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

Chong Guan (), Wenting Liu (), Yinghui Yu () and Ding Ding ()
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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

Financial Innovation, 2024, vol. 10, issue 1, 1-19

Abstract: 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.

Keywords: Tokenomics; Lead–lag; Metaverse tokens; Trade volume; Daily returns (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1186/s40854-023-00594-z

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