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Portfolio optimization under mean-CVaR simulation with copulas on the Vietnamese stock exchange

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  • Le, Tuan Anh
  • Dao, Thi Thanh Binh

Abstract

This paper studies how to construct and compare various optimal portfolio frame-works for investors in the context of the Vietnamese stock market. The aim of the study is to help investors to find solutions for constructing an optimal portfolio strategy using modern investment frameworks in the Vietnamese stock market. The study contains a census of the top 43 companies listed on the Ho Chi Minh stock exchange (HOSE) over the ten-year period from July 2010 to January 2021. Optimal portfolios are constructed using Mean-Variance Framework, Mean-CVaR Framework under different copula simulations. Two-thirds of the data from 26/03/2014 to 27/1/2021 consists of the data of Vietnamese stocks during the COVID-19 recession, which caused depression globally; however, the results obtained during this period still provide a consistent outcome with the results for other periods. Furthermore, by randomly attempting different stocks in the research sample, the results also perform the same outcome as previous analyses. At about the same CvaR level of about 2.1%, for example, the Gaussian copula portfolio has daily Mean Return of 0.121%, the t copula portfolio has 0.12% Mean Return, while Mean-CvaR with the Raw Return portfolio has a lower Return at 0.103%, and the last portfolio of Mean-Variance with Raw Return has 0.102% Mean Return. Empirical results for all 10 portfolio levels showed that CVaR copula simulations significantly outperform the historical Mean-CVaR framework and Mean-Variance framework in the context of the Vietnamese stock exchange.

Suggested Citation

  • Le, Tuan Anh & Dao, Thi Thanh Binh, 2021. "Portfolio optimization under mean-CVaR simulation with copulas on the Vietnamese stock exchange," MPRA Paper 111105, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:111105
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    File URL: https://mpra.ub.uni-muenchen.de/111105/1/MPRA_paper_111105.pdf
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    References listed on IDEAS

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    1. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    2. Chirag Shekhar & Mark Trede, 2017. "Portfolio Optimization Using Multivariate t-Copulas with Conditionally Skewed Margins," Review of Economics & Finance, Better Advances Press, Canada, vol. 9, pages 29-41, August.
    3. Andrew Chen & Frank Fabozzi & Dashan Huang, 2012. "Portfolio revision under mean-variance and mean-CVaR with transaction costs," Review of Quantitative Finance and Accounting, Springer, vol. 39(4), pages 509-526, November.
    4. Houda Hafsa, 2015. "CVaR in Portfolio Optimization: An Essay on the French Market," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 6(2), pages 101-111, April.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Gaussian copula; t copula; simulation; Mean-CVaR; Mean-Variance; portfolio optimization; Vietnam;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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