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A differential evolution copula-based approach for a multi-period cryptocurrency portfolio optimization

Author

Listed:
  • Jules Clement Mba

    (University of Johannesburg)

  • Edson Pindza

    (University of Pretoria
    Achieversklub School of Cryptocurrency and Entrepreneurship)

  • Ur Koumba

    (University of Johannesburg)

Abstract

Recent years have seen a growing interest among investors in the new technology of blockchain and cryptocurrencies and some early investors in this new type of digital assets have made significant gains. The heuristic algorithm, differential evolution, has been advocated as a powerful tool in portfolio optimization. We propose in this study two new approaches derived from the traditional differential evolution (DE) method: the GARCH-differential evolution (GARCH-DE) and the GARCH-differential evolution t-copula (GARCH-DE-t-copula). We then contrast these two models with DE (benchmark) in single and multi-period optimizations on a portfolio consisting of five cryptoassets under the coherent risk measure CVaR constraint. Our analysis shows that the GARCH-DE-t-copula outperforms the DE and GARCH-DE approaches in both single- and multi-period frameworks. For these notoriously volatile assets, the GARCH-DE-t-copula has shown risk-control ability, hereby confirming the ability of t-copula to capture the dependence structure in the fat tail.

Suggested Citation

  • Jules Clement Mba & Edson Pindza & Ur Koumba, 2018. "A differential evolution copula-based approach for a multi-period cryptocurrency portfolio optimization," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 32(4), pages 399-418, November.
    • Handle: RePEc:kap:fmktpm:v:32:y:2018:i:4:d:10.1007_s11408-018-0320-9
    DOI: 10.1007/s11408-018-0320-9
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    Cited by:

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    3. Jules Clement Mba & Sutene Mwambi, 2020. "A Markov-switching COGARCH approach to cryptocurrency portfolio selection and optimization," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 34(2), pages 199-214, June.
    4. Cheng, Jiyang & Tiwari, Sunil & Khaled, Djebbouri & Mahendru, Mandeep & Shahzad, Umer, 2024. "Forecasting Bitcoin prices using artificial intelligence: Combination of ML, SARIMA, and Facebook Prophet models," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    5. Mario I. Contreras-Valdez & José Antonio Núñez & Guillermo Benavides Perales, 2022. "Bitcoin in Portfolio Selection: A Multivariate Distribution Approach," SAGE Open, , vol. 12(2), pages 21582440221, May.
    6. Osman, Myriam Ben & Galariotis, Emilios & Guesmi, Khaled & Hamdi, Haykel & Naoui, Kamel, 2023. "Diversification in financial and crypto markets," International Review of Financial Analysis, Elsevier, vol. 89(C).
    7. Jules Clément Mba & Magdaline Mbong Mai, 2022. "A Particle Swarm Optimization Copula-Based Approach with Application to Cryptocurrency Portfolio Optimisation," JRFM, MDPI, vol. 15(7), pages 1-14, June.

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    More about this item

    Keywords

    Cryptocurrencies; GARCH; Differential evolution; t-copula; CVaR; Portfolio optimization;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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