Abstract
In this paper, we consider a hybrid portfolio optimization problem with mature securities and newly listed securities. We employ uncertain random variables to characterize the returns of securities, and introduce tail value-at-risk (TVaR) to measure the corresponding risk. We first prove some mathematical properties of TVaR of uncertain random variables and give a numerical algorithm to approximate the TVaR. Then, we formulate several mean-TVaR models for the hybrid portfolio optimization problem and give the crisp equivalent forms of these models. Finally, we conduct a numerical example to illustrate the application of the proposed method.
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Funding
This work was supported by National Natural Science Foundation of China (Nos. 72071008, 71771011 and 72001011) and “Youth Top-notch Talent” Support Program of Beihang University (YWF-22-L-228).
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Li, Q., Qin, Z. & Yan, Y. Uncertain random portfolio optimization model with tail value-at-risk. Soft Comput 26, 9385–9394 (2022). https://doi.org/10.1007/s00500-022-07249-8
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DOI: https://doi.org/10.1007/s00500-022-07249-8