Simple Summary
A proposal to adjust five (5) risk parameters (collateral factor & borrow cap) across five (5) Compound V2 assets.
Abstract
Gauntlet’s simulation engine has ingested the latest market and liquidity data. These parameter updates are a continuation of Gauntlet’s regular parameter recommendations as part of Dynamic Risk Parameters.
Motivation
This set of parameter updates seeks to maintain the overall risk tolerance of the protocol while making risk trade-offs between specific assets. Gauntlet has published a blog post on our parameter recommendation methodology to provide more context to the community.
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. Gauntlet’s agent-based simulations use a wide array of varied input data that changes on a daily basis (including but not limited to user positions, asset volatility, asset correlation, asset collateral usage, DEX/CEX liquidity, trading volume, expected market impact of trades, liquidator behavior). Our simulations tease out complex relationships between these inputs that cannot be simply expressed as heuristics. As such, the charts and tables shown below may help understand why some of the parameter recommendations have been made but should not be taken as the only reason for recommendation. Our individual collateral pages on the dashboard cover other key statistics and outputs from our simulations that can help with understanding other interesting inputs and results related to our simulations.
Top 30 borrowers’ aggregate positions & borrow usages
Top 30 borrowers’ entire supply
Top 30 borrowers’ entire borrows
Price changes of key assets since 2022-12-01
Users at Risk Analysis
Below, Gauntlet provides analyses on 4 addresses that pose insolvency risks in our high volatility simulations.
User 1: 0xe84a061897afc2e7ff5fb7e3686717c528617487
We have been monitoring user 0xe84a061897afc2e7ff5fb7e3686717c528617487, who, as we mentioned in our previous posts, is the main driver behind the occasional high increase in VaR in our daily simulations. As of late, this user comprises the vast majority of LaR, indicating that the position is likely to be safely liquidated by the market.
Below are details of the user’s position:
User supply breakdown
Relevant collateral factors
User borrowing power breakdown
User borrows breakdown
User borrow usage
Below is a time series of borrow usage for this user, with the purple bars corresponding to dates when the user actively updated the tokens in their position. From this, we can observe the user’s “intended” borrow usage.
User borrow usage time series since 2022-10-01
As of late, this user has updated their risk profile to have a borrow usage between 75%-80%. Even though the simulations suggest the position rarely results in insolvencies, the borrow usage has been high recently. Additionally, as shown further below, there is another risky BAT user. Our simulations show that it would be prudent to decrease BAT CF from 65% to 62%. Also note that even though BAT supply was recently paused due to price manipulation concerns, existing BAT suppliers still have borrowing power and thus present risk from liquidation cascades.
User 2: 0xd74f186194ab9219fafac5c2fe4b3270169666db
The COMP token positions have appeared as a contributor to potential insolvency in simulation. This is mostly due to user 0xd74f186194ab9219fafac5c2fe4b3270169666db, who accounts for 71% of the total COMP supply on Compound.
Below are details of the user’s position:
User supply breakdown
Relevant collateral factors
User borrowing power breakdown
User borrows breakdown
User borrow usage time series since 2022-08-25
This user who supplies COMP and borrows USDC last updated their position on August 26th, and their borrow usage has increased as COMP price has decreased. It is now up to 71%. This user, as well as another COMP user shown below, are drivers behind insolvency in high volatility simulations. Our simulations show that it would be prudent to decrease COMP CF from 65% to 62%.
User 3: 0xcb1096e77d6eab734ffceced1fcd2d35ee6b8d15
User supply breakdown
Relevant collateral factors
User borrowing power breakdown
User borrows breakdown
User borrow usage time series since 2022-10-01
The above shows another user who partially supplies BAT and whose borrow usage has also increased into the mid-high 70%s of late.
User 4: 0x7e6f6621388047c8a481d963210b514dbd5ea1b9
User supply breakdown
Relevant collateral factors
User borrowing power breakdown
User borrows breakdown
User borrow usage time series since 2022-10-01
This is the user who, on November 8, switched from primarily supplying SUSHI to primarily supplying COMP and SUSHI, and over the past week, has been increasing their risk tolerance again, thus posing greater insolvency risk in Gauntlet’s simulations. Decreasing COMP CF from 65% to 62% and decreasing SUSHI CF from 70% to 67% would decrease the insolvency risk observed in simulation and also further decrease price manipulation risks.
Borrow Caps analysis
Below is a breakdown of the 2 assets whose borrow caps are most utilized.
| Symbol | Borrow Balance | Borrow Cap | Borrow Cap Utilization |
|---|---|---|---|
| LINK | 44,432 | 45,000 | 99% |
| UNI | 487,522 | 550,000 | 89% |
Below are the corresponding time series of borrows for each of the above assets relative to their current borrow caps.
LINK borrows time series
The increase in LINK borrow cap utilization is due to user 0xe52f5349153b8eb3b89675af45ac7502c4997e6a, who borrowed LINK on 12/6 and now accounts for 41% of all LINK borrows. This user also accounts for 99.5% of YFI borrows, 11% of UNI borrows, and 27% of AAVE borrows.
Based on market factors, including Ethereum liquidity, user behavior, and user borrower distribution, we recommend increasing the LINK borrow cap to 125k to give users the ability to borrow more LINK.
UNI borrows time series
The UNI borrow cap is not currently being maxed out, so based on marked factors, including Ethereum liquidity, user behavior, and user borrower distribution, we recommend increasing the UNI borrow cap to 700k to give users an extra borrow cap buffer. As always, if the borrow cap utilization increases, we can revisit increasing it. Another note on UNI, user 0xc977d218fde6a39c7ace71c8243545c276b48931 recently entered the protocol supplying $13.3M UNI and borrowing nothing. This user now accounts for 61% of the UNI supply.
Note on cWBTC (deprecated)
We plan on decreasing CF for the deprecated cWBTC token in the near future. The largest cWBTC supplier is user 0xafebc3ce67551a998c90b01df281a4846e806434, shown below. Note this user last updated their position on June 18, 2022.
User supply breakdown
Relevant collateral factors
User borrowing power breakdown
User borrows breakdown
User borrow usage time series since 2022-10-01
This user has a borrow usage of 86%, which our analyses take into account when determining how to effectively decrease cWBTC collateral factor.
Specification
| Parameter | Current Value | Recommended Value |
|---|---|---|
| BAT Collateral Factor | 65% | 62% |
| COMP Collateral Factor | 65% | 62% |
| SUSHI Collateral Factor | 70% | 67% |
| LINK Borrow Cap | 45,000 | 125,000 |
| UNI Borrow Cap | 550,000 | 700,000 |
The CF decreases result in ~$1k in supply of projected risk-off liquidations in dust accounts. We will continue to run liquidation analyses throughout the proposal to measure whether the CF decreases will result in any risk-off liquidations.
Dashboard
The community should use Gauntlet’s Risk Dashboard to understand better the updated parameter suggestions and general market risk in Compound.
When making recommendations, Gauntlet takes into account the entire distribution of insolvencies and liquidations from our simulations and weighs them against increases in borrows. The below metrics give the community insight into some of the insolvency and liquidation tail risks the protocol could face and Capital Efficiency improvements the protocol stands to gain. Click the collateral-specific pages linked in the Collateral Risk section for more detailed simulation metrics.
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 9 basis points, decrease VaR by 65.5%, and decrease LaR by $440k.
Next Steps
- Targeting on-chain vote on 12/26
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.
Quick Links
Analytics Dashboard
Risk Dashboard
Gauntlet Parameter Recommendation Methodology
Gauntlet Model Methodology
Gauntlet launched an insolvency refund for Compound that contains a portion of our payment stream that can be clawed back in the event of insolvencies due to market risk. Since our last recommendation there have been no new insolvencies in Compound, Gauntlet’s Insolvency Refund vault is still live and can be seen here 0x7667095Caa12b79fCa489ff6E2198Ca01fDAe057