Investing with cryptocurrencies - evaluating the potential of portfolio allocation strategies

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: IRTG 1792 Discussion Paper ; No. 2018-058

Classification and Topics

• Classification: Wirtschaft ; Econometrics ; Financial Econometrics ; Portfolio Choice; Investment Decisions
• Subject: cryptocurrency ; CRIX ; investments ; portfolio management ; asset classes ; blockchain ; Bitcoin ; altcoins ; DLT

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Petukhina, Alla ; Trimborn, Simon ; Härdle, Wolfgang Karl ; Elendner, Hermann
• Event: Veröffentlichung
• (who): Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
• (where): Berlin
• (when): 2018

Further information

• Handle: http://hdl.handle.net/10419/230769
• Last update: 10.03.2025, 11:43 AM CET