Risk model

Liquidity Farming Model

We have developed a risk model to calculate the optimal range for LP positions in our Stablecoin Vault. This model is based on the concept that market conditions can transition between trending and accumulating phases. In other words, market volatility can experience significant fluctuations during trending periods and remain relatively stable during accumulating phases. Our model is designed to estimate this volatility in order to determine the optimal range for LP positions, with the ultimate goal of maximizing rewards and optimizing the duration of being in-range.

Here is our first simplest version, we calculate SMA:

SMA=∑𝑖=1𝑁𝑃𝑖𝑁SMA=N∑i=1N​Pi​​

Where 𝑃𝑖Pi​ is the price at each period 𝑖i, and 𝑁N is the total number of periods.

Next, calculate the standard deviation of the price over the same 𝑁N periods to measure volatility. The standard deviation tells you how much the price varies from the SMA.

𝜎=∑𝑖=1𝑁(𝑃𝑖−SMA)2𝑁σ=N∑i=1N​(Pi​−SMA)2​​

Using the SMA and the volatility 𝜎σ define the upper and lower bounds of this range can be calculated as follows:

- Upper Range (UR): SMA+𝑘1×𝜎SMA+k1​×σ

- Lower Range (LR): SMA−𝑘1×𝜎SMA−k1​×σ

In accordance with market conditions, the values of 𝑘1k1​ and 𝑘2k2​ will be dynamically adjusted, where 𝑘1k1​ represents the number of standard deviations for the upper band and 𝑘2k2​ pertains to the number of standard deviations for the lower band. For instance, during an uptrending market scenario, we will set 𝑘1k1​ = 2 and 𝑘2k2​ = 1, thereby providing sufficient room for price appreciation while ensuring that positions remain within the acceptable range. Conversely, these values will be adapted differently in other market conditions as warranted.

Conclusion: We will continuously monitor the LP position in real-time. In the event that the position goes out of the calculated optimal range after a specified duration, our model will automatically re-create a new LP position. Estimating the timeframe for allowing the position to go out of range before re-creating it helps reduce costs in cases where the market experiences sudden and significant volatility before returning to its normal range.

Option OTM Model

Risk Model Options Based on Mean-Reversion Principle

Introduction: The risk model options have been developed based on the mean-reversion principle. We will take short positions when the price increases significantly above the moving average price and go long when the price decreases below the moving average.

Model Development: Based on this theory, the development team has created a model to determine suitable Long/Short points in the market:

Definitions:

• 𝑆S: Residual Deviation determined by the model.

• 𝑄1Q1​: Top positive quantile of the distribution of S.

• 𝑄2Q2​: The quantile where the price is accumulating.

• 𝑄3Q3​: Top negative quantile of the distribution of S.

Weekly Procedure: Every Friday, after the options expiry time, the model will calculate S. Based on the value of S, if:

• 𝑆S > 𝑄1Q1​: Sell covered calls with a position size of 10% of the current holding value of ETH.

• 𝑄3Q3​ < 𝑆S < 𝑄1Q1​: Sell covered calls and covered puts with a position size of 7% for each position.

• 𝑆S < 𝑄3Q3​: Sell covered puts with a position size of 10%.

Conclusion: This model aims to utilize mean-reversion principles to make informed decisions regarding Long/Short positions in the market, thereby managing risks effectively.