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


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This study back-tests a marginal cost of production model proposed to value the digital currency Bitcoin. Results from both conventional regression and vector autoregression (VAR) models show that the marginal cost of production plays an important role in explaining Bitcoin prices, challenging recent allegations that Bitcoins are essentially worthless. Even with markets pricing Bitcoin in the thousands of dollars each, the valuation model seems robust. The data show that a price bubble that began in the Fall of 2017 resolved itself in early 2018, converging with the marginal cost model. This suggests that while bubbles may appear in the Bitcoin market, prices will tend to this bound and not collapse to zero.
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Bitcoin price and its marginal cost of production:
support for a fundamental value
Adam S. Hayes
To cite this article: Adam S. Hayes (2018): Bitcoin price and its marginal cost
of production: support for a fundamental value, Applied Economics Letters, DOI:
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Bitcoin price and its marginal cost of production: support for a fundamental value
Adam S. Hayes
Department of Sociology, University of Wisconsin-Madison, Madison, WI, USA; MSc Program in Digital Currencies, University of
Nicosia, Nicosia, Cyprus
This study back-tests a marginal cost of production model proposed to value the digital currency
Bitcoin. Results from both conventional regression and vector autoregression (VAR) models show
that the marginal cost of production plays an important role in explaining Bitcoin prices,
challenging recent allegations that Bitcoins are essentially worthless. Even with markets pricing
Bitcoin in the thousands of dollars each, the valuation model seems robust. The data show that a
price bubble that began in the Fall of 2017 resolved itself in early 2018, converging with the
marginal cost model. This suggests that while bubbles may appear in the Bitcoin market, prices
will tend to this bound and not collapse to zero.
Bitcoin; cost of production;
valuation model; bubbles;
cryptocurrency; model
D40; E47; G12; C52
I. Introduction
The price of Bitcoin increased from $250 in June
of 2015 to more than $19,000 by December of
2017, where it commanded a market capitalization
in excess of $325 billion before stabilizing around
the $10,000 level.
Presently, Bitcoin sees many
hundreds of millions of dollarsworth of transac-
tions cross its system on a daily basis; and yet, the
cryptocurrency functions entirely as its own
decentralized computer network, without any cen-
tral bank, government, or regulatory body to back
it. This has led some to conclude that the price of
the cryptocurrency is a massive speculative bub-
ble, with some researchers claiming that there is
no fundamental underpinning to its value (e.g.
Hanley 2013; Yermack 2013). Cheah and Fry
(2015), among others, echo recent comments
made by Jamie Dimon, the CEO of investment
bank JPMorgan Chase, in asserting that the funda-
mental value of Bitcoin is indeed zero, and that
the entire pursuit is a fools errand, or worse a
Even the Wall Street Journal has opined
that Bitcoin is probably worth zero.
the popularity of the cryptocurrency continues to
rise, and its price remains far from nil. Against
this backdrop, it is of growing concern to evaluate
the basis for value of Bitcoin.
Challenging the views of Mr. Dimon and the
grim hypotheses of some sceptical researchers,
Hayes (2016) suggests that Bitcoin does indeed
have a quantifiable intrinsic value and formalizes
a pricing model based on its marginal cost of
mining,or the process of creating
new Bitcoins through concerted computational
effort requires the consumption of electric power,
which incurs a real monetary cost for mining par-
ticipants, and thus the value of Bitcoin is the embo-
died costs of production (on the margin).
This study seeks to test the validity of this cost of
production theory of value by back-testing the pri-
cing model against the observed market price, going
back nearly 5 years. A simple ordinary least squares
(OLS) regression indicates that the model price
explains approximately 81% of the observed market
price and a striking 97% of the observed changes in
the postestimation results of a subsequent vector
autoregression (VAR) model is carried out, which
strongly rejects the null hypothesis that the pricing
model does not causethe market price. The
CONTACT Adam S. Hayes Department of Sociology, University of Wisconsin-Madison, Madison, WI, USA
For consistency, Bitcoin with a capital Brefers to the general system, network and protocol, while Bitcoin with a small brefers to the digital currency itself
or units thereof.
Or at least, an expected lower bound to its market price.
© 2018 Informa UK Limited, trading as Taylor & Francis Group
Granger test is used here not asset causality, but to
support the notion that the modelled price and the
observed price match up to a statistically significant
degree over time.
II. The cost of production model
The process and technical elaboration of Bitcoin pro-
duction (mining) is described at length elsewhere
(e.g.Kroll,Davey,andFelten2013; Sapirshtein,
Sompolinsky, and Zohar 2016; Nakamoto 2008).
Suffice it to say that mining involves a competition
among producers; with a novel feature that the rate of
new unit formation is fixed so that increased demand
cannot induce a greater supply, and so this elasticity is
manifest instead through increased difficulty in the
production process itself, increasing the system-wide
marginal cost of production.
The primary ongoing cost for Bitcoin produc-
tion is that of electricity, measured in dollars per
kilowatt hour (kWh). Of course, different regions
of the world will consume electricity at their local
rates (which may vary by customer type, power
generation source, and time of day) and in their
local currencies, but for the sake of convenience it
is a good working assumption that the average
rate of electricity worldwide accounting for both
residential and commercial rates is approximately
USD $0.135 per kWh.
Following Hayes (2016), the (marginal) cost of
production per day, Eday per unit of mining
power can be expressed as follows:
Eday¼ρ=1000ðÞ$=kWh WperGH=shrday
where E
is the dollar cost per day for a producer, ρ
is the hashpower (computational power) employed by
a miner, $/kWh is the dollar price per kilowatt hour,
and W per GH/s is the energy efficiency of the hard-
ware, and hrs
is the number of hours in a day.
In order to calculate the expected number of Bitcoins
the same miner can produce daily, the following equa-
tion is used to calculate the daily (marginal) product:
BTC=day¼ βρ sechr
hrday (2)
where BTC/day* is the expected level of daily
Bitcoin production when mining Bitcoin, βis the
block reward (expressed in units of BTC/block), ρ
is the hashing power employed by a miner, and δ
is the difficulty (expressed in units of GH/block)
The constant sec
is the number of seconds in an
hour and hr
is the number of hours in a day.
Presently, the block reward is 12.5 BTC per block.
According to microeconomic theory, under
conditions of competition, the marginal product
should equate with its marginal cost, which should
also equal its selling price. Because of this theore-
tical equivalence, and since cost per day is
expressed in terms of $/day and production in
BTC/day, the $/BTC price level is revealed as the
ratio of (cost/day) divided by (BTC/day). This
objective price of production, P*, serves as a logi-
cal lower bound for the market price, below which
a producer would operate at a marginal loss and
presumably remove themselves from the network.
P* is expressed in dollars per Bitcoin, given the
difficulty and cost of production:
P¼ Eday
III. Testing the model empirically
I back-test the above model usinghistorical observed
price data and compare that to what the model
would have predicted. Observed market price and
difficulty data were collected using the website, a reliable and transparent source
of Bitcoin market and protocol data, at the dates of
difficulty changes in the network (approximately
once every 2 weeks) to consistently measure market
price given a particular value of mining difficulty,
from 29 June 2013 through 27 April 2018.
The model requires as an input the average
energy efficiency of the mining network. This
information was extracted from Bitcoin mining
hardware manufacturer websites and checked
against a dedicated wiki page that catalogues the
efficiency of current mining hardware (https://en.
These data were then collected for each date of
difficulty change, scraped from the web using the
internet archiveswayback machine (https://web.,170,215,000,000*/https://en.bit As
stated above, for simplicity, I hold electricity
costs constant at 13.5 cents per kWh. Table A1
describes these data points along with estimated
model price for each difficulty change date.
Conventional regression analysis
As a first pass, I compared the ratio of observed price
to modelled price over time, from June 2013 through
April 2018. As shown in Figure 1, since June 2013,
the market price has tended to fluctuate about the
price estimated by the model. In the chart, a y-axis
value of 1.00 indicates that the market price and
model price are identical. The values >1.00 indicate
a premium in the market relative to the model and
<1.00 a relative discount. Over the long-run
(~5 years), the average ratio is 1.05, σ= 0.33, which
is striking in its accuracy. This suggests that the
market for Bitcoin has been quite efficient from a
production standpoint, if not volatile, contradicting
assertions that this market is consistently inefficient
(e.g. Urquhart 2016). There is evidence of increased
volatility from approximately September 2017
through January 2018, indicating that the market
had deviated substantially from the model, but did
eventually converge once again. This spike indicates
the emergence and reconciliation of a price bubble;
however, the presence of such bubbles does not
indicate a zero value, only that prolonged departures
from the modelled price can exist, but which ulti-
mately resolves to the marginal cost of production.
This initial result is suggestive, and so a more
rigorous analysis was undertaken to test the fitof
the pricing model against observed historical data.
The valuation model output and observed prices
appear in Figure 2, with what amounts to two time
series for comparison. A conventional OLS regres-
sion was first carried out to obtain a proxy for model
fit and to judge how much of the market price is
described by the model; which produces R
= 0.813,
telling us that 81% of the observed market price can
be explained by the marginal cost of production
model over the sample period. Next, I conduct a
second OLS regression on the log transformations
of each time series, yieldingan R
= 0.969, suggesting
that nearly all of the marginal change in market price
can be explained by the change in marginal cost.
VAR Granger analysis
Next, in order to compare these two time series
directly to each other in a methodologically rigorous
way, I estimate a multivariate VAR with two lags each
on the log transformation of market price and implied
model price.
The purpose of the VAR is primarily to
test the postestimation results using a Granger test
(Geweke 1982).Typicallyusedtosuggesttemporal
causality, I instead use this test here to evaluate the
3.000 Ratio of Bitcoin Market Price to Model Price
Figure 1. Ratio of Bitcoin price observed in the market to the expected price produced by the model using historical data (source: 1.00 would indicate that the two prices are identical, anything over 1.00 indicates a premium in the market
and below a discount. The average for the study period is 1.05, σ= 0.33, indicating that over the long-term, the market price seems
to fluctuate around the modelled price with striking consistency.
Testing for autocorrelation suggests that two is the appropriate number of lags.
post-hoc predictive power of the cost of production
pricing model. The test considers two null hypotheses:
1: The market price does not causethe model
price; and
2: Themodelpricedoesnot causethe market
As shown in Table 1,H
1 cannot be rejected, which
is to be expected: the model is supposed to describe
the market and not the other way around. H
2, how-
ever, is strongly rejected, and the alternative hypoth-
esis that the model price implies the market price is
given a large degree of support (p< 0.001). This key
finding lends credibility that the marginal cost of
production of Bitcoin describes its price and disputes
those who claim that Bitcoin is worthless.
IV. Discussion and conclusion
The marginal cost of production has been proposed as
a model to value Bitcoin (Hayes 2016). In this paper,
the cost of production model was back-tested using
historical data showing that the market price of
Bitcoin tends to fluctuate around the model price,
and with the model price explaining the market
price in a statistically significant manner.
This finding is striking given the volume of recent
media accounts and research projects that have sup-
posed no fundamental value at all for Bitcoin (e.g.
Cheah and Fry 2015). Moreover, it suggests that
attempts to find a causal link between the long-run
price of Bitcoin and various exogeneous factors may
be misguided (e.g. Ciaian et al. 2016; Kristoufek and
Scalas 2015; Polasik et al. 2015), as well as attempts to
value Bitcoin as if it were a traditional financial asset
(e.g. Cretarola, Figà-Talamanca, and Patacca 2017).
These findings are also indicative that the Bitcoin
market is susceptible to price bubbles, as has been
suspected. However, despite a significant deviation
in price to the upside from the Fall of 2017 through
early 2018, the cost or production model has
remained resilient as the market price did ultimately
converge with the model. This novel pricing method
will fall and/or the mining difficulty will increase to
Table 1. Granger test on VAR postestimation equations.
Granger causality Wald tests
HO χ
df Prob > χ
1: Market price does not Granger
cause the model price
4.579 2 0.101
2: Model price does not Granger
cause the market price
13.301 2 0.001
HO l cannot be rejected, which is to be expected: the model is supposed to
describe the market and not the other way around. H02 however, is strongly
rejected, and the alternative hypothesis that the model price implies the
market price is given a large degree of support (p< 0.001). This key finding
lends credibility that the marginal cost of production of bit coin describes its
price and disputes those who claim that bit coin is worthless.
Figure 2. Historical Bitcoin market price vs. ex-post implied model price, June 2013April 2018.
mechanisms were at play: the price fell and the mining
difficulty rose simultaneously. Bubbles in the Bitcoin
market have been explored in-depth elsewhere (e.g.
Garcia et al. 2014; Cheah and Fry 2015;Lietal.2018;
Hafner 2018). Cheung, Roca, and Su (2015)aswellas
Su et al. (2018) use the Phillips, Shi, and Yu (2013,
2015)) method of bubble detection, confirming that
multiple short-lived bubbles have characterized
Bitcoin prices, with four explosive bubblessince
2011 inclusive of the late-2017 period already
described (see also: Corbet, Lucey, and Yarovya
2017). The current study adds to this literature sug-
gesting that while bubbles can andindeedhave
existed in the Bitcoin market, the resolution of such a
bubble will not be a collapse towards zero, but rather
towards its marginal cost of production. Future ana-
lysis of Bitcoin bubbles given this prediction can be
explored further following the method elaborated by
Pavlidis, Paya, and Peel (2017)sincetherecentintro-
duction of Bitcoin futures markets in December 2017.
This type of analysis, however, will require a lengthier
time series than what is presently available.
It is important to note that the above analyses apply
other cryptocurrencies such as Ethereum or Litecoin;
although a similar study may indeed support the cost
of production thesis there as well. Still, with Bitcoin
dominating the digital currency market, both in scale
and scope, it is a worthwhile pursuit to understand
why this unique asset has value.
Disclosure statement
No potential conflict of interest was reported by the author.
Adam S. Hayes
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Table A1.
Date Difficulty (observed) Market price (observed) Energy efficiency (J/GH) Implied model price Ratio mkt price/model price
06/29/13 21,335,329 77.87 500 68.73 1.133
08/03/13 37,392,766 94.95 400 96.36 0.985
09/04/13 86,933,018 119.10 225 126.01 0.945
10/16/13 267,731,249 164.08 100 172.48 0.951
10/25/13 390,928,788 195.05 90 226.67 0.861
11/05/13 510,929,738 333.93 90 296.25 1.127
12/21/13 1,180,923,195 683.69 90 684.72 0.998
01/24/14 2,193,847,870 792.02 60 848.03 0.934
02/05/14 2,621,404,453 682.21 45 759.97 0.898
02/16/14 3,129,573,175 580.01 25 504.05 1.151
02/28/14 3,815,723,799 614.46 25 614.57 1.000
03/13/14 4,250,217,920 595.75 25 684.55 0.870
03/24/14 5,006,860,589 495.02 15 483.85 1.023
04/05/14 6,119,726,089 449.62 10 394.26 1.140
04/17/14 6,978,842,560 468.89 10 449.61 1.043
04/29/14 8,000,872,136 434.67 9 463.91 0.937
05/12/14 8,853,416,309 457.65 9 513.34 0.892
05/24/14 10,455,720,138 598.07 9 606.24 0.987
06/05/14 11,756,551,917 616.04 8 605.93 1.017
06/18/14 13,462,580,115 584.68 7 607.12 0.963
06/29/14 16,818,461,371 623.82 6 650.11 0.960
07/12/14 17,336,316,979 616.36 5.5 614.29 1.003
07/25/14 18,736,441,558 581.91 5 603.54 0.964
08/08/14 19,729,645,941 534.26 4 508.43 1.051
08/19/14 23,844,670,039 501.86 3.3 506.94 0.990
08/31/14 27,428,630,902 474.96 3 530.12 0.896
09/13/14 29,829,733,124 426.66 2.25 432.40 0.987
09/25/14 34,661,425,924 360.23 1.75 390.78 0.922
10/09/14 35,002,482,026 375.10 1.75 394.63 0.951
10/23/14 35,985,640,265 338.82 1.5 347.75 0.974
11/05/14 39,603,666,252 370.64 1.5 382.72 0.968
11/18/14 40,300,030,328 366.80 1.5 389.45 0.942
12/02/14 40,007,470,217 352.88 1.5 386.62 0.913
12/17/14 39,457,671,307 319.05 1.25 317.76 1.004
12/30/14 40,640,955,017 288.21 1.1 288.01 1.001
01/12/15 43,971,662,056 221.97 1 283.29 0.784
01/27/15 41,272,837,895 226.81 0.85 226.01 1.004
02/09/15 44,455,415,962 231.28 0.85 243.44 0.950
02/22/15 46,684,376,317 252.45 0.85 255.65 0.987
03/08/15 47,427,554,951 276.01 0.85 259.72 1.063
03/22/15 46,717,549,645 248.65 0.85 255.83 0.972
04/05/15 49,446,390,688 233.50 0.8 254.84 0.916
04/19/15 47,610,564,513 228.89 0.8 245.38 0.933
05/03/15 47,643,398,018 238.70 0.8 245.55 0.972
05/17/15 48,807,487,245 237.34 0.8 251.55 0.944
05/31/15 47,589,591,154 228.49 0.8 245.27 0.932
06/14/15 49,692,386,355 244.30 0.8 256.11 0.954
06/28/15 49,402,014,931 261.20 0.8 254.62 1.026
07/11/15 51,076,366,303 278.70 0.8 263.25 1.059
07/25/15 52,278,304,846 280.99 0.8 269.44 1.043
08/08/15 52,699,842,409 249.33 0.75 254.64 0.979
08/22/15 54,256,630,328 222.53 0.65 227.20 0.979
09/04/15 56,957,648,455 235.00 0.65 238.52 0.985
Table A1. (Continued).
Date Difficulty (observed) Market price (observed) Energy efficiency (J/GH) Implied model price Ratio mkt price/model price
09/17/15 59,335,351,234 231.26 0.65 248.47 0.931
10/01/15 60,813,224,039 241.15 0.65 254.66 0.947
10/15/15 60,883,825,480 270.82 0.65 254.96 1.062
10/29/15 62,253,982,450 348.63 0.65 260.69 1.337
11/11/15 65,848,255,180 324.50 0.65 275.75 1.177
11/24/15 72,722,780,643 346.90 0.65 304.53 1.139
12/06/15 79,102,380,900 396.00 0.65 331.25 1.195
12/18/15 93,448,670,796 461.00 0.65 391.33 1.178
12/31/15 103,880,340,815 432.00 0.65 435.01 0.993
01/13/16 113,354,299,801 432.00 0.6 438.17 0.986
01/26/16 120,033,340,651 405.00 0.5 386.65 1.047
02/07/16 144,116,447,847 377.35 0.4 371.39 1.016
02/19/16 163,491,654,909 397.83 0.4 421.31 0.944
03/04/16 158,427,203,767 401.64 0.4 408.26 0.984
03/18/16 165,496,835,118 411.96 0.4 426.48 0.966
04/01/16 166,851,513,283 420.71 0.4 429.97 0.978
04/14/16 178,678,307,672 430.07 0.4 460.45 0.934
04/28/16 178,659,257,773 456.00 0.4 460.40 0.990
05/11/16 194,254,820,283 455.00 0.35 438.02 1.039
05/24/16 199,312,067,531 448.00 0.35 449.42 0.997
6/8/16 196,061,423,940 550.00 0.25 631.56 0.871
6/21/16 209,453,158,595 587.00 0.25 674.70 0.870
7/4/16 213,398,925,331 600.00 0.25 687.41 0.873
7/18/16 213,492,501,108 673.00 0.25 687.71 0.979
8/2/16 201,893,210,853 565.00 0.25 650.34 0.869
8/15/16 217,375,482,757 581.00 0.25 700.22 0.830
8/29/16 220,755,908,330 579.00 0.25 711.10 0.814
9/12/16 225,832,872,179 611.00 0.25 727.46 0.840
9/25/16 241,227,200,230 608.00 0.25 777.05 0.782
10/08/2016 258,522,748,405 616.00 0.25 832.76 0.740
10/22/2016 253,618,246,641 653.00 0.25 816.96 0.799
11/05/2016 254,620,187,304 712.00 0.25 820.19 0.868
11/18/16 281,800,917,193 750.00 0.25 907.74 0.826
12/02/16 286,765,766,821 764.00 0.25 923.74 0.827
12/15/16 310,153,855,703 782.00 0.25 999.08 0.783
12/28/16 317,688,400,354 955.00 0.25 1,023.35 0.933
01/10/17 336,899,932,796 902.00 0.25 1,085.23 0.831
01/22/17 392,963,262,344 923.00 0.25 1,265.82 0.729
02/05/17 422,170,566,884 1,018.00 0.25 1,359.91 0.749
02/18/17 440,779,902,287 1,061.00 0.25 1,419.85 0.747
03/03/17 460,769,358,091 1,272.00 0.25 1,484.24 0.857
03/17/17 475,705,205,062 1,131.00 0.25 1,532.35 0.738
03/30/17 499,635,929,817 1,037.00 0.25 1,609.44 0.644
04/13/17 520,808,749,422 1,193.00 0.25 1,677.64 0.711
04/27/17 521,974,519,554 1,314.00 0.25 1,681.40 0.781
05/10/17 559,970,892,891 1,821.00 0.25 1,803.79 1.010
05/23/17 595,921,917,085 2,379.00 0.25 1,919.60 1.239
06/04/17 678,760,110,083 2,698.00 0.25 2,186.44 1.234
06/17/17 711,697,198,174 2,507.00 0.25 2,292.54 1.094
07/02/17 708,659,466,230 2,561.00 0.25 2,282.75 1.122
07/17/17 804,525,194,568 2,059.00 0.25 2,591.56 0.795
07/27/17 860,221,984,436 2,570.00 0.25 2,770.97 0.927
08/09/17 923,233,068,449 3,424.00 0.25 2,973.94 1.151
08/24/17 888,171,856,257 4,363.00 0.25 2,861.00 1.525
09/06/17 922,724,699,726 4,311.00 0.25 2,972.30 1.450
09/18/17 1,103,400,932,964 3,943.00 0.25 3,554.30 1.109
10/02/17 1,123,863,285,133 4,293.00 0.25 3,620.22 1.186
10/15/17 1,196,792,694,099 5,711.00 0.25 3,855.14 1.481
10/26/17 1,452,839,779,146 5,773.00 0.25 4,679.92 1.234
11/10/17 1,364,422,081,125 6,363.00 0.25 4,395.11 1.448
11/24/17 1,347,001,430,559 8,707.00 0.25 4,339.00 2.007
12/07/17 1,590,896,927,258 11,000.00 0.25 5,124.64 2.146
12/18/17 1,873,105,475,221 17,373.00 0.25 6,033.70 2.879
01/01/18 1,931,136,454,487 15,000.00 0.22 5,474.15 2.740
01/13/18 2,227,847,638,503 13,850.00 0.22 6,315.23 2.193
01/25/18 2,603,077,300,218 11,000.00 0.22 7,378.89 1.491
02/06/18 2,860,000,000,000 8,100.00 0.2 7,370.16 1.099
02/20/18 3,007,383,866,429 10,500.00 0.2 7,749.97 1.355
03/05/18 3,290,605,988,754 10,750.00 0.2 8,479.83 1.268
03/19/18 3,462,542,391,191 9,000.00 0.2 8,922.90 1.009
04/01/18 3,511,060,552,899 7,000.00 0.18 8,143.14 0.860
04/14/18 3,839,316,899,029 8,350.00 0.18 8,904.46 0.938
04/27/18 4,022,059,196,164 9,330.00 0.18 9,328.29 1.000
This table consists of historical data observed for Bitcoin mining difficulty and price as well as estimated average network energy efficiency. Modelled prices also appear
along with the ratio of market price to modelled price. Energy prices for the model held constant at $0.135 per kWh. Data source:
... Cryptocurrency miners consume a large amount of electrical energy to run their cryptocurrency mining devices (CMDs). These CMDs have a powerful computational capability to solve a complicated mathematical problem that validates the transactions between the digital currency's holders [2]. In response, they are rewarded in the form of digital currency to compensate for their expenditures. ...
... In this equation, the prices of buying/selling power from/to the main grid are different, as discussed in [28]. Equation (2) represents the power balance in the MG's point of common coupling (PCC). The maximum exchange power between the MG and the main grid should also be limited as (3). ...
Full-text available
The introduction of cryptocurrencies as a new form of money has attracted a tremendous amount of attention in recent years. This new financial paradigm relies on miners to validate transactions by running their cryptocurrency mining devices (CMDs). Nowadays, the significant profitability of the mining business has tempted a large number of private players in the electrical industry to employ their renewable energy resources to mine digital currency. Here, microgrid (MG) owners may use their excessive generated power to mine digital money instead of exporting it to the main grid. This paper is devoted to investigate the influential potentials of this trending business on distribution networks operation and performance. Thus, a new energy management (EM) formulation is proposed to model the mining loads at the first step. Then, a Monte-Carlo simulation is introduced to obtain the annual profit of this mining business under the existing uncertainties. Afterward, appropriate financial indices are proposed to help the MG owner to choose the best type of CMDs, and their optimal number to be installed. Finally, this paper demonstrates how the current price profile of electricity will gradually tend the grid-interactive MG to initiate the mining business in many countries. The results show that the MG acts as a passive energy entity where the import of electricity to mine cryptocurrency is possible. In a nutshell, the higher the electricity price, the lower the mining installation and the more active MG to positively contribute to electricity generation.
... In blockchain technology, some actors, known as miners, to get rewards may promote transactions by solving a challenging computational problem and making a new "transaction block". The more computational power of a miner, the more blocks are created in a specific period, and as a result, the more reward is attained [4]. ...
... Thus, its cost is a function of electricity price in dollars per kWh, the device operation time, and its efficiency. The relation of a miner's operational cost is presented in the following [4]: ...
... The price volatility often associated with cryptocurrency trade and investments have led to parallels being drawn with past historical financial market bubbles such as The South Sea, Mississipi and Tulia Mania bubbles [67][68][69][70][71][72]. The key lessons from these parallels relates to the notion of investment irrationality, monetary policy shocks and the need to balance the risk/return trade-off arising from investments [73][74][75]. ...
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This paper examined cryptocurrency and its global practices with particular reference to salient lessons for the Nigerian economy. The desk review methodology anchored on content analysis was used for the study. The paper identified distrust in political systems, weak domestic currency and high inflation rates as key factors fueling the growth of cryptocurrency usage in Nigeria thus motivating individuals to resort to cryptocurrencies as a tool for wealth preservation and inflation hedge. The study also found that the existence of trust deficit and challenges associated with privacy concerns, system uptime and stringent onboarding requirements were capable of derailing the success of the newly launched digital currency(‘e-naira’) issued by government to curtail cryptocurrency usage in Nigeria. The study concluded that cryptocurrencies and central bank issued digital currencies (CBDCs) are now part and parcel of the new economic order and represents the future of finance. It therefore recommended that nation states should work assiduously to develop uniformly agreed regulatory framework and global standards for the usage of cryptocurrencies.
... The studies generally conclude that the asset price depends on speculative and supply and demand factors but not on economic and monetary factors (e.g. Garcia et al., 2014 ;Baek and Elbeck, 2015;Bouoiyour and Selmi, 2015;Chead and Fry, 2015;Cheung et al., 2015 ;Ciaian et al., 2016;Fry et Cheah, 2016 ;Hayes, 2017Hayes, , 2019Eom et al., 2019). However, more recently, academic research analyzed the theoretical pricing of BTC, which depends on the utility that it provides. ...
This paper analyzes Bitcoin investment in terms of portfolio diversification. Over the period July 2011-April 2021, we use the copula-GARCH approach to test the time-varying dependence of Bitcoin in a portfolio composed of six stock markets (CAC40, DJIA, EUROSTOXX50, FTSE100, HANGSENG, and NIKKEI225). Our results reveal that volatility modeling provides better results with the Dynamic Conditional Correlation model. The performance of the portfolio is largely due to the high returns of Bitcoin which allows for better portfolio diversification. As a result, there is a mitigation of the extreme rates of return associated with crypto-currencies. Finally, while Bitcoin's contribution to the portfolio is more attributable to its risk than its return, it does play a role in stabilizing portfolio performance, for varying levels of risk.
... 2016; Cong et al. 2021;Hayes, 2017;Huhtinen, 2014;Kristoufek, 2015;Li & Wang, 2017;Liu et al., 2020;Liu & Tsyvinski, 2021), production factors (e.g., Chen et al., 2021;Hayes, 2019;Georgoula et al., 2015;Li & Wang, 2017;Liu & Tsyvinski, 2021;Poyser, 2019;Sockin & Xiong, 2021), trade volume (e.g., Aalborg et al., 2019;Balcilar et al., 2017;Bouri et al., 2021;Cheah et al., 2020;Figà-Talamanca & Patacca, 2019;Mai et al., 2018;Makarov & Schoar, 2020), supply and demand forces (e.g., Buchholz et al., 2012;Ciaian et al., 2016;De La Horra et al., 2019;Huhtinen, 2014;Goczek & Skliarov, 2019;Gronwald, 2015;Kancs et al., 2019;Kristoufek, 2015;Li & Wang, 2017;Polasik et al., 2015), price swings (e.g., Aalborg et al., 2019;Ahmed, 2020;Ahmed & Al Mafrachi, 2021;Cheah et al., 2020;De La Horra et al., 2019;Sovbetov, 2018), and return momentum (e.g., Cheah et al., 2020;Cheng et al., 2019;Liu et al., 2020;Liu & Tsyvinski, 2021;Nguyen et al., 2020). The second category epitomizes the growing international attention to, and recognition of, Bitcoin and the likes, which is generally measured by social media networks and web analytic tools, such as Reddit (e.g., Bukovina & Marticek, 2016;Chen et al., 2019;Phillips & Gorse, 2017, Twitter (e.g., Chen et al., 2021;Garcia & Schweitzer, 2015;Georgoula et al., 2015;Hu et al., 2019;Kaminski, 2014;Kraaijeveld & De Smedt, 2020;Li & Wang, 2017;Liu & Tsyvinski, 2021;Sabalionis et al., 2021;Shen et al., 2019), crypto forum posts (e.g., Guégan & Renault, 2021;Kim et al., 2017;Mai et al., 2018), Wikipedia search queries (e.g., Ciaian et al., 2016;Cretarola et al., 2020;Georgoula et al., 2015;Kim et al., 2017;Kristoufek, 2013;Kristoufek, 2015;Panagiotidis et al., 2019), and Google trends (e.g., Chen et al., 2021;Cretarola et al., 2020;Dastgir et al., 2019;Figà-Talamanca & Patacca, 2019;Garcia & Schweitzer, 2015;Georgoula et al., 2015;Kristoufek, 2013;Kristoufek, 2015;Li & Wang, 2017). ...
There is a growing stream of empirical research that endeavors to identify the influential variables contributing to the price formation of cryptocurrencies and, in particular, Bitcoin. However, results of those studies generally remain inconsistent in terms of not only the true combination of factors that affect Bitcoin prices, but also the nature of effects (positive vs. negative) that each individual factor has on the price behavior. The present study investigates the robustness of a wide variety of candidate determinants that have been the focus of attention in relevant literature. Our inquiry relies on the extreme bounds analysis (EBA), which is a type of large-scale sensitivity analysis capable of addressing model uncertainty issues. The findings suggest that crypto market forces of supply and demand, public interest, and economic policy uncertainty are the only variables robust to all possible variations in the conditioning information set. Our evidence argues in favor of the predominance of cryptocurrency-related determinants over global macroeconomic and financial ones in explaining Bitcoin price movements.
... The results show that both fundamental (transactions, price level, supply) and speculative attention-based factors drive the dynamics. Hayes (2019) argues that the marginal cost of production is essential for explaining Bitcoin prices, thus challenging the allegations that Bitcoin is worthless from the standard economic viewpoint. Kristoufek (2019) adds the quantity theory of money to the equation, showing that the price dynamics (not necessarily the price itself, as the price level for the US economy is not available in USD terms) closely follows the one implied by the fundamental economic laws. ...
Full-text available
The driving forces behind cryptoassets' price dynamics are often perceived as being dominated by speculative factors and inherent bubble-bust episodes. The fundamental components are believed to have a weak, if any, role in the price formation process. This research studies five cryptoassets with different backgrounds, including Bitcoin, Ethereum, Litecoin, XRP, and Dogecoin between 2016 and 2022. It utilizes the cusp catastrophe model to connect the fundamental and speculative drivers with possible price bifurcation characteristics of events of a market collapse. The findings show that all studied assets except Dogecoin demonstrate their price and returns dynamics emerge from complex interactions among both fundamental and speculative components, including episodes of price bifurcations. Bitcoin shows the strongest fundamentals, with the on-chain activity and economic factors driving the fundamental part of the dynamics. Investor attention and off-chain activity mainly drives the speculative component for all studied assets. Within the fundamental drivers, the analyzed cryptoassets present their coin-specific factors, which can be tracked to their protocol specifics and are economically sound.
Bitcoin pricing mechanism is a complex system of interactions between factors that are not standard for traditional financial assets. Its understanding is essential for assessing specific topics, most prominently the interaction between Bitcoin price and network’s hashrate as it directly translates into its power demand and consumption and thus also environmental implications. We examine an intertwined system of equations, controlling for various statistical caveats connected to such system, providing a coherent picture of the system dynamics and thus delivering the most rigorous and complex approach in explaining the pricing dynamics of the Bitcoin system up to date. We shown that the whole system is very well structured and delivers economically and logically sound results, pointing at the network security narrative in the Bitcoin price-hashrate nexus.
This paper explores the role of media coverage in bubble formation in the Bitcoin market. Three main findings emerge. First, media coverage, regardless of the tone, increases the next day’s Bitcoin returns in the bubble period but not in the non-bubble period. Second, the Bitcoin returns can predict media coverage of Bitcoin both in the bubble and non-bubble periods. Finally, there is an insignificant relationship between media coverage and the next day’s Bitcoin’s trading volume in the bubble period but a negative relationship between them in the non-bubble period. Overall, our findings demonstrate that media coverage can act as a driver of Bitcoin returns during bubbles, providing support to Shiller’s argument and advancing understandings of the formation of bubbles and influences of media coverage.
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Energy production is a phenomenon that has always preserved its importance for the history of humanity, as well as where the energy is spent and its consumption are also important. This study examined the causality relationship between Bitcoin energy consumption and Apple, Dell Technologies, Lenova Group, HP, Quanta Computer, Compal Electronics, Canon, Wistron and Hewlett Packard Enterprise has been taken into account to represent technology companies’ stock market. In the analysis, daily price data for the period 12.02.2017-07.02.2021 were used. Toda-Yamamoto (1995) symmetric causality test and Hatemi-J (2012) asymmetric causality test were used for used to determine the relationship between Bitcoin energy consumption and technology companies’ stock values. According to the results of the Toda-Yamamoto (1995) causality test, it has been found that there is a causality from Bitcoin energy consumption to Apple's stock value; according to the Hatemi-J (2012) asymmetric causality test results, it has been determined that there is a causality from Bitcoin energy consumption positive shocks to Apple, Dell Technologies, Lenova Group, HP, Quanta Computer, Compal Electronics, Canon, Wistron and Hewlett Packard Enterprise stock values negative shocks and from Bitcoin energy expenditure negative shocks to Hewlett Packard Enterprise negative shocks. According to the results of the study in general, it is seen that the change in Bitcoin energy consumption has an effect on the firm returns of the companies that sell the necessary tools for bitcoin energy production. From this, it can be commented that bitcoin mining is also effective on the stock returns of technology companies as well as many financial factors.
Full-text available
This paper explores when will occur and collapse in Bitcoin bubbles by applying generalized sup augmented Dickey–Fuller test method proposed by Phillips et al. (Testing for multiple bubbles: historical episodes of exuberance and collapse in the S&P 500. Singapore Management University, Working Paper, No. 04-2013, 2013). The results show that there are six explosive bubbles in China and five bubbles in U.S. market, mostly occur in the period of huge surges in Bitcoin price. This is consistent with the bubble model originated by Blanchard and Watson (Bubbles, rational expectations and financial markets. NBER Working Paper, No. 945 1982) that certain asset price is decomposed into fundamental and the bubble components. In particular, exogenous shocks, including foreign or domestic economic events lead to the origination of bubbles. Serious financial crisis may trigger long-term and large- scale bubbles, while relative not persistence (short-term) bubbles are caused by domestic particular components. It can be inferred that Bitcoin can be used as a hedge against market specific risk. Finally, Bitcoin bubbles would collapse due to the administrative intervention by economic authorities. Thereby, government should lead public expectation to keep the confidence to authority and reduce the speculation behavior to stabilize the asset price and financial market.
This article has been retracted: please see Elsevier Policy on Article Withdrawal ( This article has been retracted at the request of the Editor-in-Chief and Author. The article is a duplicate of a paper that has already been published in journal Quality & Quantity, Volume 53, Pages 91-105, DOI: One of the conditions of submission of a paper for publication is that authors declare explicitly that the paper has not been previously published and is not under consideration for publication elsewhere. As such this article represents a misuse of the scientific publishing system. The scientific community takes a very strong view on this matter and apologies are offered to readers of the journal that this was not detected during the submission process.
We examine the existence and dates of pricing bubbles in Bitcoin and Ethereum, two popular cryptocurrencies using the (Phillips et al., 2011) methodology. In contrast to previous papers, we examine the fundamental drivers of the price. Having derived ratios that are economically and computationally sensible, we use these variables to detect and datestamp bubbles. Our conclusion is that there are periods of clear bubble behaviour, with Bitcoin now almost certainly in a bubble phase.
The probabilistic structure of periodically collapsing bubbles creates a gap between future spot and forward (futures) asset prices in small samples. By exploiting this fact, we use a recently developed recursive unit root test and rolling Fama regressions for detecting bubbles. Both methods do not rely on a particular model of asset price determination, are robust to explosive fundamentals, and allow date stamping. An application to U.S. dollar exchange rates provides evidence of bubbles during the interwar German hyperinflation, but not during the recent floating-rate period. A further application to S&P 500 supports the existence of bubbles in the U.S. equity market.
Conference Paper
The Bitcoin protocol requires nodes to quickly distribute newly created blocks. Strong nodes can, however, gain higher payoffs by withholding blocks they create and selectively postponing their publication. The existence of such selfish mining attacks was first reported by Eyal and Sirer, who have demonstrated a specific deviation from the standard protocol (a strategy that we name SM1). In this paper we investigate the profit threshold – the minimal fraction of resources required for a profitable attack. Our analysis provides a bound under which the system can be considered secure against such attacks. Our techniques can be adapted to protocol modifications to assess their susceptibility to selfish mining, by computing the optimal attack under different variants. We find that the profit threshold is strictly lower than the one induced by the SM1 scheme. The policies given by our algorithm dominate SM1 by better regulating attack-withdrawals. We further evaluate the impact of some previously suggested countermeasures, and show that they are less effective than previously conjectured. We then gain insight into selfish mining in the presence of communication delays, and show that, under a model that accounts for delays, the profit threshold vanishes, and even small attackers have incentive to occasionally deviate from the protocol. We conclude with observations regarding the combined power of selfish mining and double spending attacks.
Bitcoin has received much attention in the media and by investors in recent years, although there remains scepticism and a lack of understanding of this cryptocurrency. We add to the literature on Bitcoin by studying the market efficiency of Bitcoin. Through a battery of robust tests, evidence reveals that returns are significantly inefficient over our full sample, but when we split our sample into two subsample periods, we find that some tests indicate that Bitcoin is efficient in the latter period. Therefore we conclude that Bitcoin in an inefficient market but may be in the process of moving towards an efficient market.
This paper aims to identify the likely determinants for cryptocurrency value formation, including for that of bitcoin. Due to Bitcoin’s growing popular appeal and merchant acceptance, it has become increasingly important to try to understand the factors that influence its value formation. Presently, the value of all Bitcoins in existence represent approximately $7 billion, and more than $60 million of notional value changes hands each day. Having grown rapidly over the past few years, there is now a developing but vibrant marketplace for bitcoin, and a recognition of digital currencies as an emerging asset class. Not only is there a listed and over-the-counter market for bitcoin and other digital currencies, but also an emergent derivatives market. As such, the ability to value bitcoin and related cryptocurrencies is becoming critical to its establishment as a legitimate financial asset.