Abstract
In this paper, value-at-risk (VaR for short) is used as the measure of risk. Based on the concept of VaR, a fuzzy mean-VaR model is proposed. Firstly, we recall some definitions and results of value-at-risk in credibilistic risk analysis. Secondly, we propose the fuzzy mean- VaR model of fuzzy programming, or more precisely, credibilistic programming. Thirdly, a hybrid intelligent algorithm is provided to give a general solution of the optimization problem. Finally, numerical examples are also presented to illustrate the effectiveness of the proposed algorithm.
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Dong, W., Peng, J. (2009). Mean-VaR Models and Algorithms for Fuzzy Portfolio Selection. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2009. Communications in Computer and Information Science, vol 51. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04962-0_36
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DOI: https://doi.org/10.1007/978-3-642-04962-0_36
Publisher Name: Springer, Berlin, Heidelberg
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