Three distinct levels from which the community can select a preferred risk tolerance for the Compound ecosystem. The results of this survey will inform Gauntlet’s analysis to deliver Dynamic Risk Parameters to optimize yield, capital efficiency, and mitigate depositor losses.
From a market risk perspective, the goal for Gauntlet’s simulations is to standardize Value-at-Risk (VaR) across all assets. Matching risk tolerance to a normalized expected yield throughout Compound ensures no subset of assets adds disproportionate risk to the protocol.
Following asset onboarding, empirical data on user behaviour (e.g., average health factors), and changes in market conditions (e.g., expected slippage) improve our simulation precision. Improved precision allows for higher confidence in model outputs—particulary for aggressive recommendations.
Gauging risk appetite is something Gauntlet will do quarterly to ensure our risk parameter recommendations track the preference of the Compound community.
As expected and observed, liquidity risk, volatility risk, and market capitalization frequently change for all assets on Compound. Updating risk paramaters (including Collateral Factor, Close Factor, Borrow Cap, Reserve Factor, and Liquidation Incentive) to remain in lockstep with the market is key to improving the Gauntlet’s target metrics.
We would note that the below are subject to change by the time a vote goes up but should provide a good illustration of the delineation between risk categories.
|Current Collateral Factor (Current CF)||Conservative CF||Moderate CF||Aggressive CF|
Conservative metrics: the models target a 5-10% decrease on each side of the capital efficiency (opportunity cost of capital) as well as risk metrics (some conflation of liquidations, insolvencies, and market impact) on each asset.
Moderate metrics: target a similar risk profile to the current risk parameters with reweightings to achieve improved capital efficiency.
Aggressive metrics: target a 5-10% increase for capital efficiency (opportunity cost of capital) with considerations for the same risk metrics (liquidations, insolvencies, and market impact) on each asset.
We chose to include Collateral Factor to illustrate risk categories in this poll, as Collateral Factor is the parameter that has the most obvious and significant impact on Compound’s risk levels and capital efficiency. As a general note, we make efforts to update parameters on a single-batch basis (as opposed to updating several parameters for any given asset at the same time), which the Compound community has voiced as their preference. Gauntlet’s scope also covers Close Factor, Borrow Cap, Reserve Factor, and Liquidation Incentive, which are parameters that have more complex impacts. Global parameters such as Liquidation Incentive and Close Factor can have broader impacts on the Compound ecosystem. As such, we are continuing to work on validating such parameters through our simulations with updated liquidation economics.
|Symbol (Current CF)||Volatility||Collateral Supply (USD)||Average Daily Volume (USD)||Liquidation Volume (USD, 90 day)||Liquidation Count (90 day)|
As on-chain options and perpetual futures become more popular, it is important to acknowledge the various sources of liquidity available. The liquidators in our simulations mimic the behavior of liquidations observed on the Ethereum blockchain. Namely, many liquidators sell liquidated collateral in an atomic transaction on a decentralized exchange. For liquidations larger than 10,000 USD in size in the last 90 days, over 60% went through a Sushiswap, Uniswap V2, or Uniswap V3 pool within the liquidation transactions. The asset slippage calculations focus on spot market conditions. As market liquidity and behavior changes, we will adapt our simulations accordingly.