# Testnet Parameters

A brief overview of the logical framework we use for parameter setting and the factors we consider, using the initialized ETH parameters as an example. read more at https://www.lyra.finance

If you’ve read the Lyra whitepaper, you may have noticed that there are a few parameters requiring initialization prior to launch. This post will give a brief overview of the logical framework we use for parameter setting and the factors we consider, using the initialized ETH parameters as an example. Let's get into it!

## AMM Parameters

**Standard Size: 20**

Standard size is the way the mechanism determines the degree of implied volatility (IV) slippage for a given trade. Setting the standard size to 20 means that for every 20 contracts bought (sold) in a given expiry, the AMM will increase (decrease) baseline IV by 1 percentage point. The higher the standard size, the less sensitive the AMM’s pricing is to a given trade (i.e. lower slippage) and vice versa. Picking a suitable standard size is a decision which must balance the needs of traders (lower slippage) with the needs of the AMM (lower impermanent loss).

**Skew Ratio Impact: 0.0125**

The skew ratio determines the ratio of the volatility of the option traded to the baseline volatility of its expiration. The skew ratio impact parameter determines how much this ratio is adjusted for a given standard size traded. You can think of this parameter as determining how much we change the price of the specific option being traded (strike/expiry combo) versus the price of the options which share that expiry. The higher this number is, the more slippage for a given standard size.

**Example**

At the time of writing the ETH 50d call with 4 days to expiry has $0.85 of vega on Deribit (with an IV of 110%). If the Lyra system has a baseline IV for the 4 days-to-go expiration of 100%, with a skew ratio of 1.1, the volatility of the 50d call equals 110%. If a user were to buy 20 calls (1 standard size), the baseline IV would increase to 101% and the skew ratio would increase to 1.1125, leading to a trade volatility of 112.36%. This results in a price slippage of approximately $2.00 per option (assuming vega remains close to $0.85).

### Fee Parameters

Fee parameters are another point of tension between LPs and traders within the system. Ultimately, these parameters will be determined via a governance process, with LPs driving the decision making around spread width and risk tolerance for each asset. **Vega Fee Coefficient**

The vega fee coefficient is a multiplier of the raw vega utilization percentage which yields the vega-dependent component of the fee. The ETH vega fee coefficient deployed to testnet is 200. Given this, if vega utilization is 2% and the AMM is net short vega, new option buyers will be charged an extra 100 * 0.02 = $4 fee per option. The vega fee coefficient needs to be scaled up for higher dollar assets (like BTC) and down for lower dollar assets (like LINK or UNI). For example, the current testnet coefficient for BTC is 3000 and the UNI coefficient is 5.

Riskier assets should generally command a higher vega fee coefficient (relative to their asset price). This is because taking large vega positions in volatile assets leads to large swings in P&L, and liquidity providers should be compensated at a higher rate for the increased risk.

**Option/Spot Price Fee Coefficient**

These parameters are simply percentages of the option/spot prices that are charged as part of the spread. Similar to the vega fee coefficient, these should be higher for more volatile, less liquid assets, and lower for more established, lower volatility ones. For ETH, we set the spot price fee to be 0.0025 (~$5.50 at time of trading) with the option price fee 0.005. We selected these to create realistic spreads that were close to those typically seen on centralized exchanges like Deribit.

### Summary

This post has provided a brief overview of the high level concerns we considered when initializing the parameters for testnet. The full list of initial parameters (all assets) is published below.

We have stress-tested the mechanism under adverse conditions (i.e. in situations where volatility spikes from 120% -> 300%, as it did in May) and examined the impermanent losses realized by the AMM using a harsh set of assumptions (i.e. 0 fees, optimized execution from market participants). The research detailing these results will be published shortly, and will led some quantitative reasoning to the qualitative factors considered in this post.

### Testnet Parameters

**ETH**

Standard size = 20

Skew ratio impact = 0.0125

spotPriceFeeCoefficient = 0.0025

optionPriceFeeCoefficient = 0.005

vegaFeeCoefficient = 200

**BTC**

Standard size = 1.2

Skew ratio impact = 0.0125

spotPriceFeeCoefficient = 0.0025

optionPriceFeeCoefficient = 0.005

vegaFeeCoefficient = 3000

**LINK **

Standard size = 200

Skew ratio impact = 0.0125

spotPriceFeeCoefficient = 0.003

optionPriceFeeCoefficient = 0.03

vegaFeeCoefficient = 5

**UNI **

Standard size = 200

Skew ratio impact = 0.0125

spotPriceFeeCoefficient = 0.003

optionPriceFeeCoefficient = 0.03

vegaFeeCoefficient = 5

**AAVE **

Standard size = 20

Skew ratio impact = 0.0125

spotPriceFeeCoefficient = 0.003

optionPriceFeeCoefficient = 0.03

vegaFeeCoefficient = 50

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