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Portfolio Optimization During the COVID-19 Epidemic: Based on an Improved QBAS Algorithm and a Dynamic Mixed Frequency Model

Author

Listed:
  • Siyao Wei

    (Southwest University of Science and Technology)

  • Pengfei Luo

    (Southwest University of Science and Technology)

  • Jiashan Song

    (Southwest University of Science and Technology)

  • Kunliang Jiang

    (Southwest University of Science and Technology)

Abstract

The determination of weights and the measurement of risk have been the core problems of portfolio optimization. In this paper, we propose the improved Quantum Beetle Antennae Search (IQBAS) algorithm for solve the first problem. Moreover, we use the GAS-MIDAS-Copula model to solve the second problem. Meanwhile, we combine both methods for portfolio optimization. Using a 5-min high-frequency returns covering ten sectors in the Shanghai Stock Exchange from September 1, 2019 to September 1, 2022, we find that the GAS-MIDAS-Copula model is very effective in describing the portfolio distribution and interdependence structure. Also, for different confidence levels and different optimization objectives, the IQBAS algorithm outperforms other popular optimization methods. In addition, when constructing a portfolio during the COVID-19 epidemic, China’s Medical industry should receive more weight, while China’s Information and Telecom industries should receive less. Our findings are informative on how to better invest during major public health emergencies.

Suggested Citation

  • Siyao Wei & Pengfei Luo & Jiashan Song & Kunliang Jiang, 2025. "Portfolio Optimization During the COVID-19 Epidemic: Based on an Improved QBAS Algorithm and a Dynamic Mixed Frequency Model," Computational Economics, Springer;Society for Computational Economics, vol. 65(4), pages 1999-2028, April.
    • Handle: RePEc:kap:compec:v:65:y:2025:i:4:d:10.1007_s10614-024-10621-5
    DOI: 10.1007/s10614-024-10621-5
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