research-article

A bioinspired algorithm to price options

Authors:

Ruppa K. Thulasiram,

Parimala ThulasiramanAuthors Info & Claims

C3S2E '08: Proceedings of the 2008 C³S²E conference

Pages 11 - 22

https://doi.org/10.1145/1370256.1370260

Published: 12 May 2008 Publication History

Abstract

Computing option prices is a challenging problem. Finding the best time to exercise an option is a even more challenging problem. One has to be watchful for the price changes in the market place and act at the right time. That is, prices need to be policed. This paper proposes a novel idea for pricing options using a nature inspired meta-heuristic algorithm, Ant Colony Optimization (ACO). ACO has been used extensively in combinatorial optimization problems and recently in dynamic applications such as mobile ad-hoc networks. Specifically, we adapt the general ACO algorithm to apply to a totally different application, computational finance, in the current study. We police the prices using ants to decide on the best time to exercise so that the holder of the option contract will get the maximum benefit out of his/her investment. Our algorithm and implementation suggests a better way to price options than traditional numerical techniques such as binomial lattice algorithm. From our results we conclude that reactive ants may be best suited for long-dated options whose performance can still be improved.

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Cited By

Houssein ESaeed MHu GAl-Sayed M(2024)Metaheuristics for Solving Global and Engineering Optimization Problems: Review, Applications, Open Issues and ChallengesArchives of Computational Methods in Engineering10.1007/s11831-024-10168-631:8(4485-4519)Online publication date: 21-Aug-2024
https://doi.org/10.1007/s11831-024-10168-6
Soler-Dominguez AJuan AKizys R(2017)A Survey on Financial Applications of MetaheuristicsACM Computing Surveys10.1145/305413350:1(1-23)Online publication date: 4-Apr-2017
https://dl.acm.org/doi/10.1145/3054133
Mather KThulasiraman PThulasiram RDash S(2017)A parallel firefly meta-heuristics algorithm for financial option pricing2017 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI.2017.8280981(1-8)Online publication date: Nov-2017
https://doi.org/10.1109/SSCI.2017.8280981
Show More Cited By

Index Terms

A bioinspired algorithm to price options
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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cover image ACM Other conferences

C3S2E '08: Proceedings of the 2008 C³S²E conference

May 2008

240 pages

ISBN:9781605581019

DOI:10.1145/1370256

General Chair:
Bipin C. Desai
Concordia University

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 12 May 2008

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C3S2E '08

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Concordia University

C3S2E '08: Canadian Conference on Computer Science & Software Engineering

May 12 - 13, 2008

Quebec, Montreal, Canada

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Overall Acceptance Rate 12 of 42 submissions, 29%

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Cited By

Houssein ESaeed MHu GAl-Sayed M(2024)Metaheuristics for Solving Global and Engineering Optimization Problems: Review, Applications, Open Issues and ChallengesArchives of Computational Methods in Engineering10.1007/s11831-024-10168-631:8(4485-4519)Online publication date: 21-Aug-2024
https://doi.org/10.1007/s11831-024-10168-6
Soler-Dominguez AJuan AKizys R(2017)A Survey on Financial Applications of MetaheuristicsACM Computing Surveys10.1145/305413350:1(1-23)Online publication date: 4-Apr-2017
https://dl.acm.org/doi/10.1145/3054133
Mather KThulasiraman PThulasiram RDash S(2017)A parallel firefly meta-heuristics algorithm for financial option pricing2017 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI.2017.8280981(1-8)Online publication date: Nov-2017
https://doi.org/10.1109/SSCI.2017.8280981
Singh GThulasiram RThulasiraman P(2017)Pricing European Options Using a Novel Multi-objective Firefly AlgorithmProceedings of SAI Intelligent Systems Conference (IntelliSys) 201610.1007/978-3-319-56994-9_47(687-705)Online publication date: 20-Aug-2017
https://doi.org/10.1007/978-3-319-56994-9_47
Thulasiram RThulasiraman PPrasain HJha G(2016)Nature-inspired soft computing for financial option pricing using high-performance analyticsConcurrency and Computation: Practice & Experience10.1002/cpe.336028:3(707-728)Online publication date: 10-Mar-2016
https://dl.acm.org/doi/10.1002/cpe.3360
He GHuang NMa HLu JWu M(2014)An Improved Particle Swarm Optimization Algorithm for Option PricingProceedings of the Eighth International Conference on Management Science and Engineering Management10.1007/978-3-642-55122-2_74(861-869)Online publication date: 7-May-2014
https://doi.org/10.1007/978-3-642-55122-2_74
Singh SThulasiram RThulasiraman P(2013)A novel application of ACO to price transmission rights in electricity markets2013 World Congress on Nature and Biologically Inspired Computing10.1109/NaBIC.2013.6617874(267-273)Online publication date: Aug-2013
https://doi.org/10.1109/NaBIC.2013.6617874
Martínez-Jaramillo SGarcía-Almanza AAlexandrova-Kabadjova BCenteno T(2011)Evolutionary Computation in FinanceEncyclopedia of Machine Learning10.1007/978-0-387-30164-8_274(344-353)Online publication date: 2011
https://doi.org/10.1007/978-0-387-30164-8_274
Thulasiram RThulasiraman P(2010)Pricing algorithms for financial derivativesAlgorithms and theory of computation handbook10.5555/1882723.1882756(33-33)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.5555/1882723.1882756
Prasain HThulasiraman PThulasiram RJha GPelikan MBranke J(2010)Particle swarm optimization algorithm for option pricingProceedings of the 12th annual conference companion on Genetic and evolutionary computation10.1145/1830761.1830886(2109-2110)Online publication date: 7-Jul-2010
https://dl.acm.org/doi/10.1145/1830761.1830886
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