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DAI digital art index: a robust price index for heterogeneous digital assets

Author

Listed:
  • Lin, Min-Bin
  • Wang, Bingling
  • Bocart, Fabian Y.R.P.
  • Hafner, Christian M.

    (Université catholique de Louvain, LIDAM/ISBA, Belgium)

  • Härdle, Wolfgang Karl

Abstract

The market of non-fungible tokens (NFTs), driven by blockchain and smart contracts, provides both artists and art collectors an unprecedented marketplace with more security, flexibility, publicity, and freedom to monetize. Yet, the emergence of such a market has been considered to be packed with speculation and economic uncertainty, given the limited understanding towards this market. To provide a precise depiction of the NFT art market and gauge market volatility, we construct the Digital Art Index, a novel price index using hedonic regression on the top 10 liquid NFT art collections (as of 2023). Addressing artwork price inequality, which often disrupts the price discovery process, this paper introduces two innovative alternative methods: Huberization and score-based filtering. These methods effectively mitigate the influence of outliers, particularly in an emerging market with limited accessible observations. In conclusion, the NFT art market presents significant opportunities for large gains, which are often favoured by risk-takers, but also carries the potential for significant losses. Its pricing is necessarily determined by institutional creators and platforms, meaning that solo artists may not benefit significantly in the current market environment.

Suggested Citation

  • Lin, Min-Bin & Wang, Bingling & Bocart, Fabian Y.R.P. & Hafner, Christian M. & Härdle, Wolfgang Karl, 2026. "DAI digital art index: a robust price index for heterogeneous digital assets," LIDAM Reprints ISBA 2026002, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
  • Handle: RePEc:aiz:louvar:2026002
    DOI: https://doi.org/10.1093/jrsssa/qnag008
    Note: In: Journal of the Royal Statistical Society Series A: Statistics in Society, 2026
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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • Z11 - Other Special Topics - - Cultural Economics - - - Economics of the Arts and Literature

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