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What Factors Give Cryptocurrencies Their Value: An Empirical Analysis

Author

Listed:
  • Adam Hayes

    (Department of Economics, New School for Social Research)

Abstract

This paper aims to identify the likely source(s) of value that cryptocurrencies exhibit in the marketplace using cross sectional empirical data examining 66 of the most used such 'coins'. A regression model was estimated that points to three main drivers of cryptocurrency value: the aggregate computational power employed in mining for units of the cryptocurrency; the rate of unit production; and the cryptologic algorithm used for the protocol. Bitcoin-denominated relative prices were used, avoiding much of the price volatility associated with the dollar price of Bitcoin. The resulting model can be used so better understand the drivers of value observed in cryptocurrencies. These findings may also have implications in understanding other assets such as commodity forms of money.

Suggested Citation

  • 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.
    • Handle: RePEc:new:wpaper:1406
    as

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    File URL: http://www.economicpolicyresearch.org/econ/2014/NSSR_WP_062014.pdf
    File Function: First version, 2014
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    Citations

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    Cited by:

    1. Singh, Sanjeet & Bansal, Pooja & Bhardwaj, Nav, 2022. "Correlation between geopolitical risk, economic policy uncertainty, and Bitcoin using partial and multiple wavelet coherence in P5 + 1 nations," Research in International Business and Finance, Elsevier, vol. 63(C).
    2. Tomić, Bojan, 2020. "BITCOIN: Systematic Force of Cryptocurrency Portfolio," MPRA Paper 101290, University Library of Munich, Germany, revised 26 May 2020.
    3. Mayer, Fabian & Bofinger, Peter, 2023. "Cryptocurrency competition: An empirical test of Hayek's vision of private monies," W.E.P. - Würzburg Economic Papers 103, University of Würzburg, Department of Economics.
    4. Adam Hayes, 2015. "The Decision to Produce Altcoins: Miners' Arbitrage in Cryptocurrency Markets," Working Papers 1504, New School for Social Research, Department of Economics.
    5. Sumit Ranjan & Parthajit Kayal & Malvika Saraf, 2023. "Bitcoin Price Prediction: A Machine Learning Sample Dimension Approach," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1617-1636, April.
    6. Adam Hayes, 2015. "A Cost of Production Model for Bitcoin," Working Papers 1505, New School for Social Research, Department of Economics.
    7. Beate Sauer, 2016. "Virtual Currencies, the Money Market, and Monetary Policy," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 22(2), pages 117-130, May.
    8. Aiman Hairudin & Imtiaz Mohammad Sifat & Azhar Mohamad & Yusniliyana Yusof, 2022. "Cryptocurrencies: A survey on acceptance, governance and market dynamics," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4633-4659, October.

    More about this item

    Keywords

    Bitcoin; cryptocurrencies; altcoins; asset pricing; money; payment systems; currency exchanges; quantitative analysis;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • 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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