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Forecasting short-term transaction fees on a smart contracts platform

Author

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  • Charles Hoffreumon
  • Nicolas van Zeebroeck

Abstract

In the present article, we analyze the transaction fees market on smart contracts-enabling blockchains. On such systems, as opposed to traditional on-premise and cloud computing solutions, users are effectively competing for computational resources through an auction for priority. This paper proposes a way to estimate the bid one has to offer to have a transaction included in the next block. This method outperforms naive bidding (bidding the optimal value of the last block) if the user is realistically "impatient" to have a transaction processed. It also shows that users collectively spend several million of dollar every years for transaction fees that could be avoided without degrading the service received. This is this "waste" we seek to reduce throughour forecasting method.

Suggested Citation

  • Charles Hoffreumon & Nicolas van Zeebroeck, 2018. "Forecasting short-term transaction fees on a smart contracts platform," Working Papers TIMES² 2018-028, ULB -- Universite Libre de Bruxelles.
    • Handle: RePEc:ict:wpaper:2013/276709
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    References listed on IDEAS

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    1. Marie Briere & Kim Oosterlinck & Ariane Szafarz, 2015. "Virtual Currency, Tangible Return: Portfolio Diversification with Bitcoins," Post-Print CEB, ULB -- Universite Libre de Bruxelles, vol. 16(6), pages 365-373.
    2. Hal R. Varian, 2010. "Computer Mediated Transactions," American Economic Review, American Economic Association, vol. 100(2), pages 1-10, May.
    3. repec:zbw:bofrdp:2017_027 is not listed on IDEAS
    4. June Ma & Joshua S. Gans & Rabee Tourky, 2018. "Market Structure in Bitcoin Mining," NBER Working Papers 24242, National Bureau of Economic Research, Inc.
    5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    6. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    7. Richard Engelbrecht-Wiggans & Elena Katok, 2007. "Regret in auctions: theory and evidence," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 33(1), pages 81-101, October.
    8. Ladislav Kristoufek, 2015. "What Are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-15, April.
    9. Todd E. Clark & Michael W. McCracken, 2009. "Improving Forecast Accuracy By Combining Recursive And Rolling Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(2), pages 363-395, May.
    10. P. B. Solibakke, 2001. "Efficiently ARMA-GARCH estimated trading volume characteristics in thinly traded markets," Applied Financial Economics, Taylor & Francis Journals, vol. 11(5), pages 539-556.
    11. Swanson, Norman R., 1998. "Money and output viewed through a rolling window," Journal of Monetary Economics, Elsevier, vol. 41(3), pages 455-474, May.
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    More about this item

    Keywords

    Blockchain; Auctions; Forecasting; Transaction Fees; Smart Contracts;
    All these keywords.

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