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How does background risk affect portfolio choice: An analysis based on uncertain mean-variance model with background risk

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  • Huang, Xiaoxia
  • Yang, Tingting

Abstract

This paper explores how background risk affects individual investment decisions under the framework of uncertainty theory. We propose an uncertain mean-variance model with background risk and give its optimal solution when the returns of stocks and background asset obey normal uncertainty distributions. On this basis, we study the characteristic of the mean-variance efficient frontier of the stock portfolio in the presence of background risk. Furthermore, we compare the proposed model with the uncertain mean-variance model without background risk and discuss the efficiency difference between the two models and further give the condition where the two models select the same stocks. Based on the comparison, we analyze how background risk affects individual portfolio choice. Finally, we give some numerical examples as illustrations.

Suggested Citation

  • Huang, Xiaoxia & Yang, Tingting, 2020. "How does background risk affect portfolio choice: An analysis based on uncertain mean-variance model with background risk," Journal of Banking & Finance, Elsevier, vol. 111(C).
    • Handle: RePEc:eee:jbfina:v:111:y:2020:i:c:s0378426619302997
    DOI: 10.1016/j.jbankfin.2019.105726
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    Cited by:

    1. Huang, Xiaoxia & Ma, Di & Choe, Kwang-Il, 2023. "Uncertain mean–variance portfolio model with inflation taking linear uncertainty distributions," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 203-217.
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    3. Vergara, Marcos & Bonilla, Claudio A., 2021. "Precautionary saving in mean-variance models and different sources of risk," Economic Modelling, Elsevier, vol. 98(C), pages 280-289.
    4. Tingting Yang & Xiaoxia Huang, 2022. "A New Portfolio Optimization Model Under Tracking-Error Constraint with Linear Uncertainty Distributions," Journal of Optimization Theory and Applications, Springer, vol. 195(2), pages 723-747, November.
    5. Razvan Oprisor & Roy Kwon, 2020. "Multi-Period Portfolio Optimization with Investor Views under Regime Switching," JRFM, MDPI, vol. 14(1), pages 1-31, December.
    6. Huang, Chenchen & Luo, Di & Mukherjee, Soumyatanu & Mishra, Tapas, 2022. "To Acquire or to Ally? Managing Partners’ Environmental Risk in International Expansion," MPRA Paper 117591, University Library of Munich, Germany, revised 07 Jan 2023.
    7. Yang, Tingting & Huang, Xiaoxia, 2022. "Two new mean–variance enhanced index tracking models based on uncertainty theory," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    8. Ramesh Adhikari & Kyle J. Putnam & Humnath Panta, 2020. "Robust Optimization-Based Commodity Portfolio Performance," IJFS, MDPI, vol. 8(3), pages 1-16, September.

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    More about this item

    Keywords

    Uncertainty theory; Background risk; Mean-variance optimization; Portfolio selection;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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