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Network Activity and Ethereum Gas Prices

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  • Dimitrios Koutmos

    (Department of Accounting, Finance, and Business Law, College of Business, Texas A&M University—Corpus Christi, Corpus Christi, TX 78412, USA)

Abstract

This article explores the extent to which network activity can explain changes in Ethereum transaction fees. Such fees are referred to as “gas prices” within the Ethereum blockchain, and are important inputs not only for executing transactions, but also for the deployment of smart contracts within the network. Using a bootstrapped quantile regression model, it can be shown that network activity, such as the sizes of blocks or the number of transactions and contracts, can have a heterogeneous relationship with gas prices across periods of low and high gas price changes. Of all the network activity variables examined herein, the number of intraday transactions within Ethereum’s blockchain is most consistent in explaining gas fees across the full distribution of gas fee changes. From a statistical perspective, the bootstrapped quantile regression approach demonstrates that linear modeling techniques may yield but a partial view of the rich dynamics found in the full range of gas price changes’ conditional distribution. This is an important finding given that Ethereum’s blockchain has undergone fundamental economic and technological regime changes, such as the recent implementation of the Ethereum Improvement Proposal (EIP) 1559, which aims to provide an algorithmic updating rule to estimate Ethereum’s “base fee”.

Suggested Citation

  • Dimitrios Koutmos, 2023. "Network Activity and Ethereum Gas Prices," JRFM, MDPI, vol. 16(10), pages 1-14, September.
    • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:10:p:431-:d:1251709
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    References listed on IDEAS

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    1. Cynthia Weiyi Cai, 2018. "Disruption of financial intermediation by FinTech: a review on crowdfunding and blockchain," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(4), pages 965-992, December.
    2. Buchinsky, Moshe, 1994. "Changes in the U.S. Wage Structure 1963-1987: Application of Quantile Regression," Econometrica, Econometric Society, vol. 62(2), pages 405-458, March.
    3. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
    4. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    5. Buchinsky, Moshe, 1995. "Estimating the asymptotic covariance matrix for quantile regression models a Monte Carlo study," Journal of Econometrics, Elsevier, vol. 68(2), pages 303-338, August.
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    Cited by:

    1. Alexander Karaivanov and Shayan Zarifian, 2024. "Economic Determinants of Ethereum Transaction Fees in the Priority Fee and Proof of Stake Periods," Discussion Papers dp24-02, Department of Economics, Simon Fraser University.

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