Apollo Simple Summary
A proposal to adjust 4 total risk parameters, including Collateral Factor and Borrow Cap across 3 assets.
| Parameter | Current Value | Recommended Value |
|---|---|---|
| ETH.multi Borrow Cap | 700 | 110 |
| ETH.multi Collateral Factor | 64% | 62% |
| USDC.multi Borrow Cap | 9,727,000 | 2,200,000 |
| USDT.multi Borrow Cap | 600,000 | 250,000 |
Gauntlet proposes adjusting interest rate parameters for 2 assets. Tokens impacted include the following:
| IR Parameters | USDC.multi | USDT.multi |
|---|---|---|
| Base | 0.0 | 0.0 |
| Kink | 0.8 | 0.8 |
| Multiplier | 0.05 | 0.05 |
| Jump Multiplier | 2.5 → 3.175 | 2.5 → 3.175 |
| Reserve Factor | 0.15 | 0.15 |
CF/Borrow Cap Rec Supporting Data
CF Rationale:
Apollo’s VaR is $0 and our recommendations will leave it unchanged. The LaR is $58k and our recommendations will leave it unchanged. USDC.multi, USDT.multi, WMOVR, FRAX, and xcKSM’s collateral factors are effectively balancing risk and capital efficiency.
ETH.multi can be de-risked with relatively small impact in capital efficiency, so we recommend decreasing its collateral factor to reduce overall risk. ETH.multi’s 25% market depth on Solarbeam is at $37k while collateral usage is at $210k. As of now, we recommend reducing the collateral factor by 200 bps in order to prevent any liquidations which might impact user experience.
Visualizing Gauntlet’s internal on-chain liquidity data, the 30% reduction on ETH.multi’s 2% market depth post Multichain market stress is evident. We continue to still monitor these liquidity levels and will provide further risk recommendations if necessary.
ETH.multi 2% Market Depth
MultiChain Borrow Cap Reductions
Gauntlet analyzes assets’ on-chain liquidity and potential risk of short market manipulation attack to assess borrow cap recommendations. With ETH.multi, USDC.multi, and USDT.multi’s low borrow cap usage and lower liquidity and circulating supply on Moonriver post the MultiChain market FUD, we recommend lowering their caps to reduce market and exploitation risk to the protocol.
Multichain assets have exited from the Apollo protocol and Moonriver chain during the Multichain market stress. As shown in the chart below, MultiChain assets have experienced a large decrease in circulating supply of tokens on the Moonriver chain from May 22nd until now. ETH.multi and USDC.multi experienced the largest outflow of tokens with 70% and 54% reduction, respectively.
Circulating Supply Decrease for Multi Assets
Borrow Amounts for the MultiChain Assets experience a large decrease during the market stress period. As such, our recommendation to reduce borrow caps protects the protocol from liquidity concerns and potential market exploitation.
Borrow By Asset - Timeseries
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.
Supporting Data
The below figures show trends on key market statistics regarding borrows and utilization that we will continue to monitor:
Top 10 Borrowers’ Aggregate Positions & Borrow Usages
Top 10 Borrowers’ Entire Supply
Link to chart
Top 10 Borrowers’ Entire Borrows
Link to chart
Utilization Rate of Assets - Timeseries
Link to chart
Borrow Cap Utilization
Risk Dashboard
The community should use Gauntlet’s Apollo Risk Dashboard to understand better the updated parameter suggestions and general market risk.
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.
IR Rec Supporting Data
Abstract
Given the significant shifts in crypto markets, Gauntlet’s platform has evaluated all assets on Apollo and Artemis’ active markets and has identified opportunities to adjust parameters for certain assets for the benefit of the protocol. Our methodology makes data-informed decisions around setting borrower and supplier interest rates when market conditions require the protocol to reduce risk or when strategic opportunities present themselves to increase protocol revenue without materially impacting risk.
Methodology
Objectives
Among other factors, there are two primary reasons to adjust an interest rate curve:
- mitigate the risk of 100% utilization in a pool
- build reserves via protocol revenue to cover insolvencies or other expenses in the future
As a secondary objective, we want to optimize the user experience of Moonwell’s borrowers and suppliers.
Mitigating Risk
The first case of mitigating 100% utilization is of more immediate benefit to the protocol. High utilization is poor UX for suppliers, as it can restrict their ability to withdraw an asset from the pool. For example, if a pool contains $10M in xcUSDT, and $9M are loaned out, the maximum a supplier could withdraw is $1M since the pool cannot exceed 100% utilization. In addition to impacting suppliers, liquidations may be hindered because, at 100% utilization, only mTokens (not the underlying collateral) can be seized. If liquidators are concerned they won’t be able to cash these mTokens in for the underlying collateral in time to lock in a profit, this risks leaving the protocol with insolvent debt. Increasing interest rates can motivate borrowers to repay the asset and motivate suppliers to deposit more of the asset. Both would decrease utilization to more desirable levels.
Building Reserves
The second use case of building reserves is more opportunistic in nature. Reserves serve as the rainy day fund for protocols, protecting against unforseen events. Over time they may also be used to fund operations, reducing the reliance on the native token treasury. Moreover, interest rates can be used opportunistically to capture increased reserves when specific market conditions are met. Since the annualized reserve growth is (total borrowed) * (borrow rate) * (reserve factor), opportunities to increase revenue present themselves when:
- We can incentivize more borrowing without slashing borrow rates
- We can increase borrow rates without losing borrowers
- We can raise reserve factors without losing suppliers
Elasticity Model
The reaction of borrowers and suppliers to changes in interest rate is governed by borrower and supplier elasticity. If a borrower is elastic, they would reduce their borrowing position in response to an increased interest rate, but if a borrower is inelastic, they would ignore changes in interest rates. If a supplier is elastic, they would increase their supplying position in response to increased interest rates, but if a supplier is inelastic, they would ignore changes to interest rates.
Interest rates on Artemis and Apollo are computed as a function of utilization through the interest rate curve such that interest rates are higher when utilization is higher. As a result, there is a natural counterbalancing effect to interest rate curve changes: if borrowers or suppliers are elastic, then an increase in interest rates would be followed by borrowers reducing their positions or suppliers increasing their positions, which would bring interest rates back down.
Consider the case where either all borrowers are elastic and all suppliers are inelastic. The figure below shows the expected user behavior if we were to swap out Curve A for Curve B. At time t=1 (shown as â‘ in the figure), the borrow rate is steady at the market rate. At time t=2, we execute the IR curve change to Curve B which hikes the borrower rate. As a result, borrowers begin closing their positions due to the higher rate because they are elastic, until we get back down to the equilibrium rate at t=3.
An analagous scenario can be constructed if suppliers are elastic and borrowers are inelastic. If both borrowers and suppliers are elastic, it is harder to predict the effect on user positions because it is dependent on who is more elastic and who acts faster between borrowers and suppliers.
While this simple model is helpful in making recs, it is important to caveat that different tokens can have a mixture of elastic and inelastic users, and that no user is perfectly elastic or inelastic. It is also important to note that utilization is extremely noisy, fluctuating due to token prices, competing yield offerings, and the whims of individual users, which makes elasticity measurement noisy.
Impact Measurement
When making IR curve recs, we measure impact in 3 ways:
- Predicted immediate impact on utilization and revenue
- Counterfactual utilization
- Rate at 100% utilization
Predicted immediate impact on utilization and revenue
Based on our assumptions about borrower and supplier elasticity, we can quantify the expected change in total amounts borrowed and supplied:
- If only borrowers are elastic, then we can compute the change in amount borrowed by assuming that the borrow rate will be restored to equilibrium
- If only suppliers are elastic, then we can compute the change in amount supplied by assuming that the supply rate will be restored to equilibrium
- If borrowers and suppliers are inelastic, then we can assume that borrows and supply stay constant
Based on the new equilibrium borrows and supply, we can compute the projected utilization and projected protocol revenue.
Counterfactual utilization
Based on our assumptions about borrower and supplier elasticity, we can look at historical interest rate data to predict what utilization would have been if we were on a different IR curve. If borrowers are elastic, we can take the borrow rate at a given point in time and determine what utilization it corresponds to on a new interest rate curve. If suppliers are elastic, we could do the same with supplier interest rates.
In order to quantify the risk posed by a historical or counterfactual timeseries of utilization, we measure the percentage of time that utilization was above 90%, 95%, and 99%.
When computing counterfactual utilization, there are a few caveats that are important to keep in mind:
- During utilization spikes, users could be quite inelastic to interest rates which may cause impact to be overestimated
- For moments when utilization hit 100%, we cannot measure counterfactual utilization because we do not know the maximum interest rates that users would have tolerated
Rate at 100% utilization
The maximum borrower and supplier interest rates are used when utilization is 100%. If this rate is not high enough, there would not be enough incentive for borrowers to close their positions and suppliers to enter, leaving the protocol at risk. But if this rate is too high, we may see borrowers getting quickly liquidated from the exorbiant interest fees, which would be bad for user experience but also potentially lead to liquidation cascades.
We use the liquidation time metric to better quantify this danger of max interest rates getting too high. We define it as the time it would take for a user to get liquidated if they supply USDC and borrow 80% of its value in a given token at the max token borrow rate and the min USDC supply rate. We assume that interest compounds once per block for this calculation.
IR Recommendations
USDC.multi and USDT.multi Risk-Off Recs
Market reaction to Multichain assets the past of couple of weeks have caused some significant outflow in liquidity from the Apollo market. As such, USDC.multi and USDT.multi utilization have experienced recent spikes close to 100% utilization and had continous high utilization greater than 8 hours. Gauntlet is recommending higher Jump Multiplier to prevent high utilization.
Utilization and Cap Usage
USDC.multi
USDT.multi
IR Curve Changes
USDC.multi
USDT.multi
USDC.multi - Current and Recommended IR Params
| parameter | Current | Recommended |
|---|---|---|
| Base | 0 | 0 |
| Kink | 0.8 | 0.8 |
| Multiplier | 0.05 | 0.05 |
| Jump Multiplier | 2.5 | 3.175 |
| Reserve Factor | 0.15 | 0.15 |
USDT.multi - Current and Recommended IR Params
| parameter | Current | Recommended |
|---|---|---|
| Base | 0 | 0 |
| Kink | 0.8 | 0.8 |
| Multiplier | 0.05 | 0.05 |
| Jump Multiplier | 2.5 | 3.175 |
| Reserve Factor | 0.15 | 0.15 |
Estimated Impact of IR Recs
Max Borrow Rate
| Asset | Current | New Max Rate | Delta | Percent Change |
|---|---|---|---|---|
| USDT.multi | 71.53% | 96.28% | 24.75% | 34.60% |
| USDC.multi | 71.53% | 96.28% | 24.75% | 34.60% |
Liquidation Timing
| Asset | Current | Projected | Delta | Percent Change |
|---|---|---|---|---|
| USDT.multi | -82 days | -61 days | 21 days | -25.70% |
| USDC.multi | -82 days | -61 days | 21 days | -25.70% |
*Liquidation Timing represents the time it would take someone to get liquidated if they supply USDC.multi and borrow the max amount of that they can, when the USDC supply rate is at its min and ’s borrow rate is at its max.
Counterfactual Utilization TimeSeries
Artemis Summary
A proposal to adjust 2 parameters total risk parameters, including Borrow Cap and Collateral Factor across one asset.
| Parameter | Current Value | Recommended Value |
|---|---|---|
| xcUSDT Borrow Cap | 1,200,000 | 1,300,000 |
| xcUSDT Collateral Factor | 53% | 55% |
| USDC.wh Borrow Cap | 2,600,000 | 3,000,000 |
Gauntlet proposes adjusting interest rate parameters for 3 assets. Tokens impacted include the following:
| IR Parameters | USDC.wh | WETH.wh | xcUSDT |
|---|---|---|---|
| Base | 0 | 0.02 | 0 |
| Kink | 0.8 | 0.6 | 0.8 |
| Multiplier | 0.05 → 0.0625 | 0.15 → 0.1875 | 0.05 → 0.0625 |
| Jump Multiplier | 2.5 | 3 | 2.5 |
| Reserve Factor | 0.15 | 0.25 | 0.15 |
CF/Borrow Cap Rec Supporting Data
Rationale:
VaR is $0 and our recommendations will leave it unchanged. Our recommendations will increase LaR from $483k to $484k. xcUSDT is relatively safe from a market risk perspective, so we can gradually increased its collateral factor to improve capital efficiency. WETH.wh, WGLMR, WBTC.wh, USDC.wh, FRAX, and xcDOT’s collateral factors are effectively balancing risk and capital efficiency.
Analyzing xcUSDT positions, the top suppliers of xcUSDT have recursive stable positions. This data is supportive of our model’s recommendation to increase xcUSDT collateral factor.
Top 10 xcUSDT Supplier’s borrow positions
For USDC.wh and xcUSDT, we recommend increasing borrow caps based on stable DEX on-chain liquidity and increased demand on the protocol.
As we make recommendations through our risk models, we keep a constant check on the market liquidity and concentration risk to the Artemis protocol. In this regard, we would like to present some key liquidity figures for Artemis assets to share with the community. Since our last post, liquidity has decreased for non-stablecoins. We will continue to monitor these assets and provide further recommendation when necessary.
| Asset | Concentration Risk | Total Circulating Supply | 25% Depth | 25% Depth USD | 25% Depth on May 11th |
|---|---|---|---|---|---|
| ETH.wh | 94% | 1,992 | 55 | $98,727 | 80 |
| USDC.wh | 43% | 4,905,502 | 1,250,000 | $1,250,000 | 1,500,000 |
| WBTC.wh | 96% | 143 | 4 | $108,246 | 6 |
| xcUSDT | 20% | 1,864,992 | 1,500,000 | $1,500,000 | 1,300,000 |
| xcDOT | 33% | 971,246 | 18,000 | $95,400 | 26,000 |
| FRAX | 24% | 5,319,872 | 1,500,000 | $1,500,000 | 1,300,000 |
*Concentration Risk represents the percentage of token supply held by Artemis.
Even though none of the assets on Artemis were exposed to the Multichain bridge, we still found some impact during the Multichain FUD when investigating liquidity for Artemis assets on Moonbeam chain. Artemis assets’ liquidity decreased during the initial market news about Multichain. Some assets such as xcDOT, WETH.wh, and WGLMR experienced initial dips of 20-40% before rebounding.
Moonbeam 2% Market Depth
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.
Supporting Data
The below figures show trends on key market statistics regarding borrows and utilization that we will continue to monitor:
Top 10 Borrowers’ Aggregate Positions & Borrow Usages
Top 10 Borrowers’ Entire Supply
Link to chart
Top 10 Borrowers’ Entire Borrows
Link to chart
Utilization Rate of Assets - Timeseries
Link to chart
Borrow Cap Utilization
Risk Dashboard
The community should use 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.
IR Rec Supporting Data
Abstract
Given the significant shifts in crypto markets, Gauntlet’s platform has evaluated all assets on Apollo and Artemis’ active markets and has identified opportunities to adjust parameters for certain assets for the benefit of the protocol. Our methodology makes data-informed decisions around setting borrower and supplier interest rates when market conditions require the protocol to reduce risk or when strategic opportunities present themselves to increase protocol revenue without materially impacting risk
Recommendations
WETH.wh, USDC.wh, and xcUSDT Risk-Off Recs
WETH.wh, xcUSDT, USDC.wh have continue to utilize a high percentage of their respective Borrow cap since early April. Continous borrow cap usage signals that borrowers are likely inelastic to increases in interest rates because they would likely borrow more if the option were available.
Utilization and Cap Usage
WETH.wh
USDC.wh
xcUSDT
Given the signal for inelasticity of users to increases to borrow rates, we recommend an increase of the Multiplier for all 3 assets. This Multiplier increase will earn more revenue for the protocol
IR Curve Changes
WETH.wh
USDC.wh
xcUSDT
Current and Recommended IR Params
WETH.wh
| parameter | Current Artemis | Recommended |
|---|---|---|
| Base | 0.02 | 0.02 |
| Kink | 0.6 | 0.6 |
| Multiplier | 0.15 | 0.1875 |
| Jump Multiplier | 3 | 3 |
| Reserve Factor | 0.25 | 0.25 |
USDC.wh
| parameter | Current | Recommended |
|---|---|---|
| Base | 0 | 0 |
| Kink | 0.8 | 0.8 |
| Multiplier | 0.05 | 0.0625 |
| Jump Multiplier | 2.5 | 2.5 |
| Reserve Factor | 0.15 | 0.15 |
xcUSDT - Current and Recommended IR Params
| parameter | Current | Recommended |
|---|---|---|
| Base | 0 | 0 |
| Kink | 0.8 | 0.8 |
| Multiplier | 0.05 | 0.0625 |
| Jump Multiplier | 2.5 | 2.5 |
| Reserve Factor | 0.15 | 0.15 |
Estimated Impact of IR Recs
| Assets | Current Annualized Rev | Projected Annualized Rev | Delta | Percent Change |
|---|---|---|---|---|
| WETH.wh | $42,519 | $50,920 | $8,400 | 19.76% |
| USDC.wh | $9,799 | $12,289 | $2,490 | 25.42% |
| xcUSDT | $5,754 | $7,227 | $1,473 | 25.60% |
Quick Links
Please click below to learn about our methodologies:
Gauntlet Parameter Recommendation Methodology
Gauntlet Model Methodology
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.
Next Steps
This will be put up for an on-chain vote by June 13th.
Pass
Slippage to buy $38.2k ETH.multi = 19%
