I'm interested in modeling NFT Floor Price. Specifically, I'm trying to answer the question:
Given current bid-ask info on an NFT collection, what is the probability distribution of the lowest ask on any NFT in the collection at some future time t?
So far, I've tried:
- Searching for existing literature on modeling NFTs. I've found a lot about how to try to speculate on the price of a specific NFT, but very little on modeling them in the abstract.
- Searching for literature on modeling low-liquidity assets, including real-estate. I've found a lot about valuation, but very little about distributions.
- Modeling NFTs in a collection as 10,000 highly correlated assets with geometric brownian motion and then finding the minimum. Not only is this computationally intensive, but the massive covariance matrix is virtually impossible to estimate properly.
- Treating the floor price itself as a geometric brownian motion variable. While this is much more tractable, I'm not sure if it's theoretically sound. It also doesn't account for discrete jumps in the floor price and assymetry between upward and downward movement (the floor price can react very quickly to bullish news as speculators buy existing NFTs, but in order to drop, one of the 10k individuals in the world who own one must submit an ask lower than the current floor).
Is there any existing literature on modeling the future price distributions of illiquid assets or portfolio order statistics? Any ideas on how to modify a standard geometric brownian motion/lognormal model to incorporate bid/ask info, allow for discrete jumps, or have assymetric movement?
