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

Mohammad Ashraful Ferdous Chowdhury; Mohammad Abdullah; Masud Alam; Mohammad Zoynul Abedin; Baofeng Shi

doi:10.1016/j.irfa.2023.102642

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

Mohammad Ashraful Ferdous Chowdhury
, Mohammad Abdullah
, Masud Alam
, Mohammad Zoynul Abedin^*
, Baofeng Shi

^*Corresponding author for this work

Interdisciplinary Research Center for Finance and Digital Economy

Research output: Contribution to journal › Article › peer-review

58 Scopus citations

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.

Original language	English
Article number	102642
Journal	International Review of Financial Analysis
Volume	87
DOIs	https://doi.org/10.1016/j.irfa.2023.102642
State	Published - May 2023

Bibliographical note

Publisher Copyright:
© 2023 The Authors

Keywords

A-MFDFA
Asymmetric multifractal analysis
Cryptocurrencies
DeFi
MF-DFA
NFTs
Non-fungible tokens

ASJC Scopus subject areas

Finance
Economics and Econometrics

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10.1016/j.irfa.2023.102642

Cite this

@article{68d855331f7e4684a526ffcf1be07a0c,

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

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 = "A-MFDFA, Asymmetric multifractal analysis, Cryptocurrencies, DeFi, MF-DFA, NFTs, Non-fungible tokens",

author = "Chowdhury, \{Mohammad Ashraful Ferdous\} and Mohammad Abdullah and Masud Alam and Abedin, \{Mohammad Zoynul\} and Baofeng Shi",

note = "Publisher Copyright: {\textcopyright} 2023 The Authors",

year = "2023",

month = may,

doi = "10.1016/j.irfa.2023.102642",

language = "English",

volume = "87",

journal = "International Review of Financial Analysis",

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T2 - An asymmetric multifractality analysis

AU - Chowdhury, Mohammad Ashraful Ferdous

AU - Abdullah, Mohammad

AU - Alam, Masud

AU - Abedin, Mohammad Zoynul

AU - Shi, Baofeng

PY - 2023/5

Y1 - 2023/5

N2 - 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.

AB - 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.

KW - A-MFDFA

KW - Asymmetric multifractal analysis

KW - Cryptocurrencies

KW - DeFi

KW - MF-DFA

KW - NFTs

KW - Non-fungible tokens

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VL - 87

JO - International Review of Financial Analysis

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M1 - 102642

ER -

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

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this