2020
DOI: 10.2139/ssrn.3737514
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Infinite but Rare: Valuation and Pricing in Marketplaces for Blockchain-Based Virtual Items

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Cited by 18 publications
(15 citation statements)
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“…For this reason, given the bell-shaped distribution of the log transformed average price in Figure 2b and knowing that in the literature, the two most used distributions are the gamma and the log-normal (de Jong and Heller, 2008), we choose the latter to model the average price per NFT. Nadini et al (2021) and Kireyev and Lin (2021) show that machine learning (ML) algorithms are particularly suitable for capturing the high volatility patterns typical of the NFT market, more than a simple linear model would do. We also challenge the predictive performances of GLMs with non-parametric models.…”
Section: Approachmentioning
confidence: 99%
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“…For this reason, given the bell-shaped distribution of the log transformed average price in Figure 2b and knowing that in the literature, the two most used distributions are the gamma and the log-normal (de Jong and Heller, 2008), we choose the latter to model the average price per NFT. Nadini et al (2021) and Kireyev and Lin (2021) show that machine learning (ML) algorithms are particularly suitable for capturing the high volatility patterns typical of the NFT market, more than a simple linear model would do. We also challenge the predictive performances of GLMs with non-parametric models.…”
Section: Approachmentioning
confidence: 99%
“…The authors point out that parcels with easy-to-remember coordinates or coordinates close to the centre of the metaverse tend to have higher prices. Kireyev and Lin (2021) show that the price of the CryptoKitties NFTs declines over time as more NFTs are generated. They argue that the hedonic regression approach is not the optimal method for determining the price of NFTs and claim that models such as the gradient boosting machine (GBM) are superior since they can handle a potential selection bias much better.…”
Section: Introductionmentioning
confidence: 97%
“…Previous results show effects of several determinants on NFTs pricing (Kräussl & Tugnetti, 2022): rarity, NFTs market size, NFT market participants, and favorable or unfavorable characteristics (Kong & Lin, 2021;Schaar & Kampakis, 2022); physical and virtual location in the metaverse (Goldberg et al, 2021); centrality on the trader network, sales history and visual characteristics (Nadini et al, 2021); selling rate and NFT features (ID and generation) (Kireyev & Lin, 2021); relationships between different projects, price of Bitcoin (BTC) and Ether (ETH) (Ante, 2021a(Ante, , 2021bDowling, 2022b); bitcoins and alternative asset classes (bonds, crude oil, gold, stocks) (Umar et al, 2022). In some cases, results are somewhat contradictory.…”
Section: Introductionmentioning
confidence: 98%
“…For all these reasons, several previous studies have applied it to diagnose art markets [4,10,11]. Some works also used the hedonic model to price NFTs, but focused on a single collection: CryptoKitties [12], CryptoPunks [13] or Decentraland [14]. To the best of our knowledge, our article is the first proposal of a global NFT price index.…”
Section: Introductionmentioning
confidence: 99%