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Portfolio performance evaluation in Mean-CVaR framework: A comparison with non-parametric methods value at risk in Mean-VaR analysis

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  • Banihashemi, Shokoofeh
  • Navidi, Sarah

Abstract

As we know, there is a belief in the finance literature that Value at Risk (VaR) and Conditional Value at Risk (CVaR) are new approaches to manage and control the risk. Regard to, value at risk is not a coherent risk measure and it is not sub-additive and convex, so, we have considered conditional value at risk as a risk measure by different confidence level in the Mean-CVaR and multi objective proportional change Mean-CVaR models and compared these models with our previous mean-VaR models. This paper focuses on the performance evaluation process and portfolios selection by using Data Envelopment Analysis (DEA). Conventional DEA models assume non-negative values for inputs and outputs, but many of data take the negative value. Therefore, we have used our models based on Range Directional Measure (RDM) that can take positive and negative values. Here value at risk is obtained by non-parametric methods such as historical simulation and Monte Carlo simulation. Finally, a numerical example in Iran's market is presented.

Suggested Citation

  • Banihashemi, Shokoofeh & Navidi, Sarah, 2017. "Portfolio performance evaluation in Mean-CVaR framework: A comparison with non-parametric methods value at risk in Mean-VaR analysis," Operations Research Perspectives, Elsevier, vol. 4(C), pages 21-28.
    • Handle: RePEc:eee:oprepe:v:4:y:2017:i:c:p:21-28
    DOI: 10.1016/j.orp.2017.02.001
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    Cited by:

    1. Maciel, Leandro, 2021. "A new approach to portfolio management in the Brazilian equity market: Does assets efficiency level improve performance?," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 38-56.
    2. Wang, Xiantao & Zhu, Yuanguo & Tang, Pan, 2024. "Uncertain mean-CVaR model for portfolio selection with transaction cost and investors’ preferences," The North American Journal of Economics and Finance, Elsevier, vol. 69(PA).
    3. Mohammad Mehdi Hosseinzadeh & Sergio Ortobelli Lozza & Farhad Hosseinzadeh Lotfi & Vittorio Moriggia, 2023. "Portfolio optimization with asset preselection using data envelopment analysis," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(1), pages 287-310, March.
    4. Wencheng Yu & Shaobo Liu & Lili Ding, 2021. "Efficiency Evaluation and Selection Strategies for Green Portfolios under Different Risk Appetites," Sustainability, MDPI, vol. 13(4), pages 1-15, February.
    5. Andrei Rusu, 2020. "Multivariate VaR: A Romanian Market study," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 12(1), pages 79-95, June.
    6. Beatrice D. Simo-Kengne & Kofi A. Ababio & Jules Mba & Ur Koumba, 2018. "Behavioral portfolio selection and optimization: an application to international stocks," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 32(3), pages 311-328, August.
    7. Chai, Shanglei & Zhou, P., 2018. "The Minimum-CVaR strategy with semi-parametric estimation in carbon market hedging problems," Energy Economics, Elsevier, vol. 76(C), pages 64-75.
    8. Ahmed Imran Hunjra & Suha Mahmoud Alawi & Sisira Colombage & Uroosa Sahito & Mahnoor Hanif, 2020. "Portfolio Construction by Using Different Risk Models: A Comparison among Diverse Economic Scenarios," Risks, MDPI, vol. 8(4), pages 1-23, November.

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