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

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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.

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Banihashemi, S., & Navidi, S. (2017). Portfolio performance evaluation in Mean-CVaR framework: A comparison with non-parametric methods value at risk in Mean-VaR analysis. Operations Research Perspectives, 4, 21–28. https://doi.org/10.1016/j.orp.2017.02.001

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