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NFTs and asset class spillovers: Lessons from the period around the COVID-19 pandemic
Open accessAbstract
In this paper, we analyze the connectedness between returns for non-fungible tokens (NFTs) and other financial assets (equities, bonds, currencies, gold, oil, Ethereum) during the period from January 2018 to June 2021. By using the Time-Varying Parameter Vector Autoregressions (TVP-VAR) approach, we show that the overall connectedness between the returns for financial assets increased during the COVID-19 period. Our static analysis shows that the behavior of the majority of NFT returns is attributable to endogenous shocks and only a small portion of this variation resulted from the impact of innovation in other assets. The results suggest that NFTs are mainly independent of shocks from common assets classes and even from their close relation, Ethereum. The dynamic analysis across time reveals that during normal times, NFTs act as transmitters of systemic risk to some degree, but during stressful times, their role shifts, and they act as absorbers of risk spillovers. This suggests that NFTs may have diversification benefits during turbulent times, as apparent during the COVID-19 crisis, and especially around the great March 2020 market plunge.
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“This distinction was clearly highlighted by [40], and the relevance of endogenous shocks for NFTs' price dynamics (namely their return) was strongly advocated for by [41].”
From What Is the Right Price for Non-Fungible Tokens (NFTs)? A Systematic Review of the Current Literature (2025) · cites this paper as [41]
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“Both the static and the dynamic analysis reveal that during the pandemic the overall interdependence increases.”
From What Is the Right Price for Non-Fungible Tokens (NFTs)? A Systematic Review of the Current Literature (2025) · cites this paper as [41]
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“Nevertheless, the majority of NFTs' return fluctuations can be attributed to endogenous shocks; only a small percentage is due to the trends of other assets.”
From What Is the Right Price for Non-Fungible Tokens (NFTs)? A Systematic Review of the Current Literature (2025) · cites this paper as [41]
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“Ethereum and NFTs are shown to exhibit the highest correlation and volatility in the pandemic period.”
From What Is the Right Price for Non-Fungible Tokens (NFTs)? A Systematic Review of the Current Literature (2025) · cites this paper as [41]
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“However, NFTs do not appear to be correlated with other common assets.”
From What Is the Right Price for Non-Fungible Tokens (NFTs)? A Systematic Review of the Current Literature (2025) · cites this paper as [41]
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Metadata
- Authors
- Aharon, D. Y., & Demir, E.
- Affiliations
- Ono Academic College (IL)
- Reykjavík University (IS)
- Corresponding author
- Demir, Ender
- Venue
- Finance Research Letters
- Publisher
- Elsevier BV
- Published
- 2022-06
- Volume
- 47
- Pages
- 102515
- ISSN
- 1544-6123 (ISSN-L)
- PMC
- PMC9643051
- OpenAlex
- W3208066366
- Cited by
- 274
- References
- 23
- Citations by year
- 2026: 22 · 2025: 54 · 2024: 57 · 2023: 86 · 2022: 46
- FWCI
- 37.79
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Finance research letters (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Market Dynamics and Volatility
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Risk and Volatility Modeling
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › COVID-19 Pandemic Impacts
- Keywords
- non-fungible tokens, return connectedness, COVID-19 pandemic, Ethereum, systemic risk, diversification benefits, time-varying connectedness
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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