extended-abstract

Particle swarm optimization algorithm for option pricing: extended abstract

Authors:

Hari Prasain,

Parimala Thulasiraman,

Ruppa K. Thulasiram,

Girish K. JhaAuthors Info & Claims

GECCO '10: Proceedings of the 12th annual conference companion on Genetic and evolutionary computation

Pages 2109 - 2110

https://doi.org/10.1145/1830761.1830886

Published: 07 July 2010 Publication History

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Abstract

In this paper we design, develop and implement a sequential Particle swarm optimization based option pricing algorithm. The proposed algorithm is applicable for both European and American option.

References

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G. K. Jha, P. Thulasiraman, and R. K. Thulasiram. PSO based neural network for time series forecasting. In Proceedings of IEEE International Joint Conference on Neural Networks, pages 1422--1427, June 2009.

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S. Kumar, R. K. Thulasiram, and P. Thulasiraman. A bioinspired algorithm to price options. In ACM Proc. of the C* Conference on Computer Science and Software Engienering, pages 11--22, Montreal, May 2008.

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Z. Yin, A. Brabazon, and C. O'Sullivan. Adaptive genetic programming for option pricing. In GECCO, pages 2588--2594, London, England, UK, 2007.

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Index Terms

Particle swarm optimization algorithm for option pricing: extended abstract
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Discrete space search
      2. Game tree search

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Published In

GECCO '10: Proceedings of the 12th annual conference companion on Genetic and evolutionary computation

July 2010

1496 pages

ISBN:9781450300735

DOI:10.1145/1830761

General Chair:
Martin Pelikan
University of Missouri, USA
,
Program Chair:
Jürgen Branke
University of Warwick, Coventry, UK

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2010

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Extended-abstract

Conference

GECCO '10

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SIGEVO

GECCO '10: Genetic and Evolutionary Computation Conference

July 7 - 11, 2010

Oregon, Portland, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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