Cryptocurrencies as an asset class in portfolio optimisation

Olha Holovatiuk

Open Access

Cryptocurrencies as an asset class in portfolio optimisation

Olha Holovatiuk

| Sep 09, 2020

Central European Economic Journal

Volume 7 (2020): Issue 54 (January 2020)

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Page range: 33 - 55

DOI: https://doi.org/10.2478/ceej-2020-0004

Keywords
cryptocurrencies, blockchain technology, asset class, portfolio optimisation, Modern Portfolio Theory, Post-Modern Portfolio Theory

© 2020 Olha Holovatiuk, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Downside risk on the bell curve. Source: Rollinge and Hoffman (2013).

Correlation matrices of cryptocurrencies based on Pearson's correlation coefficient. Source: Own work, computed in R.

Correlation matrix between returns of the asset classes based on Spearman's coefficient, for the period from August 2014 to July 2019. Source: Own work, computed in R.

Daily risk–return profiles of the asset classes. Source: Own work, computed in R.

Efficient frontier of portfolios with inclusion of cryptocurrencies, only long positions allowed versus long and short positions allowed. Source: Own work, computed in R.

Minimum-variance portfolio optimal weights with inclusion of cryptocurrencies, only long positions allowed versus long and short positions allowed. Source: Own work, computed in R.

Weights of portfolios of efficient frontier with inclusion of cryptocurrencies, only long positions allowed versus long and short positions allowed. Source: Own work, computed in R.

Tangency portfolio optimal weights with inclusion of cryptocurrencies, only long positions allowed versus long and short positions allowed. Source: Own work, computed in R.

Risk–return profiles of the asset classes, for the period from August 2014 to July 2019

Risk-return measuremets	CRIX	Stocks	Bonds	Commodities	FX	Real estate
Annualised return	0.0816	0.0828	0.0017	–0.0977	0.0295	0.0373

Annualised standard deviation	0.6551	0.1342	0.0323	0.1281	0.0453	0.1408

Annualised Sharpe ratio (Rf=0%)	0.1245	0.6172	0.0516	–0.7625	0.6511	0.2649
Maximum DD	0.4519	0.0801	0.0162	0.0573	0.0276	0.0702

Descriptive statistics of the asset's daily returns, for the period from August 2014 to July 2019

Asset class	Mean	SD	Median	MAD	Maximum	Minimum	Range	Skew	Kurtosis
CRIX	0.00119	0.04127	0.00241	0.02220	−0.25334	0.19854	0.45188	−0.73932	6.06653

Stocks	0.00035	0.00845	0.00042	0.00544	−0.04184	0.04840	0.09025	−0.44359	3.74452

Bonds	0.00001	0.00203	0.00012	0.00188	−0.00994	0.00693	0.01686	−0.36463	1.01629

Commodities	−0.00038	0.00807	−0.00014	0.00722	−0.03945	0.02989	0.06934	−0.11117	1.02663

FX	0.00012	0.00286	0.00013	0.00257	−0.01184	0.01743	0.02927	0.00864	2.00035
Real estate	0.00018	0.00887	0.00061	0.00737	−0.04703	0.03393	0.08097	−0.57110	2.05658

Asset classes and their proxies

Asset class	Proxy	Ticker	Details
Stocks	S&P500	^GSPC	The index represents stocks of 500 of the largest US companies.

Bonds	Vanguard Total Bond Market Index ETF	BND	ETF follows the Bloomberg Barclays US Aggregate Float Adjusted Index, which comprises corporate, government, international bonds, as well as mortgage- and asset-backed securities.

Foreign exchange	Dow Jones FXCM Dollar Index	USDOLLAR	The index tracks the performance of foreign exchange (FX) trading activity based on appreciation and depreciation of the dollar relative to EUR, GBP, AUD and JPY.

Commodities	Bloomberg Commodity Index	BCOM	The index reflects the changes in commodity futures prices. It contains 27 of the most significant and liquid commodities, including gold, silver, oil, gas, wheat, corn and so on.
Real estate	Dow Jones Real Estate Index	DJUSRE	The index reflects the performance of the real estate industry. It captures segments of the US market with large, medium and small capitalisation.

Transaction fees on top cryptocurrency exchanges

Exchange	Trading Fees			Funding Fees		Discounts

	Maker	Taker	Spread	Deposits	Withdrawals	Exchange Token Discount	Volume Discount
Bibox	0.1%	0.1%	No	No	Yes	Yes	No

Binance	0.1%	0.1%	No	No	Yes	Yes	Yes

Bitfinex	0.1%	0.2%	No	Yes (<$1k)	Yes	No	Yes

Bitsane	0.1%	0.2%	No	Yes	Yes	No	Yes

Bitstamp	0.25%	0.25%	No	No	No	No	Yes

Bittrex	0.25%	0.25%	No	No	No	No	No

BTCMarkets	0.22%–0.85%	0.22%–0.85%	No	No	Yes (AUD free)	No	Yes

CEX.IO	0.16%	0.25%	No	No	Yes	No	Yes

Coinbase	N/A	1.49% or fixed fee	0.5% fiat 1.00% crypto	No	No	No	Yes

Coinbase Pro	0.15%	0.25%	No	No	No	No	Yes

CoinSpot	0.1%	0.1%	No	Yes	No	No	No

Coss	0.14%	0.2%	No	Yes	Yes	Yes	Yes

Cryptopia	0.2%	0.2%	No	No	No	No	No

Gate.io	0.2%	0.2%	No	No	Yes	No	Yes

Gemini	1.00%	1.00%	No	No	No	No	Yes

HitBTC	0.1%	0.2%	No	No	No	No	No

Huboi	0.2%	0.2%	No	No	No	Yes	Yes

IDEX	0.1%	0.2%	No	No	No	Yes	No

Kraken	0.16%	0.26%	No	No	No	No	Yes

Kucoin	0.1%	0.1%	No	No	No	No	Yes

Livecoin	0.18%	0.18%	No	Yes	Yes	No	Yes

Liquid	0.1%	0.1%	No	No	Yes	Yes	Yes

Poloniex	0.08%	0.2%	No	No	Yes	No	Yes

Shakepay	0.75%	0.75%	No	No	Yes	No	No
Uphold	0.65%–1.95%	0.65%–1.95%	No	No	Yes	No	No

Spread percentage, turnover and close ratio of the top cryptocurrencies with the highest market capitalisation (average over the period from August 2014 to July 2019)

Cryptocurrency	Spread percentage [%]	Turnover ratio	Close ratio
BTC	4.0992	0.0952	0.5276

ETH	5.8820	0.2185	0.4906

XRP	6.0555	0.0577	0.4726

LTC	6.4739	0.3513	0.4966

BCH	7.8009	0.1525	0.4820

BNB	6.3621	0.0548	0.5439

EOS	6.7004	0.3292	0.5277

BSV	8.9249	0.1136	0.4495

TRX	0.8114	0.1802	0.4890
Total market	-	0.1649	-

Consolidated results of portfolio optimisation

	Annual return	Annual standard deviation	Annual Sharpe ratio	Maximum DD	Asset allocation (weights)

					CRIX	Stocks	Bonds	Commodities	FX	Real estate
Portfolio without cryptocurrencies, only long position allowed

MinVar	0.0102	0.0202	0.5057	0.1263	–	0.0421	0.5619	0.0534	0.3427	0.0000

Tangency	0.0291	0.0280	1.0372	0.1565	–	0.1577	0.3673	0.0000	0.4749	0.0000

Portfolio without cryptocurrencies, long and short positions allowed

MinVar	0.0108	0.0197	0.5471	0.1320	–	0.0694	0.5932	0.0491	0.3286	–0.0403

Tangency	0.0632	0.0478	1.3226	0.2300	–	0.3343	0.5519	–0.2440	0.4459	–0.0881

Portfolio with inclusion of cryptocurrencies, only long position allowed

MinVar	0.0105	0.0202	0.5212	0.3295	0.0010	0.0421	0.5617	0.0530	0.3423	0.0000

Tangency	0.0339	0.0298	1.1371	0.3366	0.0187	0.1528	0.3637	0.0000	0.4648	0.0000

Portfolio with inclusion of cryptocurrencies, long and short positions allowed

MinVar	0.0111	0.0198	0.5624	0.3356	0.0009	0.0694	0.5930	0.0487	0.3283	–0.0403
Tangency	0.0707	0.0499	1.4189	0.4163	0.0276	0.3271	0.5453	–0.2465	0.4322	–0.0858

Portfolio performance analysis within the framework of PMPT

	Annual return	Maximum DD	Sharpe ratio	Sortino ratio	Downside volatility (%)
Portfolio without cryptocurrencies, only long position allowed

MinVar	0.0102	0.1263	0.5057	0.7910	5.63

Tangency	0.0291	0.1565	1.0372	1.3989	5.26

Portfolio without cryptocurrencies, long and short positions allowed

MinVar	0.0108	0.1320	0.5471	0.8761	5.78

Tangency	0.0632	0.2300	1.3226	1.9041	6.71

Portfolio with inclusion of cryptocurrencies, only long position allowed

MinVar	0.0105	0.3295	0.5212	0.7916	12.92

Tangency	0.0339	0.3366	1.1371	0.7396	12.73

Portfolio with inclusion of cryptocurrencies, long and short positions allowed

MinVar	0.0111	0.3356	0.5625	0.8744	13.26
Tangency	0.0707	0.4163	1.4189	1.0839	15.99

eISSN:: 2543-6821
Language:: English

Publication timeframe:: Volume Open
Journal Subjects:: Business and Economics, Political Economics, Economic Theory, Systems and Structures, Microeconomics, Macroecomics, Economic Policy

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Cryptocurrencies as an asset class in portfolio optimisation

Published Online: Sep 09, 2020

Page range: 33 - 55

DOI: https://doi.org/10.2478/ceej-2020-0004

Keywords
cryptocurrencies, blockchain technology, asset class, portfolio optimisation, Modern Portfolio Theory, Post-Modern Portfolio Theory

© 2020 Olha Holovatiuk, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Risk–return profiles of the asset classes, for the period from August 2014 to July 2019

Descriptive statistics of the asset's daily returns, for the period from August 2014 to July 2019

Asset classes and their proxies

Transaction fees on top cryptocurrency exchanges

Spread percentage, turnover and close ratio of the top cryptocurrencies with the highest market capitalisation (average over the period from August 2014 to July 2019)

Consolidated results of portfolio optimisation

Portfolio performance analysis within the framework of PMPT

Cryptocurrencies as an asset class in portfolio optimisation

Published Online: Sep 09, 2020

Page range: 33 - 55

DOI: https://doi.org/10.2478/ceej-2020-0004

Keywordscryptocurrencies, blockchain technology, asset class, portfolio optimisation, Modern Portfolio Theory, Post-Modern Portfolio Theory

© 2020 Olha Holovatiuk, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Risk–return profiles of the asset classes, for the period from August 2014 to July 2019

Descriptive statistics of the asset's daily returns, for the period from August 2014 to July 2019

Asset classes and their proxies

Transaction fees on top cryptocurrency exchanges

Spread percentage, turnover and close ratio of the top cryptocurrencies with the highest market capitalisation (average over the period from August 2014 to July 2019)

Consolidated results of portfolio optimisation

Portfolio performance analysis within the framework of PMPT

Keywords
cryptocurrencies, blockchain technology, asset class, portfolio optimisation, Modern Portfolio Theory, Post-Modern Portfolio Theory