Investing with cryptocurrencies – evaluating their potential for portfolio allocation strategies

Abstract

Cryptocurrencies (CCs) have risen rapidly in market capitalization over the past years. Despite striking volatility, their high average returns and low correlations have established CCs as alternative investment assets for portfolio and risk management. We investigate the benefits of adding CCs to well-diversified portfolios of conventional financial assets for different types of investors, including risk-averse, return-maximizing and diversification-seeking investors who may trade at different frequencies, namely, daily, weekly or monthly. We calculate out-of-sample performance and diversification benefits for the most popular portfolio-construction rules, including mean-variance optimization, risk-parity, and maximum-diversification strategies, as well as combined strategies. Our results demonstrate that CCs can improve the risk-return profile of portfolios, but their benefit depends on investor objectives. In particular, diversification strategies (maximizing the portfolio diversification index or equating risk contributions) draw appreciably on CCs and show, in line with spanning tests, CCs to be non-redundant extensions of the investment universe. However, when we introduce liquidity constraints via the LIBRO method to account for illiquidity of many CCs, out-of-sample performance drops considerably, while the diversification benefits persist. We conclude that the utility of CC investments strongly depends on investor characteristics.

Keywords:

• Cryptocurrency
• CRIX
• Investments
• Portfolio management
• Asset classes
• Blockchain
• Bitcoin
• Altcoins
• DLT

JEL Classification:

• C01
• C59
• G11

Acknowledgments

The authors wish to thank the editor, associate editor and two anonymous referees for their thoughtful comments and efforts towards improving the manuscript. They also gratefully acknowledge the comments and discussions from Jörg Osterrieder, Peter Schwendner, Li Guo and members of FG17 of the Weizenbaum-Institut as well as participants of the workshop Cryptocurrencies in a Digital Economy, 12th International Conference on Business Excellence, 3rd European COST Conference on Mathematics for Industry: Artificial Intelligence in Industry and Finance, CENTRAL workshop: Machine Learning in Economics and research seminar at the Dept of Statistics & Applied Probability (NUS).

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 We do not compare CCs to derivatives, as they clearly constitute underlyings—in fact, a common complaint, albeit ignoring their economic role, laments that, in the words of Alan Greenspan, ‘You have to really stretch your imagination to infer what the intrinsic value of Bitcoin is. I haven't been able to do it ’ (Kearns 2013). CC derivative markets, while growing, still remain quite nascent.

2 Platanakis and Urquhart (2019) do run a robustness test replacing Bitcoin with CRIX, acknowledging the importance of altcoins. Naturally, diversification across CCs necessitates an optimization including their individual, distinct return series.

3 This approach puts the emphasis on the equilibrium model and is thus often preferred by theorists.

4 An additional benefit is how it links potential empirical shortcomings to insufficiently captured statistical properties, offering remedy via more refined methods.

5 Markowitz (1952).

6 Sharpe (1964).

7 In fact, already Roll (1977) had stressed the ‘market portfolio’ ought to include all wealth. Naturally, his critique has led to innumerous suggestions for further asset classes that cannot all be part of our analysis, including private equity (Gompers et al. 2010), fine art (Mei and Moses 2002, Campbell 2008), or even fine wine (Fogarty 2010, Chu 2014).

8 More precisely, the intent was a protocol with the emphasis on the tokens' role as medium of exchange, not as stores of value.

9 Some debate centered on the question whether investments in CCs play an economic role similar to gold: See Dyhrberg (2016), Shahzad et al. (2019) for affirmative views, and Klein et al. (2018) for a dissenting one.

10 Generally, mainstream economics has joined the research effort on CCs deplorably late; it is now catching up, see for instance Schilling and Uhlig (2019), Abadi and Brunnermeier (2019). Game-theoretic modeling has been more active, including Houy (2016), Dimitri (2017), Caginalp and Caginalp (2019), Bolt and van Oordt (2020).

11 At the latest update of this writing, in 2021-Q1, the leading dedicated information platform coinmarketcap.com records more than 8000 CCs traded at more than 33,000 markets, totalling a market capitalization close to 1 trillion USD (almost two thirds of which are due to Bitcoin), with a 24-h trading volume surpassing 150 billion USD.

12 Note that the commonly reported thousands of CCs include mostly such with extremely low liquidity: As of 2020-04-28, only 10 CCs exhibit daily trading volumes exceeding 1 billion USD; volume below 100 000 USD exists already among the top 200 CCs.

13 Quick growth in the number of traded CCs was mostly driven by the free-software nature of Bitcoin, allowing forks, and to a lesser degree by development of new (sometimes blockchainless) CCs.

14 The reasons to consider CCs an asset class naturally go beyond the similarity of their return processes; the major reason is that their economic rationale differs decisively from all other asset classes, as they constitute the only means to provide real resources to decentralized apps.

15 We also test strategies on extending windows as in Trimborn et al. (2019); since the insights are similar, these results are not reported.

16 Technically, CC markets never close; the terminology ‘closing price’ is still used in reference to the last price of a day, where days are customary defined on UTC time.

17 We also run our entire analysis for a sample period extending until end of December 2017. For this shorter period, 55 CCs fulfill our criteria, and with minor exceptions only for combined strategies, all our results remain qualitatively unchanged.

18 As a robustness test, we also calculate with extending windows, where no historical data is dropped and only new observations added as they become observable. The results are qualitatively the same.

19 These results are robust for all Mean-Variance portfolio versions used as a benchmark: MV-S TrA, MinVar, MV-S and RR-MaxRet.

20 Here and henceforth we provide the values of the performance metric for LIBRO portfolios in parentheses.

21 Of course, in principle the efficient frontier is unique, thus identical for all allocation strategies. However, it depends on the risk measure (variance or CVaR in this paper), as well as on whether liquidity constraints are enforced (via LIBRO in this paper) or not.

Additional information

Funding

Financial support from IRTG 1792 ‘High Dimensional Non Stationary Time Series,’ Humboldt-Universität zu Berlin, Czech Science Foundation (Grantová Agentura České Republiky) under grant no. 19/28231X and NUS FRC grant R-146-000-298-114 ‘Augmented machine learning and network analysis with applications to cryptocurrencies and blockchains’ as well as CityU Start-up Grant 7200680 ‘Textual Analysis in Digital Markets’ is gratefully acknowledged. The work of the authors is receiving support from the European Union’s Horizon 2020 ‘FIN-TECH’ Project [grant number 825215] (Topic ICT-35-2018, Type of actions: CSA).