IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v63y2024i3d10.1007_s10614-023-10522-z.html
   My bibliography  Save this article

Dynamic Efficiency and Herd Behavior During Pre- and Post-COVID-19 in the NFT Market: Evidence from Multifractal Analysis

Author

Listed:
  • Onur Özdemir

    (Istanbul Gelisim University)

  • Anoop S. Kumar

    (Gulati Institute of Finance and Taxation, Amikkattu)

Abstract

This study investigates how the lockdowns during the COVID-19 outbreak affect the multifractal features of four Non-Fungible Tokens (NFTs) (i.e., Cryptokitties, Cryptopunks, SuperRare, and Decentraland) using daily price data ranging from 23 June 2017 to 15 February 2022. The major concern is to assess whether the presence of herd investing and the level of market inefficiency altered for the period between pre-first-lockdown (i.e., 23 June 2017–22 March 2020) and post-first-lockdown (i.e., 23 March 2020–15 February 2022). The generalized Hurst exponents are measured towards a multifractal detrended fluctuation approach. In particular, the empirical results document that multifractality exists for each NFT during the COVID-19 outbreak. Besides, the level of market inefficiency differs among the selected NFTs. The results refer to the case that the post-first-lockdown period is more prone to herd investing for Cryptokitties, Cryptopunks, and Decentraland. Furthermore, testing for MLM (inefficiency) index, the empirical findings show that Cryptokitties became more vulnerable in the post-first-lockdown period. Regarding the impacts of this far-reaching outbreak, the highest MLM (inefficiency) index value is attributed to Cryptopunks before the first lockdown and Cryptokitties after the first lockdown periods.

Suggested Citation

  • Onur Özdemir & Anoop S. Kumar, 2024. "Dynamic Efficiency and Herd Behavior During Pre- and Post-COVID-19 in the NFT Market: Evidence from Multifractal Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 63(3), pages 1255-1279, March.
    • Handle: RePEc:kap:compec:v:63:y:2024:i:3:d:10.1007_s10614-023-10522-z
    DOI: 10.1007/s10614-023-10522-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-023-10522-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-023-10522-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," The Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    2. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    3. Okorie, David Iheke & Lin, Boqiang, 2021. "Adaptive market hypothesis: The story of the stock markets and COVID-19 pandemic," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    4. Cheah, Eng-Tuck & Mishra, Tapas & Parhi, Mamata & Zhang, Zhuang, 2018. "Long Memory Interdependency and Inefficiency in Bitcoin Markets," Economics Letters, Elsevier, vol. 167(C), pages 18-25.
    5. Dowling, Michael, 2022. "Fertile LAND: Pricing non-fungible tokens," Finance Research Letters, Elsevier, vol. 44(C).
    6. Bassiouny, Aliaa & Kiryakos, Mariam & Tooma, Eskandar, 2023. "Examining the adaptive market hypothesis with calendar effects: International evidence and the impact of COVID-19," Global Finance Journal, Elsevier, vol. 56(C).
    7. Khuntia, Sashikanta & Pattanayak, J.K., 2020. "Adaptive long memory in volatility of intra-day bitcoin returns and the impact of trading volume," Finance Research Letters, Elsevier, vol. 32(C).
    8. Emna Mnif & Anis Jarboui, 2021. "COVID-19, bitcoin market efficiency, herd behaviour," Review of Behavioral Finance, Emerald Group Publishing Limited, vol. 13(1), pages 69-84, March.
    9. Matteo, T. Di & Aste, T. & Dacorogna, Michel M., 2005. "Long-term memories of developed and emerging markets: Using the scaling analysis to characterize their stage of development," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 827-851, April.
    10. Kukacka, Jiri & Kristoufek, Ladislav, 2020. "Do ‘complex’ financial models really lead to complex dynamics? Agent-based models and multifractality," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    11. Petre Caraiani, 2012. "Evidence of Multifractality from Emerging European Stock Markets," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-9, July.
    12. Dowling, Michael, 2022. "Is non-fungible token pricing driven by cryptocurrencies?," Finance Research Letters, Elsevier, vol. 44(C).
    13. Anselmi, Giulio & Petrella, Giovanni, 2023. "Non-fungible token artworks: More crypto than art?," Finance Research Letters, Elsevier, vol. 51(C).
    14. Ko, Hyungjin & Son, Bumho & Lee, Yunyoung & Jang, Huisu & Lee, Jaewook, 2022. "The economic value of NFT: Evidence from a portfolio analysis using mean–variance framework," Finance Research Letters, Elsevier, vol. 47(PA).
    15. Éder Pereira & Paulo Ferreira & Derick Quintino, 2022. "Non-Fungible Tokens (NFTs) and Cryptocurrencies: Efficiency and Comovements," FinTech, MDPI, vol. 1(4), pages 1-8, October.
    16. Assaf, Ata & Bhandari, Avishek & Charif, Husni & Demir, Ender, 2022. "Multivariate long memory structure in the cryptocurrency market: The impact of COVID-19," International Review of Financial Analysis, Elsevier, vol. 82(C).
    17. Khuntia, Sashikanta & Pattanayak, J.K., 2018. "Adaptive market hypothesis and evolving predictability of bitcoin," Economics Letters, Elsevier, vol. 167(C), pages 26-28.
    18. Faheem Aslam & Wahbeeah Mohti & Paulo Ferreira, 2020. "Evidence of Intraday Multifractality in European Stock Markets during the Recent Coronavirus (COVID-19) Outbreak," IJFS, MDPI, vol. 8(2), pages 1-13, May.
    19. Laura Raisa Miloş & Cornel Haţiegan & Marius Cristian Miloş & Flavia Mirela Barna & Claudiu Boțoc, 2020. "Multifractal Detrended Fluctuation Analysis (MF-DFA) of Stock Market Indexes. Empirical Evidence from Seven Central and Eastern European Markets," Sustainability, MDPI, vol. 12(2), pages 1-15, January.
    20. Laib, Mohamed & Golay, Jean & Telesca, Luciano & Kanevski, Mikhail, 2018. "Multifractal analysis of the time series of daily means of wind speed in complex regions," Chaos, Solitons & Fractals, Elsevier, vol. 109(C), pages 118-127.
    21. Karim, Sitara & Lucey, Brian M. & Naeem, Muhammad Abubakr & Uddin, Gazi Salah, 2022. "Examining the interrelatedness of NFTs, DeFi tokens and cryptocurrencies," Finance Research Letters, Elsevier, vol. 47(PB).
    22. Muhammad Naeem Shahid, 2022. "COVID-19 and adaptive behavior of returns: evidence from commodity markets," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-15, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Assaf, Ata & Mokni, Khaled & Yousaf, Imran & Bhandari, Avishek, 2023. "Long memory in the high frequency cryptocurrency markets using fractal connectivity analysis: The impact of COVID-19," Research in International Business and Finance, Elsevier, vol. 64(C).
    2. Assaf, Ata & Bhandari, Avishek & Charif, Husni & Demir, Ender, 2022. "Multivariate long memory structure in the cryptocurrency market: The impact of COVID-19," International Review of Financial Analysis, Elsevier, vol. 82(C).
    3. Shimeng Shi & Jia Zhai & Yingying Wu, 2024. "Informational inefficiency on bitcoin futures," The European Journal of Finance, Taylor & Francis Journals, vol. 30(6), pages 642-667, April.
    4. Wang, Jying-Nan & Lee, Yen-Hsien & Liu, Hung-Chun & Hsu, Yuan-Teng, 2023. "Dissecting returns of non-fungible tokens (NFTs): Evidence from CryptoPunks," The North American Journal of Economics and Finance, Elsevier, vol. 65(C).
    5. Aurelio F. Bariviera & Ignasi Merediz‐Solà, 2021. "Where Do We Stand In Cryptocurrencies Economic Research? A Survey Based On Hybrid Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 377-407, April.
    6. Aslam, Faheem & Aziz, Saqib & Nguyen, Duc Khuong & Mughal, Khurrum S. & Khan, Maaz, 2020. "On the efficiency of foreign exchange markets in times of the COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    7. Proelss, Juliane & Sévigny, Stéphane & Schweizer, Denis, 2023. "GameFi: The perfect symbiosis of blockchain, tokens, DeFi, and NFTs?," International Review of Financial Analysis, Elsevier, vol. 90(C).
    8. Zhang, Wenting & Liu, Tiantian & Zhang, Yulian & Hamori, Shigeyuki, 2024. "Can NFTs hedge the risk of traditional assets after the COVID-19 pandemic?," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
    9. Urom, Christian & Ndubuisi, Gideon & Guesmi, Khaled, 2022. "Dynamic dependence and predictability between volume and return of Non-Fungible Tokens (NFTs): The roles of market factors and geopolitical risks," Finance Research Letters, Elsevier, vol. 50(C).
    10. Naseem Al Rahahleh & Ahmed Al Qurashi, 2024. "The impact of COVID-19 on Ethereum returns and Ethereum market efficiency," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 14(3), pages 729-755, September.
    11. Chowdhury, Mohammad Ashraful Ferdous & Abdullah, Mohammad & Alam, Masud & Abedin, Mohammad Zoynul & Shi, Baofeng, 2023. "NFTs, DeFi, and other assets efficiency and volatility dynamics: An asymmetric multifractality analysis," International Review of Financial Analysis, Elsevier, vol. 87(C).
    12. Nobanee, Haitham & Ellili, Nejla Ould Daoud, 2023. "Non-fungible tokens (NFTs): A bibliometric and systematic review, current streams, developments, and directions for future research," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 460-473.
    13. Nakavachara, Voraprapa & Saengchote, Kanis, 2022. "Does unit of account affect willingness to pay? Evidence from metaverse LAND transactions✰," Finance Research Letters, Elsevier, vol. 49(C).
    14. Kumar, Anoop S & Padakandla, Steven Raj, 2023. "Do NFTs act as a good hedge and safe haven against Cryptocurrency fluctuations?," Finance Research Letters, Elsevier, vol. 56(C).
    15. Xia, Yufei & Li, Jinglong & Fu, Yating, 2022. "Are non-fungible tokens (NFTs) different asset classes? Evidence from quantile connectedness approach," Finance Research Letters, Elsevier, vol. 49(C).
    16. Urom, C. & Ndubuisi, Gideon & Guesmi, K., 2022. "Quantile return and volatility connectedness among Non-Fungible Tokens (NFTs) and (un)conventional asset," MERIT Working Papers 2022-017, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    17. Theodore Panagiotidis & Georgios Papapanagiotou, 2024. "A note on the determinants of NFTs returns," Working Paper series 24-07, Rimini Centre for Economic Analysis.
    18. Dunbar, Kwamie & Owusu-Amoako, Johnson, 2022. "Cryptocurrency returns under empirical asset pricing," International Review of Financial Analysis, Elsevier, vol. 82(C).
    19. Yencha, Christopher, 2023. "Spatial heterogeneity and non-fungible token sales: Evidence from Decentraland LAND sales," Finance Research Letters, Elsevier, vol. 58(PA).
    20. Aslam, Faheem & Zil-e-huma, & Bibi, Rashida & Ferreira, Paulo, 2022. "Cross-correlations between economic policy uncertainty and precious and industrial metals: A multifractal cross-correlation analysis," Resources Policy, Elsevier, vol. 75(C).

    More about this item

    Keywords

    Non-fungible tokens; Multifractal detrended fluctuation analysis; MLM (inefficiency) index; Herding behavior; COVID-19 pandemic;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • G1 - Financial Economics - - General Financial Markets
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:compec:v:63:y:2024:i:3:d:10.1007_s10614-023-10522-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.