Bitcoin price and its marginal cost of production: support for a fundamental value

Hayes, Adam

doi:10.1080/13504851.2018.1488040

Cited by 101 publications

(71 citation statements)

References 18 publications

(19 reference statements)

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“…This result is different from the one obtained by Hayes (2019) and Abbatemarco et al (2018), who conclude that the Bitcoin price could be explained by the cost of production model.…”

Section: Discussioncontrasting

confidence: 93%

The Bitcoin as a Virtual Commodity: Empirical Evidence and Implications

Baldan

Zen

2020

Front. Artif. Intell.

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The present work investigates the impact on financial intermediation of distributed ledger technology (DLT), which is usually associated with the blockchain technology and is at the base of the cryptocurrencies' development. "Bitcoin" is the expression of its main application since it was the first new currency that gained popularity some years after its release date and it is still the major cryptocurrency in the market. For this reason, the present analysis is focused on studying its price determination, which seems to be still almost unpredictable. We carry out an empirical analysis based on a cost of production model, trying to detect whether the Bitcoin price could be justified by and connected to the profits and costs associated with the mining effort. We construct a sample model, composed of the hardware devices employed in the mining process. After collecting the technical information required and computing a cost and a profit function for each period, an implied price for the Bitcoin value is derived. The interconnection between this price and the historical one is analyzed, adopting a Vector Autoregression (VAR) model. Our main results put on evidence that there aren't ultimate drivers for Bitcoin price; probably many factors should be expressed and studied at the same time, taking into account their variability and different relevance over time. It seems that the historical price fluctuated around the model (or implied) price until 2017, when the Bitcoin price significantly increased. During the last months of 2018, the prices seem to converge again, following a common path. In detail, we focus on the time window in which Bitcoin experienced its higher price volatility; the results suggest that it is disconnected from the one predicted by the model. These findings may depend on the particular features of the new cryptocurrencies, which have not been completely understood yet. In our opinion, there is not enough knowledge on cryptocurrencies to assert that Bitcoin price is (or is not) based on the profit and cost derived by the mining process, but these intrinsic characteristics must be considered, including other possible Bitcoin price drivers.

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“…This result is different from the one obtained by Hayes (2019) and Abbatemarco et al (2018), who conclude that the Bitcoin price could be explained by the cost of production model.…”

Section: Discussioncontrasting

confidence: 93%

The Bitcoin as a Virtual Commodity: Empirical Evidence and Implications

Baldan

Zen

2020

Front. Artif. Intell.

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“…He argues for a fundamental value given the cost of mining. Similarly, Hayes (2015Hayes ( , 2018 proposed a specific cost of production model for valuating Bitcoin. Additionally, Cheah and Fry (2015) conclude that Bitcoin is a speculative bubble and that the fundamental value of Bitcoin is zero.…”

Section: Literature Reviewmentioning

confidence: 99%

An Analysis of Bitcoin’s Price Dynamics

Kjærland

Khazal

Krogstad

et al. 2018

JRFM

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This paper aims to enhance the understanding of which factors affect the price development of Bitcoin in order for investors to make sound investment decisions. Previous literature has covered only a small extent of the highly volatile period during the last months of 2017 and the beginning of 2018. To examine the potential price drivers, we use the Autoregressive Distributed Lag and Generalized Autoregressive Conditional Heteroscedasticity approach. Our study identifies the technological factor Hashrate as irrelevant for modeling Bitcoin price dynamics. This irrelevance is due to the underlying code that makes the supply of Bitcoins deterministic, and it stands in contrast to previous literature that has included Hashrate as a crucial independent variable. Moreover, the empirical findings indicate that the price of Bitcoin is affected by returns on the S&P 500 and Google searches, showing consistency with results from previous literature. In contrast to previous literature, we find the CBOE volatility index (VIX), oil, gold, and Bitcoin transaction volume to be insignificant.

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“…It is broadly accepted that the probability of finding a valid block-the "arrival rate" of valid blocks-is a Poisson process (Bowden, Keeler, Krzesinski, & Taylor, 2018). This is assumed by authors including Nakamoto (2008), Rosenfeld (2011), Eyal and Sirer (2013), Decker and Wattenhofer (2013), Rosenfeld (2014), A. K. Miller and LaViola (2014), Sapirshtein, Sompolinsky, and Zohar (2015), Göbel, Keeler, Krzesinski, and Taylor (2015), Lewenberg, Bachrach, Sompolinsky, Zohar, and Rosenschein (2015), Houy (2016), Cocco and Marchesi (2016), Solat and Potop-Butucaru (2016), Beccuti and Jaag (2017), Chiu and Koeppl (2017), Dimitri (2017), Aggarwal, Brennen, Lee, Santha, and Tomamichel (2018), L. Cong, Li, and Wang (2018), Hayes (2019), Easley, O'Hara, and Basu (2019), L. W. Cong, He, and Li (2019), and Wang et al (2019). The assumption that the mining process follows the Poisson distribution implies that the miners' probabilities of winning remain constant throughout the race (i.e., throughout the time that elapses between the moment at which a miner starts trying to find the next valid block and the moment at which any miner finds and broadcasts that valid block).…”

Section: Introductionmentioning

confidence: 99%

Urns Filled with Bitcoins: New Perspectives on Proof-of-Work Mining

2019

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The probability of a miner finding a valid block in the bitcoin blockchain is assumed to follow the Poisson distribution. However, simple, descriptive, statistical analysis reveals that blocks requiring a lot of time to find-long blocks-are won only by miners with a relatively higher hash power per second. This suggests that relatively bigger miners might have an advantage with regard to winning long blocks, which can be understood as a sort of "within block learning". Modelling the bitcoin mining problem as a race, and by means of a multinomial logit model, we can reject that the time spent mining a particular block does not affect the probability of a miner finding a valid version of this block in a manner that is proportional to her size. Further, we postulate that the probability of a miner finding a valid block is governed by the negative hypergeometric distribution. This would explain the descriptive statistics that emerge from the data and be aligned with the technical aspects of bitcoin mining. We draw an analogy between bitcoin mining and the classical "urn problem" in statistics to sustain our theory. This result can have important consequences for the miners of proof-of-work cryptocurrencies in general, and for the bitcoin mining community in particular.

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Bitcoin price and its marginal cost of production: support for a fundamental value

Cited by 101 publications

References 18 publications

The Bitcoin as a Virtual Commodity: Empirical Evidence and Implications

The Bitcoin as a Virtual Commodity: Empirical Evidence and Implications

An Analysis of Bitcoin’s Price Dynamics

Urns Filled with Bitcoins: New Perspectives on Proof-of-Work Mining

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