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

Mohammad Ashraful Ferdous Chowdhury, Mohammad Abdullah, Masud Alam, Mohammad Abedin Orcid Logo, Baofeng Shi

International Review of Financial Analysis, Volume: 87, Start page: 102642

Swansea University Author: Mohammad Abedin Orcid Logo

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DOI (Published version): 10.1016/j.irfa.2023.102642

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...

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Published in: International Review of Financial Analysis
ISSN: 1057-5219 1873-8079
Published: Elsevier BV 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa64221
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last_indexed 2024-11-25T14:13:39Z
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spelling 2023-09-20T16:18:43.3277352 v2 64221 2023-08-31 NFTs, DeFi, and other assets efficiency and volatility dynamics: An asymmetric multifractality analysis 4ed8c020eae0c9bec4f5d9495d86d415 0000-0002-4688-0619 Mohammad Abedin Mohammad Abedin true false 2023-08-31 CBAE 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. Journal Article International Review of Financial Analysis 87 102642 Elsevier BV 1057-5219 1873-8079 NFTs, DeFi, Non-fungible tokens, Asymmetric multifractal analysis, MF-DFA, A-MFDFA, Cryptocurrencies 31 5 2023 2023-05-31 10.1016/j.irfa.2023.102642 http://dx.doi.org/10.1016/j.irfa.2023.102642 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University 2023-09-20T16:18:43.3277352 2023-08-31T17:20:30.3625574 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Mohammad Ashraful Ferdous Chowdhury 1 Mohammad Abdullah 2 Masud Alam 3 Mohammad Abedin 0000-0002-4688-0619 4 Baofeng Shi 5 64221__28599__b82fd6dc43ba495da76c598e3a149184.pdf 64221.VOR.pdf 2023-09-20T16:17:44.4013489 Output 18352792 application/pdf Version of Record true © 2023 The Authors. Published by Elsevier Inc. Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0). true eng https://creativecommons.org/licenses/by/4.0/
title NFTs, DeFi, and other assets efficiency and volatility dynamics: An asymmetric multifractality analysis
spellingShingle NFTs, DeFi, and other assets efficiency and volatility dynamics: An asymmetric multifractality analysis
Mohammad Abedin
title_short NFTs, DeFi, and other assets efficiency and volatility dynamics: An asymmetric multifractality analysis
title_full NFTs, DeFi, and other assets efficiency and volatility dynamics: An asymmetric multifractality analysis
title_fullStr NFTs, DeFi, and other assets efficiency and volatility dynamics: An asymmetric multifractality analysis
title_full_unstemmed NFTs, DeFi, and other assets efficiency and volatility dynamics: An asymmetric multifractality analysis
title_sort NFTs, DeFi, and other assets efficiency and volatility dynamics: An asymmetric multifractality analysis
author_id_str_mv 4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv 4ed8c020eae0c9bec4f5d9495d86d415_***_Mohammad Abedin
author Mohammad Abedin
author2 Mohammad Ashraful Ferdous Chowdhury
Mohammad Abdullah
Masud Alam
Mohammad Abedin
Baofeng Shi
format Journal article
container_title International Review of Financial Analysis
container_volume 87
container_start_page 102642
publishDate 2023
institution Swansea University
issn 1057-5219
1873-8079
doi_str_mv 10.1016/j.irfa.2023.102642
publisher Elsevier BV
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
url http://dx.doi.org/10.1016/j.irfa.2023.102642
document_store_str 1
active_str 0
description 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.
published_date 2023-05-31T20:24:20Z
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score 11.04748

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