Simple Summary
This week, Gauntlet recommends no changes to Compound’s current parameterization.
Abstract
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 and align with the Moderate risk level chosen by the Compound community.
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 param recs 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.
Gauntlet’s simulations show that Compound is effectively balancing capital efficiency and market risk under current market conditions. As such, Gauntlet recommends no parameter changes this week.
Top 30 borrowers’ aggregate positions & borrow usages
Top 30 borrowers’ entire supply
Top 30 borrowers’ entire borrows
Note that Value at Risk (VaR) for Compound has maintained low levels over the past several months. However, on September 26, we observed a VaR of $6.1M, in large part due to the riskiness of the user with address 0xe84a061897afc2e7ff5fb7e3686717c528617487, whose breakdown is shown below:
User supply breakdown
Relevant collateral factors
User borrowing power breakdown
User borrows breakdown
User borrow usage
We recommend this user either increase their supply or decrease their borrows such that their borrow usage is at most 65%. Otherwise, should this position become riskier, we may recommend decreasing collateral factors in some combination of {ETH, WBTC, BAT} to reduce the insolvency risk this user poses during a Black Thursday event.
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.
Next Steps
Given that Gauntlet is proposing no parameter changes, no governance proposal will be created for this forum post.
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Quick Links
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Risk Dashboard
Gauntlet Parameter Recommendation Methodology
Gauntlet Model Methodology