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A Dynamic Average Value-at-Risk Portfolio Model with Fuzzy Random Variables

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Fuzzy Sets, Rough Sets, Multisets and Clustering

Part of the book series: Studies in Computational Intelligence ((SCI,volume 671))

Abstract

A perception-based portfolio model under uncertainty is presented. In the proposed model, randomness and fuzziness are evaluated respectively by probabilistic expectation and the mean values with evaluation weights and \(\lambda \)-mean functions. Introducing average value-at-risks under conditions, this paper formulates average value-at-risks in dynamic stochastic environment. By dynamic programming approach, an optimality condition of the optimal portfolios for dynamic average value-at-risks is derived. It is shown that the optimal average value-at-risk is a solution of the optimality equation under a reasonable assumption, and an optimal portfolio weight is obtained from the equation.

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Acknowledgements

This research is supported from JSPS KAKENHI Grant Number JP 16K05282.

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Authors and Affiliations

  1. Faculty of Economics and Business Administration, University of Kitakyushu, 4-2-1 Kitagata, Kokuraminami, Kitakyushu, 802-8577, Japan

    Yuji Yoshida

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  1. Yuji Yoshida
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Correspondence to Yuji Yoshida .

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Editors and Affiliations

  1. University of Skövde, School of Informatics University of Skövde, Skövde, Sweden

    Vicenç Torra

  2. University of Skövde, School of Informatics University of Skövde, Skövde, Sweden

    Anders Dahlbom

  3. Toho Gakuen , Tokyo, Japan

    Yasuo Narukawa

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Yoshida, Y. (2017). A Dynamic Average Value-at-Risk Portfolio Model with Fuzzy Random Variables. In: Torra, V., Dahlbom, A., Narukawa, Y. (eds) Fuzzy Sets, Rough Sets, Multisets and Clustering. Studies in Computational Intelligence, vol 671. Springer, Cham. https://doi.org/10.1007/978-3-319-47557-8_17

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  • DOI: https://doi.org/10.1007/978-3-319-47557-8_17

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  • Online ISBN: 978-3-319-47557-8

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