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Return and volatility properties: Stylized facts from the universe of cryptocurrencies and NFTs

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  • Ghosh, Bikramaditya
  • Bouri, Elie
  • Wee, Jung Bum
  • Zulfiqar, Noshaba

Abstract

Stylized facts of returns and volatility are an important approximation tool for empirical finance studies, especially in the area of young and new assets. In this paper, we examine the return and volatility properties of four non-fungible tokens (NFTs) and four cryptocurrencies from 24th January 2018–2nd August 2022. The results show the following: Firstly, the returns of both NFTs and cryptocurrencies have fat tails, with evidence of tail exponents following the inverse cubic-law, along with clear persistence behavior. Secondly, all returns exhibit volatility clustering, albeit to varying degrees, and the detected absence of inverse volatility-asymmetry challenges the safe-haven property often documented for cryptocurrencies. Thirdly, return-based long-memory is slightly more intense than volatility-based long-memory, especially for NFTs, which demonstrate a predictability contesting market efficiency. These findings are generally consistent with previous findings on equities, implying that the return and volatility behavior of NFTs and cryptocurrencies is leaning towards that of traditional assets.

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  • Ghosh, Bikramaditya & Bouri, Elie & Wee, Jung Bum & Zulfiqar, Noshaba, 2023. "Return and volatility properties: Stylized facts from the universe of cryptocurrencies and NFTs," Research in International Business and Finance, Elsevier, vol. 65(C).
  • Handle: RePEc:eee:riibaf:v:65:y:2023:i:c:s0275531923000715
    DOI: 10.1016/j.ribaf.2023.101945
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    3. Díaz, Antonio & Esparcia, Carlos & Huélamo, Diego, 2023. "Unveiling the diversification capabilities of carbon markets in NFT portfolios," Finance Research Letters, Elsevier, vol. 58(PD).
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    5. 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).
    6. Zhang, Lei & Bouri, Elie & Chen, Yan, 2023. "Co-jump dynamicity in the cryptocurrency market: A network modelling perspective," Finance Research Letters, Elsevier, vol. 58(PB).
    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. Yousaf, Imran & Assaf, Ata & Demir, Ender, 2024. "Relationship between real estate tokens and other asset classes: Evidence from quantile connectedness approach," Research in International Business and Finance, Elsevier, vol. 69(C).
    9. Aslam, Faheem & Memon, Bilal Ahmed & Hunjra, Ahmed Imran & Bouri, Elie, 2023. "The dynamics of market efficiency of major cryptocurrencies," Global Finance Journal, Elsevier, vol. 58(C).

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    More about this item

    Keywords

    Non-fungible token (NFT); Bitcoin and cryptocurrencies; Fat tails; GARCH-models; Asymmetric volatility; Long memory;
    All these keywords.

    JEL classification:

    • B16 - Schools of Economic Thought and Methodology - - History of Economic Thought through 1925 - - - Quantitative and Mathematical
    • B23 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Econometrics; Quantitative and Mathematical Studies
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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