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A Class of Fuzzy Portfolio Optimization Problems: E-S Models

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Advances in Swarm Intelligence (ICSI 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6146))

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Abstract

This paper adopts the spread of fuzzy variable as a new criteria in practical risk management problems, and develops a novel fuzzy expectation-spread (E-S) model for portfolio optimization problem. Since the spread is defined by Lebesgue-Stieltjes (L-S) integral, its computation for general fuzzy variables is a challenge issue for research, and usually depends on approximation scheme and soft computing. But for frequently used trapezoidal and triangular fuzzy variables, the spread can be represented as quadratic functions with respect to fuzzy parameters. These new representations facilitate us to turn the proposed E-S model into its equivalent parametric programming problem. As a consequence, given the fuzzy parameters, the E-S model becomes a quadratic programming problem that can be solved by general purpose software or conventional optimization algorithms. Finally, we demonstrate the developed modeling idea via two numerical examples.

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Authors and Affiliations

  1. College of Mathematics and Computer Science, Hebei University, Baoding, 071002, Hebei, China

    Yankui Liu & Xiaoli Wu

Authors
  1. Yankui Liu
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  2. Xiaoli Wu
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Editor information

Editors and Affiliations

  1. Key Laboratory of Machine Perception (MOE), Department of Machine Intelligence, Peking University, 100871, Beijing, China

    Ying Tan

  2. Research and Postgraduate Office, Xi’an Jiaotong-Liverpool University, 215123, Suzhoum, China

    Yuhui Shi

  3. Department of Electrical and Computer Engineering, Engineering Drive 3, 4 Engineering Drive 3, National University of Singapore, 117576, Singapore

    Kay Chen Tan

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Liu, Y., Wu, X. (2010). A Class of Fuzzy Portfolio Optimization Problems: E-S Models. In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6146. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13498-2_6

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  • DOI: https://doi.org/10.1007/978-3-642-13498-2_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13497-5

  • Online ISBN: 978-3-642-13498-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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