(MIP- 14/15) Gauntlet Risk Parameter Recs (2022-12-19)

TLDR:

  • Gauntlet is recommending parameter adjustments for both Apollo and Artemis

Apollo Recommendations

  • We recommend increasing FRAX borrow cap to 5,682,000
  • We recommend increasing USDC.multi Collateral Factor to 64%.

Our simulation models estimate VaR will remain unchanged at $0 with these recommendations. LaR will increase from $737k to $837k with no impact to VaR. Increasing USDC.multi’s collateral should have a minor impact on borrow usage with our model estimating it to increase from 83.01% to 85.08%.

Methodology

This set of parameter updates seeks to maintain the overall risk tolerance of the protocol while making risk trade-offs between specific assets.

Gauntlet’s parameter recommendations are driven by an optimization function that balances 3 core metrics: insolvencies, liquidations, and borrow usage. Parameter recommendations seek to optimize for this objective function. Our agent-based simulations use a wide array of varied input data that changes on a daily basis (including but not limited to asset volatility, asset correlation, asset collateral usage, DEX / CEX liquidity, trading volume, expected market impact of trades, and liquidator behavior). Gauntlet’s simulations tease out complex relationships between these inputs that cannot be simply expressed as heuristics. As such, the input metrics we show below can help understand why some of the param recs have been made but should not be taken as the only reason for recommendation. The individual collateral pages on the Gauntlet’s Apollo Risk Dashboard cover other key statistics and outputs from our simulations that can help with understanding interesting inputs and results related to our simulations.

Supporting Data on Apollo

Below are charts showing a snapshot of the largest positions on the protocol that we will continue to monitor and provide insights on.

Top 10 Borrowers’ Aggregate Positions & Borrow Usages on 12/15

Top 10 Borrowers’ Entire Supply on 12/15

Top 10 Borrowers’ Entire Borrows on 12/15

Utilization Rate Since 2022-12-04

Top 10 USDC.multi Positions & Borrow Usages Ordered by USDC.multi supply on 12/15

Top 10 Entire Account Supply Positions Ordered by USDC.multi Supply on 12/15

These two charts show how the top 6 suppliers of USDC.multi on Apollo are taking recursive positions within USDC.multi and FRAX.

Top 10 Entire Account Borrow Positions Ordered by USDC.multi supply on 12/15

Individual Account Analysis

The largest borrower and supplier on Apollo is address: 0x6fffe084f6413fa400bdb93b951e71e190d5d18a. The account has a large supply and borrow positions in ETH.multi with borrow usage at 95%. Their position has a mostly recursive position in ETH.multi, FRAX, USDC.multi which minimizes risk to the protocol. Here is a snapshot of their position:



Risk Dashboard

The community should use Gauntlet’s Gauntlet’s Apollo Risk Dashboard to better understand the updated parameter suggestions and general market risk in Apollo.

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.

Artemis Summary Recommendations

  • We recommend increasing USDC.wh borrow cap to 317,000.

Our simulation models estimate VaR at $0 and LaR at $704k. All assets’ collateral factors are effectively balancing risk and capital efficiency.

Methodology

This set of parameter updates seeks to maintain the overall risk tolerance of the protocol while making risk trade-offs between specific assets.

Gauntlet’s parameter recommendations are driven by an optimization function that balances 3 core metrics: insolvencies, liquidations, and borrow usage. Parameter recommendations seek to optimize for this objective function. Our agent-based simulations use a wide array of varied input data that changes on a daily basis (including but not limited to asset volatility, asset correlation, asset collateral usage, DEX / CEX liquidity, trading volume, expected market impact of trades, and liquidator behavior). Gauntlet’s simulations tease out complex relationships between these inputs that cannot be simply expressed as heuristics. As such, the input metrics we show below can help understand why some of the param recs have been made but should not be taken as the only reason for recommendation. The individual collateral pages on the Gauntlet’s Artemis Risk Dashboard cover other key statistics and outputs from our simulations that can help with understanding interesting inputs and results related to our simulations.

Supporting Data on Artemis

Below are charts showing a snapshot of the largest positions on the protocol that we will continue to monitor and provide insights on.

Top 10 Borrowers’ Aggregate Positions & Borrow Usages on 12/15


Top 10 Borrowers’ Entire Supply On 12/15

Top 10 Borrowers’ Entire Borrows On 12/15

Utilization Rate Since 2022-12-04

Risk Dashboard

The community should use Gauntlet’s Gauntlet’s Artemis Risk Dashboard to better understand the updated parameter suggestions and general market risk in Artemis.

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.

Helpful Quick Links

Please click below to learn about our methodologies:

Next Steps

  • Gauntlet to put up on chain proposal
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