NFTs, DeFi, and other assets efficiency and volatility dynamics: An asymmetric multifractality analysis

Mohammad Ashraful Chowdhury, Mohammad Abdullah, Masud Alam, Mohammad Zoynul Abedin and Baofeng Shi

International Review of Financial Analysis, 2023, vol. 87, issue C

Abstract: This paper examines the efficiency and asymmetric multifractal features of NFTs, DeFi, cryptocurrencies, and traditional assets using Asymmetric Multifractal Cross-Correlations Analysis covering the period from November 2017 to February 2022. Considering the full sample with a significant variation among asset classes, the study reveals DeFi-DigiByte is the most efficient while the cryptocurrency-Tether is the least efficient. However, S&P 500 showed high efficiency before COVID-19, and DeFi-Enjin Coin advanced as the most efficient asset during COVID-19. The volatility dynamics of NFTs, DeFi, and cryptocurrencies follow strong nonlinear cross-correlations, but evidence of weaker nonlinearity exists in traditional assets. Additionally, the sensitivity to smaller events in bull markets is high for NFTs and DeFi. The findings have significant implications for portfolio diversification when an investor's portfolio set includes traditional assets and cryptocurrency and relatively new blockchain-based assets like NFTs and DeFi.

Keywords: NFTs; DeFi; Non-fungible tokens; Asymmetric multifractal analysis; MF-DFA; A-MFDFA; Cryptocurrencies (search for similar items in EconPapers)
JEL-codes: C58 F36 G12 G14 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (27)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:87:y:2023:i:c:s1057521923001588

DOI: 10.1016/j.irfa.2023.102642

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