The diversification benefits of cryptocurrency asset categories and estimation risk: pre and post Covid-19

Xinyu Huang, Weihao Han, David Newton* (Corresponding Author), Emmanouil Platanakis, Dimitrios Stafylas, Charles Sutcliffe

*Corresponding author for this work

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We examine the diversification benefits of cryptocurrency asset categories. To mitigate the effects of estimation risk, we employ the Bayes-Stein model with no short-selling and variance-based constraints. We estimate the inputs using lasso regression and elastic net regression, employing the shrunk Wishart stochastic volatility model and Gaussian random projection. We consider nine cryptocurrency asset categories, and find that all but two provide significant out-of-sample diversification benefits. The lower is investor risk aversion, the more beneficial are cryptocurrencies as portfolio diversifiers. During uncertain economic environments, such as the post-Covid-19 period, cryptocurrencies provide the same diversification benefits as in more stable environments. Our results are robust to different portfolio benchmarks, regression technique, transaction cost, portfolio constraints, higher moments and Black–Litterman models.
Original languageEnglish
Pages (from-to)800-825
Number of pages26
JournalEuropean Journal of Finance
Issue number7
Early online date5 Mar 2022
Publication statusPublished - May 2023

Bibliographical note

We wish to thank the anonymous reviewers and the editor of this journal for their valuable comments on earlier versions of this paper as well as participants at the Cryptocurrency Research Conference 2021 (Reading, UK) and the National Conference of the Financial Engineering and Banking Society (Athens, Greece).


  • Cryptocurrencies
  • Covid-19
  • diversification
  • machine learning
  • portfolio management


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