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Blockchain Characteristics and the Cross-Section of Cryptocurrency Returns

Author

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  • Korniotis, George
  • Bhambhwani, Siddharth
  • Delikouras, Stefanos

Abstract

We examine whether blockchain characteristics such as network size and computing power affect cryptocurrency prices and returns. Consistent with theoretical models, cryptocurrency prices are cointegrated with these two blockchain characteristics. Further, a stochastic discount factor with aggregate network and computing power explains the cross-sectional variation in expected cryptocurrency returns at least as well as models with cryptocurrency return-based factors (market, size, momentum). Overall, our results show that theoretically motivated factors are important sources of risk for cryptocurrency prices and expected returns.

Suggested Citation

  • Korniotis, George & Bhambhwani, Siddharth & Delikouras, Stefanos, 2019. "Blockchain Characteristics and the Cross-Section of Cryptocurrency Returns," CEPR Discussion Papers 13724, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:13724
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    Cited by:

    1. Radwanski, Juliusz, 2021. "The Equilibrium Value of Bitcoin," MPRA Paper 110746, University Library of Munich, Germany.
    2. De Pace, Pierangelo & Rao, Jayant, 2023. "Comovement and instability in cryptocurrency markets," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 173-200.
    3. Lambrecht, Marco & Sofianos, Andis & Xu, Yilong, 2021. "Does mining fuel bubbles? An experimental study on cryptocurrency markets," Working Papers 0703, University of Heidelberg, Department of Economics.
    4. Bruno Biais & Christophe Bisière & Matthieu Bouvard & Catherine Casamatta & Albert J. Menkveld, 2023. "Equilibrium Bitcoin Pricing," Journal of Finance, American Finance Association, vol. 78(2), pages 967-1014, April.
    5. Wüstenfeld, Jan & Geldner, Teo, 2022. "Economic uncertainty and national bitcoin trading activity," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    6. Nagula, Pavan Kumar & Alexakis, Christos, 2022. "A new hybrid machine learning model for predicting the bitcoin (BTC-USD) price," Journal of Behavioral and Experimental Finance, Elsevier, vol. 36(C).
    7. Kim, S. Thomas, 2022. "Is it worth to hold bitcoin?," Finance Research Letters, Elsevier, vol. 44(C).
    8. Şoiman, Florentina & Dumas, Jean-Guillaume & Jimenez-Garces, Sonia, 2023. "What drives DeFi market returns?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 85(C).
    9. Wolfgang Karl Hardle & Campbell R. Harvey & Raphael C. G. Reule, 2020. "Editorial: Understanding Cryptocurrencies," Papers 2007.14702, arXiv.org.
    10. Richard K. Lyons & Ganesh Viswanath-Natraj, 2020. "What Keeps Stablecoins Stable?," NBER Working Papers 27136, National Bureau of Economic Research, Inc.
    11. Xiaoquan Jiang & Iván M. Rodríguez & Qianying Zhang, 2023. "Macroeconomic fundamentals and cryptocurrency prices: A common trend approach," Financial Management, Financial Management Association International, vol. 52(1), pages 181-198, March.
    12. Gemayel, Roland & Preda, Alex, 2021. "Performance and learning in an ambiguous environment: A study of cryptocurrency traders," International Review of Financial Analysis, Elsevier, vol. 77(C).
    13. Raphael Auer & Marc Farag & Ulf Lewrick & Lovrenc Orazem & Markus Zoss, 2022. "Banking in the shadow of Bitcoin? The institutional adoption of cryptocurrencies," BIS Working Papers 1013, Bank for International Settlements.
    14. Wolfgang Karl Härdle & Campbell R Harvey & Raphael C G Reule, 2020. "Understanding Cryptocurrencies," Journal of Financial Econometrics, Oxford University Press, vol. 18(2), pages 181-208.
    15. Zhang, Wei & Li, Yi & Xiong, Xiong & Wang, Pengfei, 2021. "Downside risk and the cross-section of cryptocurrency returns," Journal of Banking & Finance, Elsevier, vol. 133(C).
    16. Li, Yi & Zhang, Wei & Urquhart, Andrew & Wang, Pengfei, 2022. "The role of media coverage in the bubble formation: Evidence from the Bitcoin market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    17. Kubal, Jan & Kristoufek, Ladislav, 2022. "Exploring the relationship between Bitcoin price and network’s hashrate within endogenous system," International Review of Financial Analysis, Elsevier, vol. 84(C).
    18. Liu, Weiyi & Liang, Xuan & Cui, Guowei, 2020. "Common risk factors in the returns on cryptocurrencies," Economic Modelling, Elsevier, vol. 86(C), pages 299-305.
    19. Imran Yousaf & Shoaib Ali & Elie Bouri & Anupam Dutta, 2021. "Herding on Fundamental/Nonfundamental Information During the COVID-19 Outbreak and Cyber-Attacks: Evidence From the Cryptocurrency Market," SAGE Open, , vol. 11(3), pages 21582440211, July.
    20. Ahmed M. Khedr & Ifra Arif & Pravija Raj P V & Magdi El‐Bannany & Saadat M. Alhashmi & Meenu Sreedharan, 2021. "Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(1), pages 3-34, January.

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    More about this item

    Keywords

    Hashrate; Network; Factor analysis; Gmm; Rolling estimation;
    All these keywords.

    JEL classification:

    • E4 - Macroeconomics and Monetary Economics - - Money and Interest Rates
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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