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Investing with Cryptocurrencies -- evaluating their potential for portfolio allocation strategies

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  • Alla Petukhina
  • Simon Trimborn
  • Wolfgang Karl Hardle
  • Hermann Elendner

Abstract

Cryptocurrencies (CCs) have risen rapidly in market capitalization over the last years. Despite striking price volatility, their high average returns have drawn attention to CCs as alternative investment assets for portfolio and risk management. We investigate the utility gains for different types of investors when they consider cryptocurrencies as an addition to their portfolio of traditional assets. We consider risk-averse, return-seeking as well as diversificationpreferring investors who trade along different allocation frequencies, namely daily, weekly or monthly. 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 low liquidity in CC markets, we incorporate liquidity constraints via the LIBRO method. Our results show that CCs can improve the risk-return profile of portfolios. In particular, a maximum-diversification strategy (maximizing the Portfolio Diversification Index, PDI) draws appreciably on CCs, and spanning tests clearly indicate that CC returns are non-redundant additions to the investment universe. Though our analysis also shows that illiquidity of CCs potentially reverses the results.

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  • Alla Petukhina & Simon Trimborn & Wolfgang Karl Hardle & Hermann Elendner, 2020. "Investing with Cryptocurrencies -- evaluating their potential for portfolio allocation strategies," Papers 2009.04461, arXiv.org, revised Sep 2020.
  • Handle: RePEc:arx:papers:2009.04461
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    8. 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.
    9. Steven Y. K. Wong & Jennifer S. K. Chan & Lamiae Azizi, 2024. "Quantifying neural network uncertainty under volatility clustering," Papers 2402.14476, arXiv.org.
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    11. Almeida, José & Gonçalves, Tiago Cruz, 2023. "A systematic literature review of investor behavior in the cryptocurrency markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
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    15. Luis Lorenzo & Javier Arroyo, 2023. "Online risk-based portfolio allocation on subsets of crypto assets applying a prototype-based clustering algorithm," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-40, December.
    16. Konstantin Gorgen & Jonas Meirer & Melanie Schienle, 2022. "Predicting Value at Risk for Cryptocurrencies With Generalized Random Forests," Papers 2203.08224, arXiv.org, revised Jun 2022.
    17. Ren, Rui & Althof, Michael & Härdle, Wolfgang Karl, 2020. "Tail Risk Network Effects in the Cryptocurrency Market during the COVID-19 Crisis," IRTG 1792 Discussion Papers 2020-028, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
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    19. Gerritsen, Dirk F. & Lugtigheid, Rick A.C. & Walther, Thomas, 2022. "Can Bitcoin Investors Profit from Predictions by Crypto Experts?," Finance Research Letters, Elsevier, vol. 46(PA).

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