IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v44y2022ics154461232100177x.html
   My bibliography  Save this article

Fertile LAND: Pricing non-fungible tokens

Author

Listed:
  • Dowling, Michael

Abstract

The current popularity of non-fungible token (NFT) markets is one of the most notable public successes of blockchain technology. NFTs are blockchain-traded rights to any digital asset; including images, videos, music, even the parts of virtual worlds. As a first study of NFT pricing, we explore the pricing of parcels of virtual real estate in the largest blockchain virtual world, Decentraland; an NFT simply termed LAND. We show a LAND price series characterised by both inefficiency and a steady rise in value.

Suggested Citation

  • Dowling, Michael, 2022. "Fertile LAND: Pricing non-fungible tokens," Finance Research Letters, Elsevier, vol. 44(C).
    • Handle: RePEc:eee:finlet:v:44:y:2022:i:c:s154461232100177x
    DOI: 10.1016/j.frl.2021.102096
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S154461232100177X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2021.102096?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. Weron, Rafał, 2002. "Estimating long-range dependence: finite sample properties and confidence intervals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 312(1), pages 285-299.
    2. Klaus Grobys, 2021. "When the blockchain does not block: on hackings and uncertainty in the cryptocurrency market," Quantitative Finance, Taylor & Francis Journals, vol. 21(8), pages 1267-1279, August.
    3. Urquhart, Andrew, 2016. "The inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 148(C), pages 80-82.
    4. Escanciano, J. Carlos & Lobato, Ignacio N., 2009. "An automatic Portmanteau test for serial correlation," Journal of Econometrics, Elsevier, vol. 151(2), pages 140-149, August.
    5. Kim, Jae H., 2009. "Automatic variance ratio test under conditional heteroskedasticity," Finance Research Letters, Elsevier, vol. 6(3), pages 179-185, September.
    6. Khuntia, Sashikanta & Pattanayak, J.K., 2018. "Adaptive market hypothesis and evolving predictability of bitcoin," Economics Letters, Elsevier, vol. 167(C), pages 26-28.
    7. Baur, Dirk G. & Hong, KiHoon & Lee, Adrian D., 2018. "Bitcoin: Medium of exchange or speculative assets?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 54(C), pages 177-189.
    8. Manuel Dominguez & Ignacio Lobato, 2003. "Testing the Martingale Difference Hypothesis," Econometric Reviews, Taylor & Francis Journals, vol. 22(4), pages 351-377.
    9. Cheah, Eng-Tuck & Fry, John, 2015. "Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin," Economics Letters, Elsevier, vol. 130(C), pages 32-36.
    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. Flori, Andrea, 2019. "News and subjective beliefs: A Bayesian approach to Bitcoin investments," Research in International Business and Finance, Elsevier, vol. 50(C), pages 336-356.
    2. 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.
    3. Andrea Flori, 2019. "Cryptocurrencies In Finance: Review And Applications," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(05), pages 1-22, August.
    4. Stefano Martinazzi & Daniele Regoli & Andrea Flori, 2020. "A Tale of Two Layers: The Mutual Relationship between Bitcoin and Lightning Network," Risks, MDPI, vol. 8(4), pages 1-18, December.
    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. Nikolaos A. Kyriazis, 2019. "A Survey on Efficiency and Profitable Trading Opportunities in Cryptocurrency Markets," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    7. Yuanyuan Zhang & Stephen Chan & Jeffrey Chu & Hana Sulieman, 2020. "On the Market Efficiency and Liquidity of High-Frequency Cryptocurrencies in a Bull and Bear Market," JRFM, MDPI, vol. 13(1), pages 1-14, January.
    8. Abakah, Emmanuel Joel Aikins & Gil-Alana, Luis Alberiko & Madigu, Godfrey & Romero-Rojo, Fatima, 2020. "Volatility persistence in cryptocurrency markets under structural breaks," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 680-691.
    9. Platanakis, Emmanouil & Sutcliffe, Charles & Urquhart, Andrew, 2018. "Optimal vs naïve diversification in cryptocurrencies," Economics Letters, Elsevier, vol. 171(C), pages 93-96.
    10. Corbet, Shaen & Lucey, Brian & Urquhart, Andrew & Yarovaya, Larisa, 2019. "Cryptocurrencies as a financial asset: A systematic analysis," International Review of Financial Analysis, Elsevier, vol. 62(C), pages 182-199.
    11. Panagiotidis, Theodore & Stengos, Thanasis & Vravosinos, Orestis, 2019. "The effects of markets, uncertainty and search intensity on bitcoin returns," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 220-242.
    12. Zhang, Wei & Wang, Pengfei & Li, Xiao & Shen, Dehua, 2018. "The inefficiency of cryptocurrency and its cross-correlation with Dow Jones Industrial Average," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 658-670.
    13. Vidal-Tomás, David, 2022. "Which cryptocurrency data sources should scholars use?," International Review of Financial Analysis, Elsevier, vol. 81(C).
    14. Akbar, Muhammad & Ullah, Ihsan & Ali, Shahid & Rehman, Naser, 2024. "Adaptive market hypothesis: A comparison of Islamic and conventional stock indices," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 460-477.
    15. Chu, Jeffrey & Zhang, Yuanyuan & Chan, Stephen, 2019. "The adaptive market hypothesis in the high frequency cryptocurrency market," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 221-231.
    16. Sinda Hadhri, 2021. "Fear of the Coronavirus and Cryptocurrencies' returns," Economics Bulletin, AccessEcon, vol. 41(3), pages 2041-2054.
    17. Gronwald, Marc, 2019. "Is Bitcoin a Commodity? On price jumps, demand shocks, and certainty of supply," Journal of International Money and Finance, Elsevier, vol. 97(C), pages 86-92.
    18. Scharnowski, Matthias & Scharnowski, Stefan & Zimmermann, Lukas, 2023. "Fan tokens: Sports and speculation on the blockchain," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 89(C).
    19. Ashok Chanabasangouda Patil & Shailesh Rastogi, 2019. "Time-Varying Price–Volume Relationship and Adaptive Market Efficiency: A Survey of the Empirical Literature," JRFM, MDPI, vol. 12(2), pages 1-18, June.
    20. Köchling, Gerrit & Müller, Janis & Posch, Peter N., 2019. "Does the introduction of futures improve the efficiency of Bitcoin?," Finance Research Letters, Elsevier, vol. 30(C), pages 367-370.

    More about this item

    Keywords

    NFT; Non-fungible tokens market efficiency;

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    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:eee:finlet:v:44:y:2022:i:c:s154461232100177x. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    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.