Non-Fungible Token (NFT) Prices, Cryptocurrencies, Interest Rate and Gold: An Econometric Analysis (Jan. 2019-Aug. 2022)
Bibliographic record
Abstract
In May 2014, the animation “Quantum” was the first work to be associated with a non-fungible token (NFT) type certificate. As of 2020, the market has evolved considerably, with the millionaire figures and exponential growth typical of new disruptive technologies. Considering the recent rise of the NFT market, it is important to understand how it works and, above all, the determinants of the prices of NFTs are highlighted. Based on a detailed analysis of this new market, a GARCH multivariate econometric model is applied in order to assess whether it is possible to identify the price determinants of NFTs, based on the behavior of the prices of cryptocurrencies (Bitcoin and Ethereum), the US interest rate and the price of gold. The research is based on the study by Dowling (2022a), which sought to analyze relations between the prices of NFTs and cryptocurrencies. The results found coincide with the prices of NFTs that are similar and independent of cryptocurrencies, the interest rate and the price of gold, some specific differences to identify a determined period.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".