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

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...

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Veröffentlicht in:International review of financial analysis 2023-05, Vol.87, p.102642, Article 102642
Hauptverfasser: Chowdhury, Mohammad Ashraful Ferdous, Abdullah, Mohammad, Alam, Masud, Abedin, Mohammad Zoynul, Shi, Baofeng
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Sprache:eng
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Zusammenfassung: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. •Before COVID-19 traditional asset (S&P 500) is the most efficient asset class.•During COVID-19 DeFi ranked first, and S&P 500 ranked the second in efficiency.•In full sample DeFi is the most efficient asset class and Gold is the second.•Overall, NFTs, DeFi, and traditional assets more efficient than cryptocurrencies.
ISSN:1057-5219
1873-8079
DOI:10.1016/j.irfa.2023.102642