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The Cross-Section of Cryptocurrency Risk and Return

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  • Steffen Günther
  • Christian Fieberg
  • Thorsten Poddig

Abstract

We analyze the cross-section of more than 1200 cryptocurrencies derived from 350 exchanges in the time period from January 2014 to June 2020. Specifically, we investigate whether well-known cross-sectional characteristics like beta (Fama/MacBeth (1973)), size (Banz (1981)) or momentum ( Jegadeesh/Titman (1993)) – which have been intensively investigated in the equities literature – explain the cross-section of cryptocurrency returns. We apply the monotonic relationship (Mr.) test developed by Patton and Timmermann (2010) to test for dependencies between characteristics and average portfolio returns and standard deviations. We extend the existing literature on cryptocurrencies showing that there are various characteristics which are able to explain cryptocurrency risk and return. Wir untersuchen den Querschnitt von über 1200 Kryptowährungen, gesammelt von 350 Handelsplätzen, in der Zeitspanne von Januar 2014 bis Juni 2020. Im speziellen untersuchen wir, ob weit verbreitete Charakteristika, wie Beta (Fama/MacBeth (1973)), Size (Banz (1981)) oder Momentum ( Jegadeesh/Titman (1993)) – die bereits intensiv in der Aktienliteratur untersucht werden – den Querschnitt der Kryptowährungsrenditen erklären können. Wir verwenden den Monotonic Relationship (MR) Test von Patton und Timmermann (2010) um auf Abhängigkeiten zwischen Charakteristika und durchschnittlichen Portfoliorenditen sowie Standardabweichungen zu testen. Wir erweitern die bestehende Literatur, indem wir zahlreiche Charakteristika identifizieren, die Risiko und Renditen von Kryptowährungen erklären können.

Suggested Citation

  • Steffen Günther & Christian Fieberg & Thorsten Poddig, 2020. "The Cross-Section of Cryptocurrency Risk and Return," Vierteljahrshefte zur Wirtschaftsforschung / Quarterly Journal of Economic Research, DIW Berlin, German Institute for Economic Research, vol. 89(4), pages 7-28.
    • Handle: RePEc:diw:diwvjh:89-4-2
    DOI: 10.3790/vjh.89.4.7
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    References listed on IDEAS

    as
    1. Zargar, Faisal Nazir & Kumar, Dilip, 2019. "Informational inefficiency of Bitcoin: A study based on high-frequency data," Research in International Business and Finance, Elsevier, vol. 47(C), pages 344-353.
    2. Chu, Jeffrey & Chan, Stephen & Zhang, Yuanyuan, 2020. "High frequency momentum trading with cryptocurrencies," Research in International Business and Finance, Elsevier, vol. 52(C).
    3. Yuanyuan Zhang & Stephen Chan & Jeffrey Chu & Hana Sulieman, 2020. "On the Market Efficiency and Liquidity of High-Frequency Cryptocurrencies in a Bull and Bear Market," JRFM, MDPI, vol. 13(1), pages 1-14, January.
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    More about this item

    Keywords

    Cryptocurrency; Cryptocurrency risk; Portfoliorendite;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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