Is the value of NFT related to the price of a token? [Comprehensive Guide]
Hello, beloved community!
Today we decided to discuss such a delicate topic as the value of NFT compared to the value of a token. The topic of the rise or fall of the token price often comes up in our chat. This prompted us to bring some statistics and show how it actually works.
The rapid growth of the NFT market
The non-fungible token (NFT) market showed significant growth in early 2021. For example, by the end of March 2021, trading volume in these digital assets was about $550 million, and more than $200 million of that volume was in March alone. This trading growth was accompanied by a significant increase in public discussion and traditional media coverage.
NFTs can be any type of digital assets. Among the most common we can name such as collectibles and artworks, digitalized characters from any type of media content. The first step in the process of creating an NFT is to register ownership of a digital asset on a blockchain, usually an Ethereum network. This digital asset can further be transferred after making important changes in ownership. The cryptocurrency payment required for this operation is registered on the blockchain.
We have based this study on the example of the CryptoPunks. It is currently the biggest single traded market in the sphere NFTs, having traded $200m. The beginning of the market’s history is marked by the creation of 10,000 unique digital characters in 2017 and their registration as individual assets on Ethereum. The characters were trading for $50 to $100 each until around April 2020 when they started a steady inexorable price rise, with prices then exploding in early 2021. By the beginning of spring 2021, CryptoPunks were trading at between $20,000 and $100,000.
It’s crucial to understand that NFTs, being traded through cryptocurrencies, still have some significant differences comparing to them. Cryptocurrencies are intended primarily as currencies, even if they maintain some asset-like properties (Baur et al., 2018). NFTs, on the other hand, are intended as pure assets. The part “non-fungible” in the abbreviation NFT is the key moment suggesting the difference. In general, cryptocurrencies and money can be defined through such a feature as fungibility, or interchangeability (all bitcoins are the same, as well as all dollars are alike). The non-fungibility of NFTs is the most significant characteristic regarding their value.
The intersection between the participants of the cryptocurrency market and NFT market becomes obvious to anyone familiar with the NFT market. It can be explained by the need to buy an NFT for a cryptocurrency acting as a payment method, and such a procedure may become rocket science for a great number of people. Bearing that in mind, our study targets the crossover effects between cryptocurrency pricing and NFT pricing. We expect cryptocurrencies to influence NFT pricing, as, in general, larger markets tend to spill over into smaller related markets (Bhattarai et al., 2020) and cryptocurrencies are a vastly larger related market to NFTs. NFT pricing might also influence cryptocurrency markets, as NFTs and their popularity shows a strong business use case for the blockchain. This, therefore, addresses an open business point about what practical uses there are for the blockchain and the cryptocurrencies built on top of blockchains (Morkunas, Paschen, Boon, 2019, Trautman, Molesky, 2019).
With this understanding of the trader crossover between the two sets of markets, this study sets out to examine the interrelationships between cryptocurrencies and NFT markets. Moratis (2021) shows there is a large level of volatility shock transmission between cryptocurrencies, with Bitcoin dominating this transmission. Given the crossover of trading between cryptocurrencies and NFTs, and the potential leading influence of cryptocurrency pricing on NFTs, we investigate if volatility also spills over to NFT markets. To boost this investigation we further examine whether there is co-movement between cryptocurrency and NFT returns, as co-movement has been shown to be a major feature within cryptocurrency markets (Qiao et al., 2020). The discovery of links between the two sets of markets would be beneficial to researchers and practitioners alike as we could then examine trends in cryptocurrency pricing for guidance on likely trends in NFT markets.
Our study contributes to the nascent NFT pricing literature. Only one previous study has examined the pricing patterns of NFTs (Dowling, 2021). That study shows, for one NFT market, that pricing does not show signs of basic efficiency, but that there are some emerging signs of changes in pricing behavior. Given the diversity of NFT markets, our study contributes to Dowling, 2021 at a basic level by being a second study in the area and by extending testing to three NFT markets (two new markets). Our primary contribution is showing how NFT pricing relates to cryptocurrency market pricing. We find limited volatility transmission and some strong evidence of co-movement, and this gives a framework for understanding how NFT pricing might develop as the markets mature. The study at a more general level contributes to the understanding of how pricing behavior develops in new markets (Khuntia and Pattanayak, 2018).
In the next section, we discuss the data and the methodology. The section after that contains the findings and analysis. Lastly, we conclude the study.
How does it work
Our dataset starts with data for the two largest cryptocurrency markets; Bitcoin and Ether, with the raw data obtained from coinmarketcap.com. The reason for choosing these two cryptocurrencies is the direct connection between Ether and NFTs, as NFTs are, to date, primarily registered on Ethereum smart contracts, and payment is normally made through Ether. Bitcoin is selected as the market with the largest size1 and the largest volatility transmissions to other cryptocurrencies (Moratis, 2021).
Our NFT data is secondary market trades in: Decentraland LAND tokens; CryptoPunk images; and Axie Infinity game characters. Individual trade data is sourced from nonfungible.com and we aggregate2 from trade data to our time window. We chose Decentraland LAND, a virtual land NFT that exists in the Decentraland virtual world, for comparability with the data in Dowling, 2021, and because it is the largest virtual world, and the fourth largest NFT market overall. We chose CryptoPunk as it is the largest NFT market, the original NFT market, and somewhat representative of the (quite diverse) NFT art and collectible market. We chose Axie Infinity as a market with a large traded volume unlike the other two markets, as it is a representative of the NFT gaming market and the eighth largest NFT market overall by dollars traded.
The table below provides the descriptive statistics for our dataset, and Fig. 1 charts the pricing history of all markets. The time period is March 20193 to March 2021. Prices are in USD equivalents and returns are calculated on a weekly basis due to excessive variation in daily returns data, as trading in some of the NFTs is quite limited in the early time periods. We have 4,936 trades of Decentraland LAND, 7,578 trades of CryptoPunks, and 95,272 trades of Axie Infinity characters over the time period. We see there are three distinct price points across the three NFT markets also, with Axie Infinity characters at $61 average price, Decentraland at $1,109, and CryptoPunks as the most premium market at an average of $4,439. Figure 1 emphasizes the large 2021 run-up in prices for both cryptocurrencies and NFTs, although the price rise of NFTs appears more abrupt.
In the testing we first look at volatility spillovers between the markets. We are interested in whether volatility shocks are flowing to the NFT markets or from the NFT markets. We are also interested in volatility transmission within the NFT markets. For this analysis, we use the volatility spillover methodology of Diebold, Yilmaz, 2009, Diebold, Yilmaz, 2012. Without wanting to over-describe this widely known technique, the technique involves constructing a matrix of Generalized Impulse Responses which are transmissions of volatility from one market to another. The popularity of the spillover matrix is because it allows an intuitive reading of many transmissions relationships to and from markets of interest in a single table.
The second technique we use is wavelet coherence to investigate co-movement between markets. Wavelet coherence analysis allows investigation as to whether there is co-movement between two assets (bivariate wavelets) in terms of both time and frequency. We use cross-wavelets following the approach of Torrence and Compo (1998) and as specified for a cryptocurrency analysis in Goodell and Goutte (2021). We incorporate phase positions in the wavelet analysis, which help inform on the direction of influence. We discuss this further in the relevant analysis contained in the next section.
Findings and results
The following table reports the spillover effects for our selection of cryptocurrencies and NFTs. Immediately apparent from the results is that, compared to cryptocurrencies, there is much lower spillover from and to NFT markets. Further, even among the NFT markets, there is quite limited spillover, suggesting these markets are quite distinct from each other. The Decentraland LAND token market shows the greatest connection to cryptocurrencies with returns impacted about 25% from Bitcoin and Ether lagged returns.
Now we check if there might be time variation to the spillover effects. We are limited here by our short time frame, and so we choose a 50-week rolling window, meaning results are available for the last year of our sample — 2020/21. Figure 2 reports net spillover effects for each NFT and cryptocurrency, and we see that these are generally negative for NFTs and generally positive for our two cryptocurrencies. There is, however, no notable change over time and, therefore, the findings in Table 2 are appropriate.
Our last set of results applies a wavelet coherence approach. The subject of our study was the interaction between Ether (as NFTs are normally registered on an Ethereum blockchain) and the three NFT markets. We, therefore, run three bivariate wavelets; Ether-Decentraland, Ether-CryptoPunks, Ether-Axie. The wavelet coherences are reported in two following charts.
These charts show probably the most convincing co-movement for Ether-Decentraland. Based on a heat map code that red indicates strong co-movement, and that values within a black outline have significant values for correlation, we see quite a lot of co-movement between Ether and Decentraland LAND pricing. This is evident across the time period at the 1–4 week cycle and a large 8–16 week cycle that dominates the chart. A in the phase indicates positive correlation, and this is the most common arrow direction, although in the January-March 2021 upswing we see arrows that indicates that Ether pricing is leading Decentraland pricing in these most recent months. While Ether-Decentraland shows the clearest evidence of co-movement, there is also consistent evidence of short-term (1–4 week) positive correlation cycles for the other two NFT markets.
Because of the small amount of research we did above, two important conclusions can be drawn.
First, the volatility transmission appears to be an essential difference between the NFT pricing and the cryptocurrency one.
This has interesting implications for investment portfolios: in fact, assets with low correlation cause direct investor interest. This is due to diversifying characteristics.
All the basic parameters show that NFT is just such an asset.
Another interesting conclusion can be drawn basing on this research regarding the irrelevance of the NFT markets spillover. The situation with cryptocurrencies (Moratis, 2021) and stock markets is contrary (Bhattarai et al., 2020). In this case, the tendency of high spillover effects existing within individual markets is obvious.
The second conclusion is that despite the low volatility transfer between NFT and cryptocurrencies, there is little movement between them.
This suggests that it would be reasonable to cross-reference the cryptocurrency pricing behavior and NFT pricing, but it is important to consider sentiment uncertainty (Lucey et al., 2021) as a driver of both asset groups.
In essence, NFT remains a separate asset class whose value cannot be unequivocally tied to a token and assess the quality of a project by that parameter.