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A Particle Swarm Optimization Copula-Based Approach with Application to Cryptocurrency Portfolio Optimisation

Author

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  • Jules Clément Mba

    (School of Economics, College of Business and Economics, University of Johannesburg, P.O. Box 524, Auckland Park, Johannesburg 2006, South Africa)

  • Magdaline Mbong Mai

    (Cultural Studies and Applied Linguistics (LanCSAL), University of Johannesburg, Languages, P.O. Box 524, Auckland Park, Johannesburg 2006, South Africa)

Abstract

Blockchain and cryptocurrency are gradually going mainstream with new cryptocurrencies introduced every single day. The speculative nature of these digital assets expose their prices to large fluctuations. Trading these crypto-assets necessitate an adequate understanding of this emerging market as well as adequate tools to model the market risk and efficient allocation of funds. This may assist crypto investors in taking advantage of the highly volatile aspects of these assets. The portfolio consider in this study consists of six cryptocurrencies: four traditional cryptocurrencies (BTC, ETH, BNB and XRP) and two stablecoins (USDT and USDC). We examine the copula particle swarm optimization (CPSO) portfolio strategy against three other portfolio strategies, namely, the global minimum variance (GMV), the most diversified portfolio (MDP) and the minimum tail dependent (MTD). CPSO appears to be a promising strategy during extreme market conditions while GMV seem favorable during normal market conditions. Most importantly, hedge and safe-havens ability of the two stablecoins is clearly exhibited with CPSO, while their diversification property is inhibited.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:7:p:285-:d:849511
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    References listed on IDEAS

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