Gauntlet’s BASE/Moonbeam/Moonriver Recommendations (2024-04-30)

Base Recommendations

Simple Summary

Risk Parameters

A proposal to adjust 6 risk parameters:

Risk Parameter Current Value Recommended Value
cbETH Borrow Cap 2,100 2,520
wstETH Borrow Cap 1,250 1,750
rETH Borrow Cap 250 350
USDbC Supply Cap 5,000,000 1,000,000
USDbC Borrow Cap 4,000,000 750,000
AERO CF 0% 65%

*Cap Recommendations will be implemented via Guardian

IR Parameters

A proposal to adjust cbETH, wstETH and rETH’s IR curve:

cbETH IR Parameters Current Recommended
Base 0 0
Kink 0.45 0.35
Multiplier 0.05 0.075
Jump Multiplier 3 3.5
wstETH IR Parameters Current Recommended
Base 0 0
Kink 0.45 0.35
Multiplier 0.07 0.075
Jump Multiplier 3.15 3.5
rETH IR Parameters Current Recommended
Base 0 0
Kink 0.45 0.35
Multiplier 0.07 0.075
Jump Multiplier 3.15 3.5

Rationale:

Risk Parameters

Gauntlet recommends increasing supply caps for cbETH and borrow caps for wstETH, rETH. Gauntlet also recommends making changes to uOptimal for LSTs as well as DAI and USDbC to improve capital efficiency.

Cap Recommendations

Gauntlet suggests implementing a comprehensive adjustment to borrow caps across both LSTs and USDbC to optimize protocol performance and mitigate risks. This includes:

  • wstETH - In accordance with Gauntlet’s methodology for managing parameters, we propose scaling up the borrow caps for wstETH (1,250->1,750). Among the top three positions, two are leveraging stablecoins against their LST/WETH collateral. These two positions exhibit health factors of 1.7 and 2.02, respectively, whereas the remaining positions are leveraging correlated assets. This adjustment aims to optimize the system by aligning borrow caps with the reduction of the kink (45%->30%). At maximum utilization, the borrow cap should reach 35% of the maximum supply cap, while the introduction of a high slope 2 in this context leads to higher Annual Percentage Rates (APRs), which helps to ensure that utilization remains at the 30% uOptimal threshold. Additionally, previous increases in Reserve Factors contribute to maintaining a balanced approach between capital efficiency and risk management.
    wstETH

  • cbETH - In accordance with Gauntlet’s methodology for managing parameters, we propose scaling up the borrow caps for wstETH (2,100->2,520). All cbETH postions are either employing correlated/recursive strategies or are only supplying assets. This adjustment aims to optimize the system by aligning borrow caps with the reduction of the kink (45%->30%). At maximum utilization, the borrow cap should reach 35% of the maximum supply cap, while the introduction of a high slope 2 in this context leads to higher Annual Percentage Rates (APRs), which help to ensure that utilization remains at the 30% uOptimal threshold. Additionally, previous increases in Reserve Factors contribute to maintaining a balanced approach between capital efficiency and risk management.
    cbETH

  • rETH - In accordance with Gauntlet’s methodology for managing parameters, we propose scaling up the borrow caps for wstETH (250->350). 3 positions in the top 10 are borrowing stables against their rETH/Correlated asset collateral, while the rest are borrowing recursively and 3 positions are only supplying. This adjustment aims to optimize the system by aligning borrow caps with the reduction of the kink (45%->30%). At maximum utilization, the borrow cap should reach 35% of the maximum supply cap, while the introduction of a high slope 2 in this context leads to higher Annual Percentage Rates (APRs), which help to ensure that utilization remains at the 30% uOptimal threshold. Additionally, previous increases in Reserve Factors contribute to maintaining a balanced approach between capital efficiency and risk management.
    rETH

  • USDbC - Adjusting USDbC supply (5,000,000 → 1,000,000) and borrow caps (4,000,000 → 750,000) based on current market demand for USDbC, this move further supports the migration of USDbC in favor of USDC. Currently all positions are either employing recursive strategies or are only supplying collateral. The caps are structured to ensure that at maximum cap utilization, they revert to the 70% uOptimal level.
    USDbC

AERO Collateral Factor

Over the last 60 days, AERO has experienced minimum daily log returns of -26.6%. We use the minimum daily log returns as a reference point for establishing the Collateral Factor (CF), suggesting a maximum CF of approximately ~75%. However, to exercise caution, we propose a CF of 65% to provide additional buffer. Gauntlet will continue to monitor market demand and liquidity for this asset and propose CF changes accordingly

BASE Circulating Tokens and Supply Cap

Asset Borrow Cap Supply Cap Borrow Cap Usage Supply Cap Usage DEX 25pct Slippage Token DEX 25pct Slippage USD
cbETH 2,100 7,200 56.39% 76.97% 1,151 $4,009,302.91
WETH 28,000 40,000 70.40% 73.08% 1,405 $4,585,722.67
USDC 52,000,000 56,000,000 58.48% 63.28% 10,561,490 $10,561,490.15
DAI 1,500,000 2,000,000 62.65% 69.74% 444,533 $444,544.53
wstETH 1,250 4,000 47.78% 89.51% 869 $3,301,567.27
USDbC 4,000,000 5,000,000 5.69% 7.56% 8,425,115 $8,425,114.86
rETH 250 1,000 93.82% 91.37% 586 $2,105,074.14

IR Parameters

LST IR Parameters

We recommend reducing kink from 45% to 35% based on the prevailing 30D utilization for all LST assets (cbETH, wstETH, rETH). Although the proposed kink reduction is still higher than market utilization, Gauntlet will monitor the market further to make any further changes to kink.

Demand for LST borrowing

Utilization for LST assets have consistently remained below the current kink (45%). The suggested reduction in kink will increase capital efficiency. This recommendation further bolsters protocol growth, with previous increases in supply caps for rETH, wstETH and cbETH and setting the borrow cap higher than current at 35% of the new supply cap. Additionally, it proposes adjusting the interest rate to target an optimal rate of 35%.

Increasing the borrowing capacity will enhance collateral yield, triggering further supply. This reduction of kink along with increased slope1 and slope2 can facilitate higher borrow caps while balancing risk. Furthemore, this move could also potentially incentivize WETH borrowing.

Gauntlet will continue to monitor borrow rates across the market and utilization on Moonwell to further adjust IR parameters.

Recommended LST IR Curve

Projected APRs for cbETH, wstETH and rETH

Utilization Borrow APR Supply APR
0 0 0
kink 6.03 4.61
100 95.94 81.54

Current APRs for wstETH and rETH

Utilization Borrow APR Supply APR
0 0 0
kink 5.19 3.53
100 99.95 74.96

Current APRs for wstETH and rETH

Utilization Borrow APR Supply APR
0 0 0
kink 5.19 3.53
100 99.95 74.96

Gauntlet will monitor the utilization of cbETH, wstETH and rETH and consider additional actions if necessary.

Moonbeam Recommendations

Simple Summary

A proposal to adjust 7 total risk parameters:

Parameter Current Value Recommended Value
USDC.wh Borrow Cap 2,400,000 1,800,000
xcUSDT Reserve Factor 25% 30%
xcUSDC Reserve Factor 25% 30%
USDC.wh Reserve Factor 25% 30%
USDC.wh Collateral Factor 62% 59%
WBTC.wh Collateral Factor 35% 32%
ETH.wh Collateral Factor 52% 49%

A proposal to make a IR curve adjustments for USDC.wh:

USDC.wh IR Parameters Current Recommended
BASE 0 0
Kink 0.8 0.75
Multiplier 0.0875 0.11
Jump Multiplier 7.4 7.4

A proposal to make an IR curve adjustments for xcUSDC:

xcUSDC IR Parameters Current Recommended
BASE 0 0
Kink 0.8 0.75
Multiplier 0.0875 0.11
Jump Multiplier 7.4 7.4

A proposal to make a IR curve adjustments for xcUSDT:

xcUSDT IR Parameters Current Recommended
BASE 0 0
Kink 0.8 0.75
Multiplier 0.0875 0.11
Jump Multiplier 7.4 7.4

A proposal to make an IR curve adjustments for FRAX:

FRAX IR Parameters Current Recommended
BASE 0 0
Kink 0.8 0.75
Multiplier 0.0563 0.11
Jump Multiplier 4 7.4

Rationale

Risk Parameters

Gauntlet recommends reducing collateral factor for wormhole assets while increasing Reserve Factor for stablecoins to boost reserves. It is also recommended to reduce borrow caps for USDC.wh due to low utilization and in the interest of capping the risk to the protocol.

USDC.wh Borrow Caps

The liquidity for USDC.wh has consistently remained below 30%, indicating that the borrow caps are set higher than the current utilization levels. Gauntlet suggests reducing the borrow caps for these USDC.wh to effectively manage and limit risk exposure.

USDC.wh Liquidity

Collateral Factor for Wormhole assets

With degrading liquidity on-chain as well as risks associated with bridged assets, Gauntlet recommends reducing CFs for all wormhole assets to cap risk to the protocol. The reduction in CFs will only incur 1 liquidation:

address supply_balance supply_balance_usd borrow_balance borrow_balance_usd borrowing_power_usd collateral_usd borrow_usage collateralization_ratio health_factor
Address 105.9 105.9 65.5 65.5 65.6 105.9 1.0 1.6 1.0

xcUSDC, USDC.wh and xcUSDT Reserve Factor

An increase in reserve factor for xcUSDC and xcUSDT will boost projected annual reserves by $14,442 while incentivizing more organic borrowing over recursive borrowing. Gauntlet will monitor this market to see the impact of reserve factor on current user positions.

IR Parameters

Utilization for stablecoin assets have remained under control since the previous recs where utilization remained above kink for sustained periods. To continue fine-tuning stablecoin utilization keeping circulating supply and liquidity in mind, Gauntlet recommends to lower kink while increasing Multiplier and Jump Mulitplier for the USDC.wh, xcUSDT and xcUSDC. The Mulitplier will move borrow APR at kink by ~125bps. Post the succses of MIP-M22, we continue to assess market demand for FRAX, although on-chain liquidity is extremely low, the existing FRAX bridge could potentially seed the market by bringing more FRAX on-chain. Gauntlet recommends adjusting slope 1, slope 2 and kink to same values as other stablecoin assets in the market, however, the reserve factor would remain at 15% to further incentivize supplying FRAX.

Gauntlet will monitor the impact of these adjustments and make any further adjustments if necessary to maintain utlization closer to the kink.

USDC.wh and xcUSDT utilization
Screenshot 2024-04-30 at 5.35.46 PM

xcUSDC utilization
Screenshot 2024-04-30 at 5.36.46 PM

USDC.wh

IR Curve for USDC.wh

Recommended Borrow and Supply APR for USDC.wh

Utilization Borrow APR Supply APR
0 0 0
kink 8.25 4.33
100 193.25 135.27

Current Borrow & Supply APR for USDC.wh

Utilization Borrow APR Supply APR
0 0 0
kink 7 4.2
100 155 116

xcUSDC

IR Curve for xcUSDC

Recommended Borrow and Supply APR for xcUSDC

Utilization Borrow APR Supply APR
0 0 0
kink 8.25 4.33
100 193.25 135.27

Current Borrow & Supply APR for xcUSDC

Utilization Borrow APR Supply APR
0 0 0
kink 7 4.2
100 155 116

xcUSDT

IR Curve for xcUSDT

Recommended Borrow and Supply APR for xcUSDT

Utilization Borrow APR Supply APR
0 0 0
kink 8.25 4.33
100 193.25 135.27

Current Borrow & Supply APR for xcUSDT

Utilization Borrow APR Supply APR
0 0 0
kink 7 4.2
100 155 116

FRAX

IR Curve for FRAX

Recommended Borrow and Supply APR for FRAX

Utilization Borrow APR Supply APR
0 0 0
80 8.25 5.25
100 84.5 71.82

Current Borrow & Supply APR for FRAX

Utilization Borrow APR Supply APR
0 0 0
80 4.5 3.0
100 84.5 71.82

Supporting Data

Moonbeam 2% Market Depth for non-Stables

Risk Dashboard

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

VaR (Value at Risk) is defined as the expected insolvent amount (defined as excess debt relative to collaterals for any account) in a given day for a protocol under extremely adverse market conditions. We use our models to pre-configure specific risky market scenarios and stress test protocols via simulations leveraging current user positions, asset prices, and varied liquidity conditions. VaR is an estimate of the expected insolvencies for a single day given a severe correlated market downturn.

LaR is calculated as the expected liquidation amount under extremely adverse market conditions. It represents our estimation on what would happen if the market crashes, etc.

Moonriver recommendations

Simple Summary

A proposal to adjust 5 total risk parameter:

Parameter Current Value Recommended Value
WMOVR Reserve Factor 25% 35%
xcKSM Reserve Factor 25% 35%
FRAX Reserve Factor 25% 35%
FRAX Collateral Factor 53% 50%
FRAX Borrow Cap 270,000 200,000

A proposal to make an IR curve adjustments for FRAX and xcKSM:

FRAX IR Parameters Current Recommended
BASE 0 0
Kink 0.8 0.8
Multiplier 0.0875 0.08
Jump Multiplier 7.4 7
xcKSM IR Parameters Current Recommended
BASE 0.02 0.02
Kink 0.6 0.45
Multiplier 0.15 0.2
Jump Multiplier 3 3.5

Rationale:

WMOVR Liquidity

xcKSM Liquidity

FRAX Liquidity

The overall liquidity on Moonriver ecosystem has been on a consitent decline. The deterioration of liquidity for WMOVR, xcKSM and FRAX call for more conservative parameters with increase in Reserve Factors, reduction in Collateral Factors(where possible), Borrow caps and more aggresive IR increases. Since the current borrow cap for FRAX is 2x that of amount borrowed, we recommended reducing caps 200k.

IR Parameters

FRAX utilization continues to be consistently high over extended periods, largely due to the accumulation of interest on longstanding positions. Gauntlet suggests reducing slope 1 to establish a borrowing APR of 7% at the kink point, while setting a supply APR of 4.2%. This adjustment aims to reduce interest on longstanding debt positions.

Screenshot 2024-04-30 at 3.10.54 PM

Recommended FRAX IR Curve

Projected APRs

Utilization Borrow APR Supply APR
0 0 0
80 6.4 3.3
100 169.6 110.24

Current APRs

Utilization Borrow APR Supply APR
0 0 0
80 7 4.2
100 155 116.24

Recommended xcKSM IR Curve

Screenshot 2024-04-30 at 2.14.28 PM

xcKSM’s utilization has been under the kink (<60%) for elongated periods, therefore we recommend reducing kink from 60% to 35% (Closer to current utilization rates) to improve capital efficiency as well reducing risk. Increasing slope1 and slope2 helps keep utilization low under the current liquidity circumstances as well help bolster protocol reserves.

Projected APRs

Utilization Borrow APR Supply APR
0 2 0
35 11 3.21
100 203 132.27

Current APRs

Utilization Borrow APR Supply APR
0 2 0
60 11 4.95
100 131 98.24

Moonriver On-chain Liquidity

In shaping our recommendations via our risk models, we consistently monitor market liquidity and concentration risks pertaining to the Moonriver protocol. We aim to provide the community with pivotal liquidity metrics for Moonriver assets. The liquidity on Moonriver has not shown improvement since our last reccomendation cycle. We recommend to continue monitoring the Moonriver market and ecosystem.

Risk Dashboard

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

VaR (Value at Risk) is defined as the expected insolvent amount (defined as excess debt relative to collaterals for any account) in a given day for a protocol under extremely adverse market conditions. We use our models to pre-configure specific risky market scenarios and stress test protocols via simulations leveraging current user positions, asset prices, and varied liquidity conditions. VaR is an estimate of the expected insolvencies for a single day given a severe correlated market downturn.

LaR is calculated as the expected liquidation amount under extremely adverse market conditions. It represents our estimation on what would happen if the market crashes, etc…

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. To learn more about our methodologies, please see the Helpful Links section at the bottom.

Quick Links

Please click below to learn about our methodologies:

Gauntlet Parameter Recommendation Methodology
Gauntlet Model Methodology

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