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A Dynamic Value-at-Risk Portfolio Model

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Modeling Decision for Artificial Intelligence (MDAI 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6820))

Abstract

A mathematical dynamic portfolio allocation model with uncertainty is discussed. Introducing a value-at-risk under a condition, this paper formulates value-at-risks in a dynamic stochastic environment. By dynamic programming approach, an optimality condition of the optimal portfolio for dynamic value-at-risks is derived. It is shown that the optimal time-average value-at-risk is a solution of the optimality equation under a reasonable assumption, and an optimal trading strategy is obtained from the equation. A numerical example is given to illustrate our idea.

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Author information

Authors and Affiliations

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

    Yuji Yoshida

Authors
  1. Yuji Yoshida
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Editor information

Editors and Affiliations

  1. Artificial Intelligence Research Institute (IIIA) Spanish National Research Council (CSIC), IIIA-CSIC, Campus Universitat Autonoma de Barcelona, 08193, Bellaterra, Catalonia, Spain

    Vicenç Torra

  2. Toho Gakuen, 3-1-10, Naka, Kunitachi, 184-0004, Tokyo, Japan

    Yasuo Narakawa

  3. School of Computer, National University of Defense Technology, 410073, Changsha, China

    Jianping Yin

  4. Department of Network Engineering, National University of Defense Technology, Yanwachi Street 137, 410073, Changsha, Hunan, China

    Jun Long

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Yoshida, Y. (2011). A Dynamic Value-at-Risk Portfolio Model. In: Torra, V., Narakawa, Y., Yin, J., Long, J. (eds) Modeling Decision for Artificial Intelligence. MDAI 2011. Lecture Notes in Computer Science(), vol 6820. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22589-5_6

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  • DOI: https://doi.org/10.1007/978-3-642-22589-5_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22588-8

  • Online ISBN: 978-3-642-22589-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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