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Optimized radial basis function neural network for improving approximate dynamic programming in pricing high dimensional options

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Abstract

Pricing American basket option is one of the essential problems in quantitative finance. The complexity of this type of option has motivated many practitioners and researchers to develop simulation-based methods. In this paper, we develop an optimized radial basis function neural network (RBFNN), which is optimally tuned by the particle swarm optimization algorithm to enhance the efficiency and accuracy of approximate dynamic programming (ADP) for pricing the American basket option. Additionally, for the scenario generation, a simulation-based technique using a copula-GARCH method and Extreme Value Theory is performed to tackle the nonlinearity of dependencies between variables. The prices obtained through the proposed approach compared with those ones achieved from pure RBFNN and ADP in different situations. This is also illustrated that the obtained prices of American basket option can outperform the results obtained through the RBFNN and ADP approaches in terms of the predefined fitness measures.

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Authors and Affiliations

  1. Department of Industrial Engineering and Management Systems, Amirkabir University of Technology (Tehran Polytechnics), 424 Hafez Ave., Tehran, 15916-34311, Iran

    Ehsan Hajizadeh & Masoud Mahootchi

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  1. Ehsan Hajizadeh
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  2. Masoud Mahootchi
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Correspondence to Masoud Mahootchi.

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Hajizadeh, E., Mahootchi, M. Optimized radial basis function neural network for improving approximate dynamic programming in pricing high dimensional options. Neural Comput & Applic 30, 1783–1794 (2018). https://doi.org/10.1007/s00521-016-2802-x

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