---
# System prepended metadata

title: MIP 32 Apollo Risk Parameter Recs (2023-03-01)

---

# MIP 32 Apollo Risk Parameter Recs (2023-03-01)

<!--
{
	"mETH": {
		borrowCapInWholeTokens: 1500
	},
	"mxcKSM": {
		borrowCapInWholeTokens: 32000
	}
}
-->

## Apollo Summary
We recommend the following parameter changes:
- **Decrease ETH.multi borrow cap from 7,000 to 1,500**
- **Decrease xcKSM borrow cap from 70,000 to 32,000**


Rationale:
The VaR is $0 and our CF recommendations will leave it unchanged. The LaR is $202k and our recommendations will leave it unchanged. WMOVR has a VaR of $0 and a LaR of $120k. xcKSM has a VaR of $0 and a LaR of $79k. ETH.multi, FRAX, USDC.multi, and USDT.multi each have a VaR and LaR of $0. 


ETH.multi, FRAX, USDC.multi, USDT.multi, WMOVR, and xcKSM's collateral factors are effectively balancing risk and capital efficiency. 


Gauntlet analyzes assets’ on-chain liquidity and potential risk of short market manipulation attack to assess borrow cap recommendations. Gauntlet recommends decreasing the borrow caps for ETH.multi and xcKSM because of recent decreases in liquidity on the Moonriver chain and borrow cap utilization being below 20% since mid February.
### xcKSM Borrow Tokens since Jan 1st
![](https://i.imgur.com/6HjwfYM.png)
### ETH.multi Borrow Tokens since Jan 1st
![](https://i.imgur.com/2UJHWXd.png)




## 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
![](https://i.imgur.com/nAMPFBQ.png)



### Top 10 Borrowers’ Entire Supply
![](https://i.imgur.com/rPrlFdW.png)



### Top 10 Borrowers’ Entire Borrows
![](https://i.imgur.com/QyENDMH.png)



![](https://i.imgur.com/8vLuwyL.png)



![](https://i.imgur.com/685Frrq.png)





## Risk Dashboard
The community should use Gauntlet’s [Gauntlet’s Apollo Risk Dashboard](https://gov.gauntlet.network/apollo) to better understand the updated parameter suggestions and general market risk in Apollo. 

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.
![](https://i.imgur.com/dCfV7Je.png)
# 



## Quick Links

*Please click below to learn about our methodologies:*

[Gauntlet Parameter Recommendation Methodology](https://medium.com/gauntlet-networks/gauntlets-parameter-recommendation-methodology-8591478a0c1c)
[Gauntlet Model Methodology](https://medium.com/gauntlet-networks/gauntlets-model-methodology-ea150ff0bafd)

*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.*