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

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 20...

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Bibliographic Details
Published in:Research in international business and finance 2023-04, Vol.65, p.101945, Article 101945
Main Authors: Ghosh, Bikramaditya, Bouri, Elie, Wee, Jung Bum, Zulfiqar, Noshaba
Format: Article
Language:English
Subjects:
Asymmetric volatility
Bitcoin and cryptocurrencies
Fat tails
GARCH-models
Long memory
Non-fungible token (NFT)
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Summary: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. [Display omitted] •Study returns and volatility of four major NFTs and four major cryptocurrencies.•Show that NFTs and cryptocurrencies have fat tails along with clear persistence behavior.•Detect absence of inverse volatility asymmetry, which challenges the safe-haven property.•Return-based long memory is slightly more intense than volatility-based long memory, especially for NFTs.•Overall results suggest a predictability contesting market efficiency.
ISSN:0275-5319
DOI:10.1016/j.ribaf.2023.101945