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Investing with cryptocurrencies - evaluating the potential of portfolio allocation strategies

Author

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  • Petukhina, Alla
  • Trimborn, Simon
  • Härdle, Wolfgang Karl
  • Elendner, Hermann

Abstract

The market capitalization of cryptocurrencies has risen rapidly during the last few years. Despite their high volatility, this fact has spurred growing interest in cryptocurrencies as an alternative investment asset for portfolio and risk management. We characterise the effects of adding cryptocurrencies in addition to traditional assets to the set of eligible assets in portfolio management. Out-of-sample performance and diversification benefits are studied for the most popular portfolio-construction rules, including mean-variance optimization, risk-parity, and maximum-diversification strategies, as well as combined strategies. To account for the frequently low liquidity of cryptocurrency markets we incorporate the LIBRO method, which gives suitable liquidity constraints. Our results show that cryptocurrencies can improve the risk-return profile of portfolios. In particular, cryptocurrencies are more useful for portfolio strategies with higher target returns; they do not play a role in minimum-variance portfolios. However, a maximum-diversification strategy (maximising the Portfolio Diversification Index, PDI) draws appreciably on cryptocurrencies, and spanning tests clearly indicate that cryptocurrency returns are non-redundant additions to the investment universe.

Suggested Citation

  • Petukhina, Alla & Trimborn, Simon & Härdle, Wolfgang Karl & Elendner, Hermann, 2018. "Investing with cryptocurrencies - evaluating the potential of portfolio allocation strategies," IRTG 1792 Discussion Papers 2018-058, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    • Handle: RePEc:zbw:irtgdp:2018058
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Tomić, Bojan, 2020. "BITCOIN: Systematic Force of Cryptocurrency Portfolio," MPRA Paper 101290, University Library of Munich, Germany, revised 26 May 2020.
    2. Alla A. Petukhina & Raphael C. G. Reule & Wolfgang Karl Härdle, 2021. "Rise of the machines? Intraday high-frequency trading patterns of cryptocurrencies," The European Journal of Finance, Taylor & Francis Journals, vol. 27(1-2), pages 8-30, January.
    3. Yatracos, Yannis G., 2018. "Residual'S Influence Index (Rinfin), Bad Leverage And Unmasking In High Dimensional L2-Regression," IRTG 1792 Discussion Papers 2018-060, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    4. Ma, Yechi & Ahmad, Ferhana & Liu, Miao & Wang, Zilong, 2020. "Portfolio optimization in the era of digital financialization using cryptocurrencies," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    5. Wolfgang Karl Hardle & Campbell R. Harvey & Raphael C. G. Reule, 2020. "Editorial: Understanding Cryptocurrencies," Papers 2007.14702, arXiv.org.
    6. Walther, Thomas & Klein, Tony & Bouri, Elie, 2019. "Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 63(C).
    7. Nils Bundi & Marc Wildi, 2019. "Bitcoin and market-(in)efficiency: a systematic time series approach," Digital Finance, Springer, vol. 1(1), pages 47-65, November.
    8. Nasekin, Sergey & Chen, Cathy Yi-Hsuan, 2018. "Deep learning-based cryptocurrency sentiment construction," IRTG 1792 Discussion Papers 2018-066, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

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    More about this item

    Keywords

    cryptocurrency; CRIX; investments; portfolio management; asset classes; blockchain; Bitcoin; altcoins; DLT;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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