Compound V2: Risk Parameter Updates 2023-01-25

Simple Summary

A proposal to adjust three (3) risk parameters (collateral factor & borrow cap) across three (3) Compound V2 assets.

Below, Gauntlet provides analyses on 4 addresses that pose insolvency risks in our high volatility simulations.

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 2023-01-10

Users at Risk Analysis

Below, Gauntlet provides analyses on 4 addresses that pose insolvency risks in our high volatility simulations. Due to the recent market uptick, borrow usages for these users have decreased, thus making their positions relatively less risky. However, we should take into account that these positions could become riskier if the users increase their borrows to take advantage of their increased borrowing power, and/or if new users join the protocol to supply these assets. As a result, we are proactively recommending decreasing CFs for some of the relevant supplied assets. Note supply is paused for BAT, YFI, MKR, and ZRX, but users can still borrow against their existing supplies.

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

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 2022-11-01

User 2: 0xd74f186194ab9219fafac5c2fe4b3270169666db

The COMP token positions have appeared as a contributor to potential insolvency in simulation. This is partially due to user 0xd74f186194ab9219fafac5c2fe4b3270169666db, who accounts for 62% of the total COMP supply on Compound. This user decreased their supply in response to our recent proposal executed on January 2, 2023, which decreased COMP CF. The insolvency risk for this user decreased as a result. Decreasing COMP CF would further decrease insolvency risk if this user were to increase their borrows in response to the recent COMP price uptick. It would also decrease risk if new users were to join the protocol to supply COMP.

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 2022-11-01

User 3: 0xcb1096e77d6eab734ffceced1fcd2d35ee6b8d15

User supply breakdown

Relevant collateral factors

User borrowing power breakdown

User borrows breakdown

User borrow usage time series since 2022-11-01

Given the current BAT liquidity, this user poses insolvency risk, as liquidators will likely be unable to arbitrage large quantities of BAT during a market downturn.

User 4: 0x7e6f6621388047c8a481d963210b514dbd5ea1b9

User supply breakdown

Relevant collateral factors

User borrowing power breakdown

User borrows breakdown

User borrow usage time series since 2022-11-01

This user supplies 87% of SUSHI and 22% of the COMP on Compound. Given current liquidity numbers, liquidators will likely be unable to arbitrage large quantities of COMP during a market downturn, and will also be somewhat limited in liquidating SUSHI as well.

Borrow Caps analysis

Below is a breakdown of YFI, whose borrow cap is almost fully utilized.

Symbol Borrow Balance Borrow Cap Borrow Cap Utilization
YFI 19.02 20 95.1%

Below is the YFI borrows time series.

LINK borrows time series

The increase in YFI borrow cap utilization is due to user 0xe52f5349153b8eb3b89675af45ac7502c4997e6a, who borrowed an additional 15 YFI tokens on 01/11/23 (roughly $90k at the time) and accounts for roughly all the YFI borrows.

Based on market factors, including Ethereum liquidity, user behavior, and user borrower distribution, we recommend increasing the YFI borrow cap to 30 (roughly $220k) to give users the ability to borrow more YFI.

Also note that YFI supply is paused, but YFI utilization is low enough such that users can borrow from the existing supply.

Specification

Parameter Current Value Recommended Value
BAT Collateral Factor 62% 60%
COMP Collateral Factor 62% 60%
YFI Borrow Cap 20 30

The CF decreases result in ~$100 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 5 basis points, decrease VaR by 15.3%, and decrease LaR by $0.96M.

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.

0xe84a061897afc2e7ff5fb7e3686717c528617487

Address Analytics:

{‘CreditScore’: 2, ‘Address’: ‘0xe84a061897afc2e7ff5fb7e3686717c528617487’, ‘features’: {‘count_borrow’: 110, ‘total_borrow’: 195569100.5055, ‘total_repay’: 131229939.76727927, ‘count_repay’: 66, ‘count_liquidation’: 0, ‘total_liquidation’: 0, ‘days_since_first_borrow’: 783, ‘count_deposit’: 128, ‘total_redeem’: 154489576.97961712}

Health factor: 1.60

0xd74f186194ab9219fafac5c2fe4b3270169666db

Address Analytics:

{‘CreditScore’: 2, ‘Address’: ‘0xd74f186194ab9219fafac5c2fe4b3270169666db’, ‘features’: {‘count_borrow’: 9, ‘total_borrow’: 11438573.802783, ‘total_repay’: 4717573.10215, ‘count_repay’: 25, ‘count_liquidation’: 0, ‘total_liquidation’: 0, ‘days_since_first_borrow’: 818, ‘count_deposit’: 15, ‘total_redeem’: 4059857.207054}

Health factor: 2.14

0xcb1096e77d6eab734ffceced1fcd2d35ee6b8d15

Address Analytics:

{‘CreditScore’: 2, ‘Address’: ‘0xcb1096e77d6eab734ffceced1fcd2d35ee6b8d15’, ‘features’: {‘count_borrow’: 124, ‘total_borrow’: 222603714.16147077, ‘total_repay’: 134943266.0295905, ‘count_repay’: 54, ‘count_liquidation’: 0, ‘total_liquidation’: 0, ‘days_since_first_borrow’: 852, ‘count_deposit’: 249, ‘total_redeem’: 81659033.49157605}

Health factor: 1.56

0x7e6f6621388047c8a481d963210b514dbd5ea1b9

Address Analytics:

{‘CreditScore’: 2, ‘Address’: ‘0x7e6f6621388047c8a481d963210b514dbd5ea1b9’, ‘features’: {‘count_borrow’: 107, ‘total_borrow’: 528078881.63471854, ‘total_repay’: 521855502.0620663, ‘count_repay’: 69, ‘count_liquidation’: 5, ‘total_liquidation’: 2970554.5666195313, ‘days_since_first_borrow’: 899, ‘count_deposit’: 117, ‘total_redeem’: 746228358.0089581}

Health factor: 1.76

Summary:

All borrowers exhibit strong credit scores and deep borrowing histories, coupled with strong current health factors. The only address with slightly light history being 0xd74f186194ab9219fafac5c2fe4b3270169666db with only 9 total borrows.