Risk Parameter Updates 2021-11-17

Simple Summary

A proposal to adjust four (4) total parameters across four (4) Compound assets.


This proposal is a batch update of risk parameters to align with the Moderate risk level chosen by the Compound community. These parameter updates are the third of Gauntlet’s regular parameter recommendations as part of Dynamic Risk Parameters.


This set of parameter updates seeks to level set assets to a Moderate risk level of the protocol while making risk trade-offs between specific assets. Note that some are different from the original risk level consensus check as market conditions have changed.


In order to react to changing market conditions, our analysis recommends decreasing the collateral factors for BAT and ZRX while increasing collateral factors for ETH and DAI. Both BAT and ZRX have increased in volatility and decreased in volume since our last parameter changes. As shown on our dashboard, these changes will slightly increase VaR, but the corresponding increase in borrow usage is substantial.

We also would like to provide color on how our simulations have incorporated the changes implemented by Proposal 49. Proposal 49 transfers 2.8% of a liquidation to cToken reserves, which decreases the effective liquidation incentive for liquidators to 5.2%. Liquidators will only liquidate an unsafe position if the expected costs (e.g. slippage of the collateral asset) are less than the liquidation incentive. The majority of assets liquidated in simulations are USDC, DAI, and ETH, which are all very liquid assets and thus still result in profitable liquidations. The net result of Proposal 49 for those liquid assets is positive - all unsafe positions are still liquidated and more collateral goes to Compound reserves. In addition, because of the lower effective liquidation incentive, less collateral hits exchanges when liquidators sell the collateral, thus marginally decreasing cascading liquidation risk. We would note that for the less liquid assets, there was a slight increase in liquidation volume and insolvency, but the net effect of Proposal 49 was beneficial.

Parameter Current Value Recommended Value
DAI Collateral Factor 75% 80%
BAT Collateral Factor 65% 60%
ZRX Collateral Factor 65% 60%
ETH Collateral Factor 75% 80%

See below volatility and exchange volume data from November 17th that were important drivers for the updated parameter recommendations.

Symbol 11-17 Volatility 11-03 Volatility Volatility Change Weekly Average Daily Volume Change (%) Collateral Usage (USD)
DAI 0.047 0.057 -0.01 -0.24 2730000000
BAT 2.32 0.68 1.63 -0.67 30000000
ZRX 1.69 0.84 0.84 -0.92 47000000
ETH 0.67 0.65 0.02 -0.4 2440000000


Gauntlet has launched the Compound Risk Dashboard. The community should use the Dashboard to better understand the updated parameter suggestions and general market risk in Compound.

As shown below, this set of parameter updates will slightly increase VaR, but meaningfully increase borrow usage.

Next Steps

While Gauntlet expects to initiate a governance proposal for this set of parameter recommendations on November 18th, we will cancel the vote should changes in our daily simulations dictate it necessary.


I have some concerns with this proposal where it’s recommending we reduce the collateral factor for BAT & ZRX. Are users who are over the 60% that is being proposed supposed to unwind or reduce their debt positions at the risk of being liquidated? The gas involved with this and users not reading these forums will lead to forced liquidations. Have we run any numbers on how many positions fall into this category?

Lastly, the moderate risk level for BAT & ZRX is 65%, so this proposal is proposing we go to the conservative 60%.

Why are we increasing collateral factor in two of the largest TVL markets and reducing it in two of the smallest TVL markets? This reminds me of the proposal where we took COMP rewards away from the small markets and provided them to new assets such as LINK without touching any of the stables, ETH, COMP, or WBTC. My point is this makes the protocol less diverse and interesting for end users such as myself.

Unfortunately I don’t hold enough COMP to influence this vote, but I will be voting no.

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Thanks, @miscao . We appreciate and value your feedback. Below are lists of liquidatable accounts as a result of the BAT and ZRX changes. We pulled in all account data (total supply and borrow) and we calculated the maximum borrowing power based on current CF. We then checked what the borrowing power would be if we updated the CF (BAT or ZRX to 60%), and checked if that borrowing power is less than the actual borrow amount. Almost every single account should be less than 1,000 USD in total supply or a recursive account that doesn’t have the risk of liquidation. Our analysis shows that lowering CF for BAT and ZRX will not drive forced liquidations in any meaningful way.

To answer your question regarding the moderate risk level, market conditions for these assets significantly changed since the Consensus Check. Namely, the volume and liquidity available for these assets have dropped significantly, in addition to a large spike in volatility in the last 2 weeks. With the current positions on Compound, we would not expect a 5% drop in price to jeopardize protocol safety, but decreasing the borrowing power of these collaterals is expected to prevent loss in tail events.

In general though, we agree that more can be done to inform users of parameter changes from a user interface perspective. We are working on potential solutions to address this.

List of all the accounts that could become liquidatable as a result of the BAT change: [‘0x3ad27e8a203b724485f8731238b6202cd5e0de06’, ‘0x46cdb38a6eb7c1c42a452a4c107ebd79bcb2a7af’, ‘0x693926bc3ea8054d164be0db4a59a37432cbc92c’, ‘0x8569f758fb3f3f0f0931cb0ee61ffbd6f2a86d22’, ‘0xa97c919f50f7fd0e93c41517144b6be2ede80aad’, ‘0xbe2f408028def100509b6c91777406cac43c7d43’, ‘0xf22e148145f7856ea37fff80363e0a06094beea6’].

List of all the accounts that could become liquidatable as a result of the ZRX change: [‘0x44e88af9ccecc4de798737cc70ee011f6c38d00e’, ‘0xc3f20ffdaf22361a036c797e45839d01774a6b50’, ‘0xcf5755e338673508214230cfa9c7a7d4baa6fed7’].

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