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One of the core assumptions behind financial models is that prices follow a geometric brownian motion (GBM) --eg. Black-Scholes.
Does the price of the most traded asset in Uniswap v3 also follow a GBM?
Let's find out! 👇
We will analyze the 5, 30, and 100bps ETH-USDC UniV3 pools.
The 5bps pool gets 90% of all volume and, zooming in on the price action, appear to have a smaller "per trade impact" than the 30 and 100bps pools
What is the size distribution of the price jumps for each pool?
If the price were a GBM, then the size of the price jumps would be normally distributed.
Instead, the price jumps are narrowly peaks around size~0.
They also have a kurtosis that's much larger than a normal distribution's (high kurtosis = long-tail event are more likely).
Indeed, the distributions can't be fit to a gaussian. An exponential distribution also fails to cover the distribution's "long-tail".
Surprisingly, the size of each price jump fits very closely to a power law distribution.
(CT: tag someone who may know why it's a power law)
Key insights:
- The 5bps pool gets 90% of all USDC volume
- The 5bps pool likely follows the CEX price more closely due to its 0.05% arbitrage fee
- The distribution of price jumps does not follow a normal distribution
- The price jumps follow a power law distribution (!)
Questions/Caveats:
- Can a GBM have a power law random process?
- What is the impact of a large kurtosis on price action?
- These distribution may be colored by uneven time steps
- A lot of the volume is arbitrage and not reflect "organic" order flow
We may tackles these questions in a future.
In the meantime, please follow @Panoptic_xyz for your daily dose of #ResearchBites and other important updates!
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This content is for educational purposes only and should not be relied upon as financial advice.
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(TL;DR) It is really encoding for a ETH-SHIB Reverse Jade Lizard as hinted (!)
We'll show below how it encodes for:
• 1 long put @ 7509 x 10^-12
• 1 short put @ 8070 x 10^-12
• 1 short call @ 8936 x 10^-12
2/ So first of all, let's get back to how positions are tracked by the UniswapV3Pool.sol smart contract
Any liquidity deployed by 𝚘𝚠𝚗𝚎𝚛 between a 𝚝𝚒𝚌𝚔𝙻𝚘𝚠𝚎𝚛 and 𝚝𝚒𝚌𝚔𝚄𝚙𝚙𝚎𝚛 is stored in a mapping defined by the keccak256 hash of (owner, tickLower, tickUpper)
The width of a position should match your risk tolerance. A narrow position will rapidly convert between token and ETH in a way that is similar to pin risk in options.
Use r<1.07 if active traders and 1.07 < r < 1.75 for most traders, with r=√(Pb/Pa)
2/ First, we have to start from the relationship between the fee tier in Uniswap v3, the daily volume, and the liquidity locked at the currently traded tick.
One can combine these qty to create an "invariant" that is the same across different pools if the track the same asset.
3/ What we did next is to *invert this expression* so that the feeTier is instead floating, and the implied volatility is constant.
This means that specifying an IV will dynamically adjust the feeTier for each trade according to the trade size and the amount of tickLiquidity
1/ First, what is gamma? Gamma is a measure of the "convexity" of an asset.
An asset with a positive convexity will increase in value faster as its price goes up. OTOH, an asset with negative convexity will see diminishing returns.
Here's a graph showing how gamma affects price:
2/ Formally, Gamma is defined as the second derivative of the value of an asset. Recall that delta is the first derivative of the value of an asset
Delta = slope, Gamma = slope of the slope
Here's the gamma and delta for a positive convexity instrument like a long call option.