Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/287583 
Year of Publication: 
2021
Citation: 
[Journal:] Digital Finance [ISSN:] 2524-6186 [Volume:] 3 [Issue:] 1 [Publisher:] Springer International Publishing [Place:] Cham [Year:] 2021 [Pages:] 45-79
Publisher: 
Springer International Publishing, Cham
Abstract: 
The drastic growth of the cryptocurrencies market capitalization boosts investigation of their diversification benefits in portfolio construction. In this paper with a set of classical and modern measurement tools, we assess the out-of-sample performance of eight portfolio allocation strategies relative to the naive 1/N rule applied to traditional and crypto-assets investment universe. Evaluated strategies include a range from classical Markowitz rule to the recently introduced LIBRO approach (Trimborn et al. in Journal of Financial Econometrics 1–27, 2019). Furthermore, we also compare three extensions for strategies with respect to input estimators applied. The results show that in the presence of alternative assets, such as cryptocurrencies, mean–variance strategies underperform the benchmark portfolio. In contrast, CVaR optimization tends to outperform the benchmark as well as geometric optimization, although we find a strong dependence of the former's success on trading costs. Furthermore, we find evidence that liquidity-bounded strategies tend to perform very well. Thus, our findings underscore the non-normal distribution of returns and the necessity to control for liquidity constraints at alternative asset markets.
Subjects: 
Portfolio management
Asset allocation
Investments
Alternative assets
Bitcoin
Cryptocurrencies
LIBRO
JEL: 
C01
C58
G11
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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