ABSTRACT
This study conducted the econometric analysis to test the hedge and safe haven effects of Non-fungible Tokens (NFTs) on major traditional asset markets in the global financial system. We investigate the estimates of these effects in times of extreme market conditions and the COVID-19 crisis. Our empirical results show evidence of the hedge and safe haven properties of NFTs, confirming two main findings: (i) NFTs act as a hedge and safe haven for particular stock markets and oil, bond, and USD indices, even though the degree of effects varies across asset classes; and (ii) NFTs also serve as sheltering facilities for the markets mentioned above, with more substantial safe haven benefits for bond and USD indices during the recent pandemic crisis.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
2 The Baur and McDermott (Citation2010) model is known to be one of the most popular, reliable, and standard model to test hedge and safe haven characteristics and is widely used for the various asset markets (Agyei-Ampomah, Gounopoulos, and Mazouz Citation2014; Chen and Wang Citation2019; Karim et al. Citation2022; Naeem et al. Citation2021). We employ this model because it can inherently examine the effect of volatile market circumstances not only from a statistical viewpoint (e.g. percentile of return) but also from an economic viewpoint (e.g. COVID-19 crisis).
4 Specifically, the US, Canada, Australia, Japan, the UK, Germany, Switzerland, Italy, Finland, Netherlands, Austria, Belgium, Spain, China, Russia, India, and South Korea are represented by S&P 500, S&P/TSX, S&P/ASX 200, Nikkei 225, FTSE 100, DAX 30, SMI, IT 40, OMXH 25, AEX, ATX, BE 20, IBEX, CSI 300, MOEX, INDA, and KOSPI 200, respectively.
5 We include cryptocurrency into our asset classes under study due to its relevance. That is, NFTs are secondary assets derived from cryptocurrency. Bitcoin is the largest cryptocurrency in terms of market capitalization. Ethereum is the representative NFT platform.
7 The reason why we use average NFT price is that it can mitigate the issue of extreme fluctuation of NFT returns due to a large number of market trade observations (Dowling Citation2021b).
8 We use a period covering 20 trading days following Baur and Lucey (Citation2010). The authors assume most of the crisis effects occur in the first 20 trading days (one month) after the start date.
9 The economic approach is more arbitrary in selecting the specific periods of crisis compared to the statistical approach, which uses the quantile of the return for a dummy variable threshold. Therefore, to show the reliability of our original results, we also conducted an analysis involving more extended periods by incrementally advancing the start date until January 12, 2020, following Aharon and Demir (Citation2021). The results are essentially similar to the original results. The full results are available upon request.
10 The authors in Zhang, Sun, and Ma (Citation2022) used the NARDL model as introduced by Shin, Yu, and Greenwood-Nimmo (Citation2014) in their study, while our study employs a different methodology proposed by Baur and Lucey (Citation2010). Furthermore, our focus is on a specific time frame, specifically extreme market or COVID-19 periods, to assess the impact of specific events, whereas Zhang, Sun, and Ma (Citation2022) conducted their investigation across both pre-COVID-19 and COVID-19 periods to observe overall trends.
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Funding
The work was supported by the National Research Foundation of Korea [2019R1A2C2002358,2022R1A5A6000840]
https://orcid.org/0000-0001-6564-733X
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