In the past years, the research community has been granted the rare chance to witness the birth and growth of new financial markets. The cryptocurrency market, launched in 2010, has undergone several phases, evolving from its early days through a period of emerging market characteristics still in development to its current state of relative maturity. The non-fungible token (NFT) market emerges as a recent trading innovation leveraging blockchain technology, mirroring the dynamics of the cryptocurrency market. Despite the NFT market's brief existence and limited liquidity, enough data exists to make rather reliable estimates toward establishing the related analogs of the so-called stylized facts.
In the presentation, I will show the result from a recent paper in which statistical properties of the NFT market trading characteristics (collection capitalization, floor price, the number of transactions, the inter-transaction times, and the transaction volume value) were analyzed. The results show that the fluctuations of all these quantities are characterized by heavy-tailed probability distribution functions, in most cases well described by the stretched exponentials, with a trace of power-law scaling at times, long-range memory, persistence, and in several cases, even the fractal organization of fluctuations, mostly restricted to the larger fluctuations. I will also present the most recent results of research on the correlation characteristics between NFT collections, where the degree of correlation in the NFT market is examined using the formalism of detrended correlation coefficient and correlation matrix. The obtained results are compared with the predictions of random matrix theory.
The main conclusion is that the NFT market—even though young and governed by somewhat different mechanisms of trading shares several statistical properties with the regular financial markets. However, some differences are visible in the specific quantitative indicators.
