Decoding multifractality in DeFi through governance tokens

Meghna Jayasankar () and Anoop Kumar ()
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Meghna Jayasankar: Gulati Institute of Finance and Taxation (Affiliated to Cochin University of Science and Technology)
Anoop Kumar: Gulati Institute of Finance and Taxation (Affiliated to Cochin University of Science and Technology)

Economics Bulletin, 2026, vol. 46, issue 1, 93 - 110

Abstract: This study investigates the multifractal characteristics of the decentralised finance (DeFi) market through the lens of governance tokens. We classify governance tokens into two categories: blockchain governance tokens and governance tokens for decentralised autonomous organisations (DAOs). Using daily returns of seven tokens Uniswap, Compound, Maker, Curve DAO, Aave, Dash, and Decred spanning May 2020 to June 2024, we apply Multifractal Detrended Fluctuation Analysis (MFDFA) to examine scaling behaviour and long-memory properties. Our findings confirm multifractality across all tokens, with blockchain governance tokens exhibiting greater multifractality and thus higher market inefficiency than DeFi tokens. We further introduce the Generalised Magnitude of Long Memory (GMLM), a novel measure of relative efficiency across return moments, which shows that DeFi tokens are more efficient at higher moments, while blockchain tokens are more efficient at lower moments. This suggests that risk assessment strategies in DeFi should account for moment-specific efficiency, with higher-order risk models being more appropriate for DeFi tokens and lower-order models being better suited for blockchain governance tokens. These findings offer actionable insights for investors, portfolio managers, and policymakers navigating the DeFi landscape.

Keywords: DeFi; governance tokens; blockchain; MF-DFA; cryptocurrencies (search for similar items in EconPapers)
JEL-codes: G1 G3 (search for similar items in EconPapers)
Date: 2026-03-30
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