healthy equilibrium between reserves and borrowing incentives.
For example, a stablecoin might have a higher base rate due to natural borrowing costs compared to WETH.
How are these values adjusted for different asset types or market conditions?
Gauntlet compares yield curves and base rates across multiple protocols and aggregates data to determine competitiveness and appropriate pay-off for the supply and borrow APR curves.
Cross-Network Differences
Why do the same basic assets (e.g., USDC) have different curve parameters on networks like Optimism and Polygon?
L2s and other chains may exhibit different liquidity depths, user behaviors, and arbitrage patterns. These differences lead to separate supply/borrow behavior.
Are network-specific factors such as ecosystem activity, liquidity, transaction costs, or governance policies considered?
What is the role of the Storefront Price Factor in determining market pricing?
Storefront price factor sets the fraction of the liquidation penalty that goes to buyers of collateral instead of the protocol. For example, if storefront price factor equals 60%, given a liquidation penalty of 5%, buyers can claim 5% * 60% = 3% discount.
How is this value calculated, and what data points are used (e.g., spot prices, volatility)?
Increase in Storefront Price Factor: Triggered when there is a noticeable increase in system risk or total value locked (TVL).
It can be preemptively adjusted in anticipation of higher risk, such as the setup of riskier positions or significant TVL growth without corresponding improvements in market liquidity.
Decrease in Storefront Price Factor: Occurs when reserves experience substantial declines, ensuring the protocol maintains sufficient buffer for insolvency protection.
Also see our analysis that dives in deeper regarding the liquidation mechanisms
While the Storefront Price Factor is primarily influenced by the overall protocol loan-to-value (LTV) ratio, adjustments are deliberately conservative. This parameter applies uniformly across all collaterals, and frequent changes could have unintended system-wide effects. For example, addressing risk in a specific collateral (e.g., COMP) is more effectively managed by altering its liquidation penalty or liquidation threshold rather than the Storefront Price Factor, which affects all collaterals and the protocol’s reserve growth.
Does this parameter account for slippage or other factors during liquidation events?
Yes, it’s lower bounded by slippage
2. Target Reserves
What methodology is used to define the appropriate reserve level for a market?
