Risk Parameter Updates 2022-06-01

Proposals
1

Simple Summary

A proposal to adjust five (5) parameters for five (5) Compound assets.

Abstract

Gauntlet’s simulation engine has ingested the latest market and liquidity data following the recent market crash. 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 twelfth 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 recently 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.

Top 30 non-recursive and partially-recursive aggregate positions

3650×596 215 KB

Top 30 non-recursive and partially-recursive borrowers’ entire supply

3518×600 171 KB

Top 30 non-recursive and partially-recursive borrowers’ entire borrows

3530×598 160 KB

Top USDC non-recursive supplies and collateralization ratios:

3796×1444 320 KB

Top LINK non-recursive supplies and collateralization ratios:

3794×1442 307 KB

Top SUSHI non-recursive supplies and collateralization ratios:

3792×1446 283 KB

Top AAVE non-recursive supplies and collateralization ratios:

3788×1448 323 KB

Top YFI non-recursive supplies and collateralization ratios:

3790×1440 329 KB

Note that the above symbol-specific charts show the positions that are entirely non-recursive (i.e., don’t supply and borrow the same assets) and have collateralization ratios less than 10. The recursive and partially recursive positions, and more highly collateralized positions, are still incorporated into our simulations.

Specification

Our recent market downturn report showed that many collaterals are resilient to insolvencies, as our simulation models have predicted. We will continue to adjust risk parameters to drive increases in capital efficiency while maintaining protocol risk at safe levels. Parameter recommendations are ordered by descending cToken collateral locked.

Parameter Current Value Recommended Value
USDC Collateral Factor 82.5% 84%
LINK Collateral Factor 77% 79%
SUSHI Collateral Factor 70% 73%
AAVE Collateral Factor 70% 73%
YFI Collateral Factor 73% 75%

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 increase borrow usage by 8 basis points with no change in Value at Risk or Liquidations at Risk.

1512×1814 317 KB

Next Steps

While Gauntlet expects to initiate a governance proposal for this set of parameter recommendations on Sunday, 6/5 (voting to begin 2 days later), we will cancel the vote should changes in our daily simulations dictate it necessary.

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.

1 Like
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It’s all great and such, my only concern is that looks like Gauntlet is on steady route to decrease overcollateralization, making it smaller and smaller with each iteration. And that kind of really always end same way in reality. It works to the point when once it doesn’t. And that especially applies to so called s…t-coins, sushi, aave, link etc. It’s not very wise to increase CF above 70% for anything aside of wbtc, eth and stable coins, as all other stuff really doesn’t have any solid value behind them (and i mean monetary value, as many tokens do have certain utility value, which they will still have regarless of their valuation). And frankly, going above 80% even for stable coins doesn’t feel very wise either.

Letting users to borrow to such extends puts them in a position where many of them wouldn’t be able to survive even if protocol itself will do fine by liquidating them.

I’m very concerned about current trend with increasing CF, especially at a times where global financial markets are quite fragile.

Maybe it’s time to reconsider further pursuing “Moderate” strategy in favor of “Low risk” strategy at least until we see how QT and hiking rates impact global markets in coming months?

3 Likes
3

Let’s do this :fire: healthy and recommended, we can dial down once market goes :peanuts: again.

4

Thanks, @Sirokko , for your feedback. In order to arrive at our parameter recommendations, our optimization engine runs simulations that incorporate on-chain data (such as the collateralization of current user positions, liquidity conditions, volatility, etc.) in order to paint a comprehensive picture of market risk. Although there is relative turmoil in the global financial markets, our simulations show a low risk of insolvency on the Compound platform, partly driven by current user positions being highly overcollateralized. Should market risk meaningfully increase on Compound, we will lower collateral factors. We understand that there is user friction in doing so (e.g., forced liquidations), which is one reason why we have been gradually increasing CF over time (instead of ramping up CF quickly and potentially overshooting risk).

Your point on letting users overextend themselves is well taken. That is why we also take into account potential liquidations (LaR), not just potential insolvencies (VaR), in our simulations. As shown in the screenshot above, these parameter recommendations do not meaningfully increase LaR.

Thanks for bringing up the point on “Moderate” vs. “Low Risk” strategy. Should the community decide on “Low Risk,” we will re-evaluate. We’d note again though that our simulations show that Value at Risk is currently low on Compound.