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Dynamic Portfolio Optimization with Diversification Analysis and Asset Selection Amidst High Correlation Using Cryptocurrencies and Bank Equities

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  • Hamdan Bukenya Ntare

    (School of Economics and Econometrics, University of Johannesburg, Auckland Park 2092, South Africa)

  • John Weirstrass Muteba Mwamba

    (School of Economics and Econometrics, University of Johannesburg, Auckland Park 2092, South Africa)

  • Franck Adekambi

    (School of Economics and Econometrics, University of Johannesburg, Auckland Park 2092, South Africa)

Abstract

There has been growing interest among investors to include cryptocurrencies in their portfolios because of their diversification potential. However, the diversification role of cryptocurrencies when added to South African bank equities is yet to be determined. This study rigorously evaluates asset co-movement and diversification benefits of integrating cryptocurrencies into South African bank equity portfolios. Using advanced financial engineering techniques, including multi-asset particle swarm optimizer (MA-PSO), random optimizer, and a static equal-weighted portfolio (EWP) model, this study analyzed the dynamic portfolio performance and diversification of cryptocurrencies in the 2017–2024 period. The portfolio performance of the three methods is also compared with the results from the traditional one-period mean–variance optimization (MVO) method. The findings underscore the superiority of dynamic models over static EWP in assessing the impact of cryptocurrency inclusion in bank equity portfolios. While pre-COVID-19 studies identified cryptocurrencies as effective hedges against market downturns, this protective role appears attenuated in the post-COVID-19 era. The dynamic MA-PSO model emerges as the optimal approach, delivering better-diversified portfolios. Consequently, South African portfolio managers must carefully evaluate investor risk tolerance before incorporating cryptocurrencies, with regulators imposing stringent guidelines to mitigate potential losses.

Suggested Citation

  • Hamdan Bukenya Ntare & John Weirstrass Muteba Mwamba & Franck Adekambi, 2025. "Dynamic Portfolio Optimization with Diversification Analysis and Asset Selection Amidst High Correlation Using Cryptocurrencies and Bank Equities," Risks, MDPI, vol. 13(6), pages 1-21, June.
    • Handle: RePEc:gam:jrisks:v:13:y:2025:i:6:p:113-:d:1680148
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    References listed on IDEAS

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    1. Zdenek Smutny & Zdenek Sulc & Jan Lansky, 2021. "Motivations, Barriers and Risk-Taking When Investing in Cryptocurrencies," Mathematics, MDPI, vol. 9(14), pages 1-22, July.
    2. Leong, Minhao & Alexeev, Vitali & Kwok, Simon, 2025. "Managing cryptocurrency risk exposures in equity portfolios: Evidence from high-frequency data," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 99(C).
    3. Muhammad Mohsin & Li Naiwen & Muhammad Zia-UR-Rehman & Sobia Naseem & Sajjad Ahmad Baig, 2020. "The volatility of bank stock prices and macroeconomic fundamentals in the Pakistani context: an application of GARCH and EGARCH models," Oeconomia Copernicana, Institute of Economic Research, vol. 11(4), pages 609-636, December.
    4. Anlan Wang & Aleš Kresta & Tomáš Tichý, 2024. "Evaluation of strategy portfolios," Computational Management Science, Springer, vol. 21(1), pages 1-27, June.
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