Optimization and Diversification of Cryptocurrency Portfolios: A Composite Copula-Based Approach
Source abstract
Abstract
This paper focuses on the selection and optimisation of a cryptoasset portfolio, using the K-means clustering algorithm and GARCH C-Vine copula model combined with the differential evolution algorithm. This integrated approach allows the construction of a diversified portfolio of eight cryptocurrencies and determines an optimal allocation strategy making it possible to minimize the conditional value-at-risk of the portfolio and maximise the return. Our results show that stablecoins such as True-USD are negatively correlated to the other cryptoassets in the portfolio and could therefore be a safe haven for crypto-investors during market turmoil. Our findings are in line with previous studies exhibiting stablecoins as potential diversifiers.
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Research context
OpenAlex topics: Market Dynamics and Volatility · Financial Markets and Investment Strategies · Financial Risk and Volatility Modeling
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Affiliations
Department of Mathematics and Applied Mathematics, North West University, P.O. Box 209, Potchefstroom 2520, South Africa
School of Economics, University of Johannesburg, P.O. Box 524, Auckland Park 2006, South Africa
References (22)
Show source-provided references
1. Portfolio selection
2. Markowitz, H. (1959). Portfolio Selection: Efficient Diversification of Investments, John Wiley and Sons.
3. Bares, P., Gibson, R., and Gyger, S. (2002). Hedge Fund Allocation with Survival Uncertainty and Investment Constraints. Soc. Sci. Res. Netw., 1–50.10.2139/ssrn.297859 ↗
4. Higher order moments of the estimated tangency portfolio weights10.1080/02664763.2020.1736523 ↗
5. Khan, K.I., Naqvi, S.M.W.A., Ghafoor, M.M., and Akash, R.S.I. (2020). Sustainable Portfolio Optimization with Higher-Order Moments of Risk. Sustainability, 12.10.3390/su12052006 ↗
6. Jurczenko, E., Maillet, B., and Merlin, P. (2005). Hedge Funds Portfolio Selection with Higher-Order Moments: A Non-Parametric Mean-Variance-Skewness-Kurtosis Efficient Frontier, Social Science Research Network (SSRN).10.2139/ssrn.676904 ↗
7. Hunjra, A.I., Alawi, S.M., Colombage, S., Sahito, U., and Hanif, M. (2020). Portfolio Construction by Using Different Risk Models: A Comparison among Diverse Economic Scenarios. Risks, 8.10.3390/risks8040126 ↗
8. Portfolio optimization with conditional value-at-risk objective and constraints10.21314/JOR.2002.057 ↗
9. Risks and portfolio decisions involving hedge funds10.1093/rfs/hhg044 ↗
10. Multi-objective portfolio optimization considering the dependence structure of asset returns10.1016/j.ejor.2015.01.025 ↗
11. A differential evolution copula-based approach for a multi-period cryptocurrency portfolio optimization10.1007/s11408-018-0320-9 ↗
12. Vine copula-based dependence and portfolio value-at risk analysis of the cryptocurrency market10.1016/j.inteco.2019.03.002 ↗
13. A Marvov-Switching COGARCH approach to cryptocurrency portfolio selection and optimization10.1007/s11408-020-00346-4 ↗
14. Functions de Répartition a n dimensions et leurs Marges
15. Joe, H. (1997). Multivariate Models and Dependence Concepts, Chapman & Hall.10.1201/9780367803896 ↗
16. Probability density decomposition for conditionally dependent random variables modeled by vines10.1023/A:1016725902970 ↗
17. Vines—A new graphical model for dependent random variables10.1214/aos/1031689016 ↗
18. On the relation between the expected value and the volatility of the nominal excess return on stocks10.1111/j.1540-6261.1993.tb05128.x ↗
19. Storn, R., and Price, K. (1995). Differential Evolution—A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces, Intemational Computer Science Institute (ICSI). TR-95-012.
20. Optimisation of mixed assets portfolio using copula differential evolution: A Behavioural approach10.1080/23322039.2020.1780838 ↗
21. Das, M., Roy, A., Maity, S., Kar, S., and Sengupta, S. (2021). Solving fuzzy dynamic ship routing and scheduling problem through new genetic algorithm. Decis. Making Appl. Manag. Eng.
22. Optimization of conditional value-at-risk10.21314/JOR.2000.038 ↗