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Ethereum risk states as a tail-risk switch for Art NFTs:Evidence from SuperRare

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  • Ziwen, Chen

Abstract

Art-NFT markets are thin and typically quoted and settled in Ethereum, so stress in the settlement asset may tighten liquidity and funding conditions in the downstream market. This paper asks whether observable Ethereum (ETH) risk states provide an ex-ante ranking of subsequent crash risk in a curated art-NFT marketplace. Using SuperRare sales aggregated to a daily price proxy (2021–2023), we sort days by (i) 7-day realized ETH volatility and (ii) the filtered high-volatility probability from a two-state Markov-switching model. Forward 30-day drawdown crashes are sharply monotone across state quartiles: a 30% USD crash rate rises from 9.9% to 38.8% from the lowest to highest volatility-probability quartile. Because crash windows overlap mechanically, conventional logit inference is overconfident; we therefore report main results as conservative linear probability models with Newey-West HAC errors and a moving-block bootstrap. Using a fully real-time state proxy, the block-bootstrap p-value for severe ETH-denominated crashes (40% drawdown) is 0.019. The signal is strongest during the 2022 market stress episode, consistent with ETH risk states activating downstream tail risks when settlement-asset stress is genuine. The settlement asset operates as a tail-risk switch for art NFTs, with limited corresponding mean-return predictability.

Suggested Citation

  • Ziwen, Chen, 2026. "Ethereum risk states as a tail-risk switch for Art NFTs:Evidence from SuperRare," Finance Research Letters, Elsevier, vol. 101(C).
    • Handle: RePEc:eee:finlet:v:101:y:2026:i:c:s1544612326005982
    DOI: 10.1016/j.frl.2026.110069
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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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