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A Cost of Production Model for Bitcoin

Author

Listed:
  • Adam Hayes

    () (Department of Economics, New School for Social Research)

Abstract

As bitcoin becomes more important as a worldwide financial phenomenon, it also becomes important to understand its sources of value formation. There are three ways to obtain bitcoins: buy them outright, accept them in exchange, or else produce them by 'mining'. Mining employs computational effort which requires electrical consumption for operation. The cost of electricity per kWh, the efficiency of mining as measured by watts per unit of mining effort, the market price of bitcoin, and the difficulty of mining all matter in making the decision to produce. Bitcoin production seems to resemble a competitive market, so in theory miners will produce until their marginal costs equal their marginal product. Break-even points are modeled for market price, energy cost, efficiency and difficulty to produce. The cost of production price may represent a theoretical value around which market prices tend to gravitate. As the average efficiency increases over time due to competition driving technological progress – as inefficient capital becomes obsolete it is removed while new capital replaces them – the break-even production cost of bitcoins denominated in dollars will fall. Increased efficiency, although necessary to maintain competitive advantage over other miners could serve to drive the value of bitcoin down, however adjustments in the mining difficulty and the regular halving of the block reward throughout time will tend to counteract a decreasing tendency in cost of production.

Suggested Citation

  • Adam Hayes, 2015. "A Cost of Production Model for Bitcoin," Working Papers 1505, New School for Social Research, Department of Economics.
  • Handle: RePEc:new:wpaper:1505
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    File URL: http://www.economicpolicyresearch.org/econ/2015/NSSR_WP_052015.pdf
    File Function: First version, 2015
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    References listed on IDEAS

    as
    1. David Garcia & Claudio Juan Tessone & Pavlin Mavrodiev & Nicolas Perony, 2014. "The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy," Papers 1408.1494, arXiv.org.
    2. Adam Hayes, 2015. "The Decision to Produce Altcoins: Miners' Arbitrage in Cryptocurrency Markets," Working Papers 1504, New School for Social Research, Department of Economics.
    3. Adam Hayes, 2014. "What Factors Give Cryptocurrencies Their Value: An Empirical Analysis," Working Papers 1406, New School for Social Research, Department of Economics, revised Mar 2015.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Baumohl, Eduard, 2018. "Are cryptocurrencies connected to forex? A quantile cross-spectral approach," EconStor Preprints 174884, ZBW - German National Library of Economics.
    2. repec:eco:journ1:2018-03-38 is not listed on IDEAS
    3. Fantazzini, Dean & Nigmatullin, Erik & Sukhanovskaya, Vera & Ivliev, Sergey, 2016. "Everything you always wanted to know about bitcoin modelling but were afraid to ask. I," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 44, pages 5-24.
    4. Qiang Ji & Elie Bouri & Rangan Gupta & David Roubaud, 2017. "Network Causality Structures among Bitcoin and other Financial Assets: A Directed Acyclic Graph Approach," Working Papers 201729, University of Pretoria, Department of Economics.
    5. repec:eee:finlet:v:23:y:2017:i:c:p:87-95 is not listed on IDEAS
    6. Bouri, Elie & Gupta, Rangan & Tiwari, Aviral Kumar & Roubaud, David, 2017. "Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions," Finance Research Letters, Elsevier, vol. 23(C), pages 87-95.
    7. Elie Bouri & Mahamitra Das & Rangan Gupta & David Roubaud, 2018. "Spillovers between Bitcoin and other Assets during Bear and Bull Markets," Working Papers 201812, University of Pretoria, Department of Economics.
    8. Alexandre Bovet & Carlo Campajola & Jorge F. Lazo & Francesco Mottes & Iacopo Pozzana & Valerio Restocchi & Pietro Saggese & Nicol'o Vallarano & Tiziano Squartini & Claudio J. Tessone, 2018. "Network-based indicators of Bitcoin bubbles," Papers 1805.04460, arXiv.org.

    More about this item

    Keywords

    Bitcoin; cryptocurrencies; asset pricing; cost of production models; valuation models; competitive markets;

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • D58 - Microeconomics - - General Equilibrium and Disequilibrium - - - Computable and Other Applied General Equilibrium Models
    • E42 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Monetary Sytsems; Standards; Regimes; Government and the Monetary System
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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