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Building Fuzzy Variance Gamma Option Pricing Models with Jump Levy Process

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Intelligent Decision Technologies 2017 (IDT 2017)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 73))

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Abstract

Option pricing models are at core of financial area, and it includes various uncertain factors, such as the randomness and fuzziness. This paper constructs an jump Levy process by combining option pricing models with fuzzy theory, and it sets the drift, diffusion and trend terms as fuzzy random variable. Then, we adopts a Monte Carlo algorithm for numerical simulation, compares and analyses the variance gamma (VG) option pricing model through a simulation experiment, and determines the VG option pricing model and BS model pricing results. The results indicate that VG option pricing with fuzzy settings is feasible.

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Author information

Authors and Affiliations

  1. Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu, Kitakyushu, 808-0135, Japan

    Huiming Zhang

  2. Computer and Information Sciences Department, PETRONAS University of Technology, 32610, Seri Iskandar, Perak Darul Ridzuan, Malaysia

    Junzo Watada

Authors
  1. Huiming Zhang
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  2. Junzo Watada
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Correspondence to Huiming Zhang .

Editor information

Editors and Affiliations

  1. Maritime University , Gdynia, Poland

    Ireneusz Czarnowski

  2. Bournemouth University and KES International, Poole, Dorset, United Kingdom

    Robert J. Howlett

  3. University of Canberra, Canberra, Aust Capital Terr, Australia

    Lakhmi C. Jain

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Zhang, H., Watada, J. (2018). Building Fuzzy Variance Gamma Option Pricing Models with Jump Levy Process. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2017. IDT 2017. Smart Innovation, Systems and Technologies, vol 73. Springer, Cham. https://doi.org/10.1007/978-3-319-59424-8_10

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  • DOI: https://doi.org/10.1007/978-3-319-59424-8_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59423-1

  • Online ISBN: 978-3-319-59424-8

  • eBook Packages: EngineeringEngineering (R0)

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