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Tokenomics in the Metaverse: understanding the lead–lag effect among emerging crypto tokens

Author

Listed:
  • Chong Guan

    (Singapore University of Social Sciences)

  • Wenting Liu

    (Singapore University of Social Sciences)

  • Yinghui Yu

    (Singapore University of Social Sciences)

  • Ding Ding

    (Singapore University of Social Sciences)

Abstract

The convergence of blockchain and immersive technologies has resulted in the popularity of Metaverse platforms and their cryptocurrencies, known as Metaverse tokens. There has been little research into tokenomics in these emerging tokens. Building upon the information dissemination theory, this research examines the role of trading volume in the returns of these tokens. An empirical study was conducted using the trading volumes and returns of 197 Metaverse tokens over 12 months to derive the latent grouping structure with spectral clustering and to determine the relationships between daily returns of different token clusters through augmented vector autoregression. The results show that trading volume is a strong predictor of lead–lag patterns, which supports the speed of adjustment hypothesis. This is the first large-scale study that documented the lead–lag effect among Metaverse tokens. Unlike previous studies that focus on market capitalization, our findings suggest that trade volume contains vital information concerning cross-correlation patterns.

Suggested Citation

  • Chong Guan & Wenting Liu & Yinghui Yu & Ding Ding, 2024. "Tokenomics in the Metaverse: understanding the lead–lag effect among emerging crypto tokens," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-19, December.
    • Handle: RePEc:spr:fininn:v:10:y:2024:i:1:d:10.1186_s40854-023-00594-z
    DOI: 10.1186/s40854-023-00594-z
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