Correlations versus noise in the NFT market

ArXiv ID: 2404.15495 “View on arXiv”

Authors: Unknown

Abstract

The non-fungible token (NFT) market emerges as a recent trading innovation leveraging blockchain technology, mirroring the dynamics of the cryptocurrency market. The current study is based on the capitalization changes and transaction volumes across a large number of token collections on the Ethereum platform. In order to deepen the understanding of the market dynamics, the collection-collection dependencies are examined by using the multivariate formalism of detrended correlation coefficient and correlation matrix. It appears that correlation strength is lower here than that observed in previously studied markets. Consequently, the eigenvalue spectra of the correlation matrix more closely follow the Marchenko-Pastur distribution, still, some departures indicating the existence of correlations remain. The comparison of results obtained from the correlation matrix built from the Pearson coefficients and, independently, from the detrended cross-correlation coefficients suggests that the global correlations in the NFT market arise from higher frequency fluctuations. Corresponding minimal spanning trees (MSTs) for capitalization variability exhibit a scale-free character while, for the number of transactions, they are somewhat more decentralized.

Keywords: Non-fungible Tokens (NFTs), Detrended Correlation Coefficient, Minimal Spanning Trees (MSTs), Marchenko-Pastur Distribution, Blockchain, Cryptocurrency/Non-fungible Tokens

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced multivariate statistical techniques like detrended correlation coefficients, Random Matrix Theory (Marchenko-Pastur distribution), and Minimal Spanning Trees (MSTs), indicating high mathematical complexity. It is highly data-driven, utilizing a large set of real-world transaction data (90 collections over 500 days) and conducting rigorous statistical comparisons between Pearson and detrended coefficients.

  flowchart TD
    A["Research Goal:<br>Understand NFT Market Dynamics<br>& Collection Dependencies"] --> B["Data Source:<br>Ethereum NFT Collections<br>(Capitalization & Transaction Volumes)"]
    B --> C["Methodology 1:<br>Calculate Detrended<br>Correlation Coefficient"]
    B --> D["Methodology 2:<br>Construct Minimal<br>Spanning Trees MST"]
    C --> E["Compute Correlation Matrix &<br>Eigenvalue Spectra"]
    D --> F["Analyze Network Structure<br>Scale-free vs. Decentralized"]
    E --> G["Key Findings/Outcomes"]
    F --> G
    G --> H["Lower correlation strength<br>vs. traditional markets"]
    G --> I["Correlations driven by<br>high-frequency fluctuations"]
    G --> J["Marchenko-Pastur comparison<br>confirms limited correlations"]
    G --> K["Capitalization MST is<br>scale-free; Transaction MST<br>is decentralized"]