Risk Parameter Updates for 3 Compound V2 Assets
01Summary
Proposal to adjust risk parameters (collateral factors and borrow caps) for 3 Compound V2 assets using Gauntlet's Dynamic Risk Parameters framework. Changes aim to maintain protocol risk tolerance while reducing Value at Risk by 15.3%, Liquidations at Risk by $0.96M, and borrow usage by 5 basis points.
- Type
- parameter change
Key changes
- Adjust borrow cap for cToken 0x80a2AE356fc9ef4305676f7a3E2Ed04e12C33946 to 30000000000000000000(medium)
- Set collateral factor for cToken 0x6C8c6b02E7b2BE14d4fA6022Dfd6d75921D90E4E to 0.6 (60%)(medium)
- Set collateral factor for cToken 0x70e36f6BF80a52b3B46b3aF8e106CC0ed743E8e4 to 0.6 (60%)(medium)
- Decrease Value at Risk by 15.3%(high)
- Decrease Liquidations at Risk by $0.96M(high)
- Decrease borrow usage by 5 basis points(medium)
Notable concerns
- Parameter changes are based on Gauntlet's optimization function balancing insolvencies, liquidations, and borrow usage
- Community alignment required through risk off framework
Generated by Kvorum
02Description
Risk Parameter Updates for 3 Compound V2 Assets
Simple Summary
A proposal to adjust three (3) risk parameters (collateral factor & borrow cap) across three (3) Compound V2 assets.
The community has aligned on a risk off framework.
Background
Gauntlet's simulation engine has ingested the latest market and liquidity data. These recommendations are Gauntlet's regular parameter recommendations as part of Dynamic Risk Parameters.
Full proposal and forum discussion
Motivation and Specification
This set of parameter updates seeks to maintain the overall risk tolerance of the protocol while making risk trade-offs between specific assets.
Our parameter recommendations are driven by an optimization function that balances 3 core metrics: insolvencies, liquidations, and borrow usage. Our parameter recommendations seek to optimize for this objective function. For more details, please see Gauntlet's Parameter Recommendation Methodology and Gauntlet's Model Methodology.
Dashboard
Gauntlet has launched the Compound Risk Dashboard. The community should use the Dashboard to understand better the updated parameter suggestions and general market risk in Compound.
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.
These parameter changes decrease borrow usage by 5 basis points, decrease VaR by 15.3%, and decrease LaR by $0.96M.
Specification
The proposal uses the Configurator contract to update the asset parameters listed above using the _setCollateralFactor and _setMarketBorrowCaps methods.
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.
03Mismatch analysis
The description is a high-level Gauntlet risk-parameter proposal stating it will adjust three risk parameters (a mix of collateral factor and borrow cap changes) across three Compound V2 assets via the Configurator contract's _setCollateralFactor and _setMarketBorrowCaps functions, with detailed values deferred to an external dashboard/forum link rather than stated in-text. The decoded calldata contains exactly three actions targeting the same Configurator address: one _setMarketBorrowCaps call (one asset, one cap) and two _setCollateralFactor calls (two assets, both set to 60%). This matches the described structure precisely — three parameter changes, correct methods, correct target contract, and correct asset count breakdown (1 borrow cap + 2 collateral factors = 3 params across 3 assets). Because the description does not state specific numeric values for the collateral factor or borrow cap in the prose itself (deferring to the dashboard), there is no textual value to contradict the calldata's 60% collateral factor or 30,000-token borrow cap — so no value_mismatch can be identified, but this also limits verification confidence to medium rather than high. No omitted, extra, or misdirected actions were found; the action count, targets, and function types align cleanly with the prose's claims. No red flags such as fund transfers, role changes, or unrelated contract calls were observed. Overall this proposal appears to faithfully implement what it describes, typical of routine Gauntlet parameter-update proposals.
Generated by Kvorum
04Actions
- Target
- 0x3d98…cd3b
- Value
- 0 wei
- Function
- _setMarketBorrowCaps(address[],uint256[])
- Arguments
{ "cTokens": [ "0x80a2AE356fc9ef4305676f7a3E2Ed04e12C33946" ], "newBorrowCaps": [ "30000000000000000000" ] }- Calldata
- Target
- 0x3d98…cd3b
- Value
- 0 wei
- Function
- _setCollateralFactor(address,uint256)
- Arguments
{ "cToken": "0x6C8c6b02E7b2BE14d4fA6022Dfd6d75921D90E4E", "newCollateralFactorMantissa": "600000000000000000" }- Calldata
- Target
- 0x3d98…cd3b
- Value
- 0 wei
- Function
- _setCollateralFactor(address,uint256)
- Arguments
{ "cToken": "0x70e36f6BF80a52b3B46b3aF8e106CC0ed743E8e4", "newCollateralFactorMantissa": "600000000000000000" }- Calldata
05Tally
Summed from votes
06Voters
No votes recorded yet.
07Forum
- Compound V2: Risk Parameter Updates 2023-01-25 ↗discourse · high confidence
- Dynamic Risk Parameters ↗discourse · high confidence
Arguments for
- Gauntlet's risk simulations show the parameter changes decrease VaR by 15.3%, LaR by $0.96M, and reduce insolvency risk from four high-risk users.— pauljlei, VonNeumann2022
- The collateral factor reductions for BAT and COMP are justified by identified insolvency risks in high-volatility scenarios affecting concentrated user positions.— pauljlei
- Increasing YFI's borrow cap from 20 to 30 addresses the 95.1% utilization and allows users more borrowing capacity based on adequate market liquidity.— pauljlei
- The proposal maintains the protocol's overall risk tolerance while making targeted adjustments to balance insolvencies, liquidations, and borrow usage.— pauljlei
Arguments against
None surfaced.
Notable participants
- pauljleiGauntlet representative who authored the proposal with detailed risk analysis and parameter recommendations.
- ccbProvided on-chain credit scoring and health factor analysis for the four at-risk user addresses.
- VonNeumann2022Community member who expressed support for the proposal and invited opposing viewpoints.
Generated by Kvorum
08Similar proposals
- compoundexecutedRisk Parameter Updates for 2 Collateral Assets95%
- compoundexecutedRisk Parameter Updates for 5 Collateral Assets95%
- compoundexecutedRisk Parameter Updates for 5 Collateral Assets94%
- compoundcanceledRisk Parameter Updates for 1 Collateral Asset94%
- compoundexecutedRisk Parameter Updates for 5 Collateral Assets91%
- compoundcanceledRisk Parameter Updates for 7 Collateral Assets91%
- compoundexecutedRisk Parameter Updates for ETH90%
- compounddefeatedRisk Parameter Updates for ETH90%
- compoundexecutedRisk Parameter Updates for 1 Collateral Asset90%
- compoundexecutedRisk Parameter Updates for WBTC, BAT, and LINK90%