Compound Market Risk Monthly (November): Updates and Review

This is the first in a series of monthly posts from Gauntlet with the goal of keeping the community informed of market risk pertaining to Compound. So far, Gauntlet has executed 2 sets of parameter recommendations as part of our Dynamic Risk Parameters engagement. Gauntlet has implemented ten (10) parameter suggestions across six (6) assets.

Old Collateral Factor New Collateral Factor
USDC (75%) 80%
LINK (50%) 65%**
MKR (35%) 55%**
SUSHI (40%) 55%**
YFI (35%) 60%**
AAVE (50%) 60%

**Represents parameters where Gauntlet has increased collateral factors two times.

In October, Gauntlet conducted a poll to gauge the Compound community’s risk tolerance. The community chose a Moderate risk level and since then, Gauntlet’s parameter updates have been designed to align with this risk level.

Gauntlet has continually been updating its simulation platform to align with the complex and changing dynamics of the Compound ecosystem. For example, 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%. More detail can be found here.

Additionally, Gauntlet has launched the Compound Risk Dashboard and has invested in the infrastructure to enable daily updates. The community should use the dashboard to better understand the updated parameter suggestions and general market risk in Compound. Gauntlet is keen on improving the dashboard and has been conducting user studies with members of this community and others to inform the next iteration.

Parameter Update Forum Posts

Risk Parameter Updates 2021-10-25

Risk Parameter Updates 2021-11-04

Compound Governance Proposals

C066: Risk Parameter Updates for AAVE, LINK, MKR, SUSHI, USDC, and YFI

  • Passed with 1,028.1K COMP

C069: Risk Parameter Updates for LINK, MKR, SUSHI, and YFI

  • Passed with 501.4K COMP

Highlights

  • Unlocked $185.95M of additional borrow, leading to $614K increase in annual income to the Compound protocol

Over the past month, Gauntlet has already driven meaningful value to the Compound community. Thus far, Gauntlet has increased collateral factors for LINK, MKR, SUSHI, YFI, AAVE, and USDC. Our analysis showed that we can safely increase collateral factors for these assets and thus improve capital efficiency for the protocol. Assuming elasticity (if collateral factor increases by x percentage points, borrow amount increases by x percentage points as well), we expect there to be a $185.95M increase in total borrows driven by our collateral factor increases. This increase in total borrows will drive an additional $614k of annual income towards reserves. We calculate the $614k conservatively by assuming borrowers will use their collateral to borrow USDC, which has the lowest Borrow APY of the 3 stablecoins which account for 93% of total borrows. We also conservatively assume the USDC borrow rate will not increase, despite utilization increasing as a result of increased borrows. See the below figure for more detail. In addition, the reserves act as insurance against borrower default, thus, the increase in reserves also makes the protocol safer. Gauntlet is currently refining its methodology of adjusting reserve factors, which will drive more value to the Compound protocol.

Assumes elasticity of borrow in relation with collateral factor increases.

While improving capital efficiency and unlocking borrows by an additional $185.95M, Gauntlet’s recommendations have maintained risk levels in the protocol. On average, Gauntlet’s recommendations have only increased VaR by 10.2%, with absolute risk amounts maintained at reasonable levels. Our simulations observe an average of 0.3% of Compound’s TVL being at risk on any given day.

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Fantastic summary @pauljlei :call_me_hand:

It will be very useful to monitor actual vs expected borrowing demand from the collateral markets mentioned, and see how usage transforms.

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Sounds good, @rleshner . Our platform is keeping track of metrics for aggregate borrow/utilization and is also indexing borrow rates. As Compound markets have more time to adjust to collateral factor changes, these metrics would be valuable to analyze. We would note that attributing the actual borrow demand changes to the CF changes and market organic growth is not trivial, so we are continuing to refine our methodology here.