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Pricing European Options Using a Novel Multi-objective Firefly Algorithm

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Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016 (IntelliSys 2016)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 15))

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Abstract

For buying an asset a market participant would like to know to profitability of the contract that he/she likes to enter into as accurately as possible. Multiple approaches available for pricing financial contracts such as options offer various levels of accuracy, difficulty of computation and success rate. Our proposal of a novel Firefly algorithm has multiple benefits in price accuracy, ease of implementation, and overall computational cost. We project the pricing issue of an option into a multi-objective optimization problem. We introduce a different approach for the utility function in our algorithm by considering pay-off and chances of achieving such a pay-off as major focus. Also, we considered a variation of the algorithm by a weight based approach for each firefly whereby we intend to achieve Pareto optimal solution. With multiple contracts that we considered in our study, we show that the proposed algorithm computes Pareto front on option values for all contracts with a maximum of 2% error. It is possible for a real market participant(s) to use our results for deciding before entering a contract of an option knowing their individual risk they could take. To the best of our knowledge, our model of projecting issue of pricing options into a multi-objective optimization problem is novel and our proposal of assigning weights to Fireflies is unique in achieving option values that are close to real market data.

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Notes

  1. 1.

    https://www.treasury.gov/resource-center/data-chart-center/interest-rates.

  2. 2.

    http://www.cboe.com/delayedquote/simplequote.aspx?ticker=VIX.

  3. 3.

    http://get.tableau.com/trial/tableau-software.html.

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Acknowledgment

The authors acknowledge Mr. Deepak Bajpai for the support on the physical server where experiments were organized. The first author received graduate enhancement of tri-council stipends (GETS) from the Faculty of Graduate Studies, University of Manitoba, which he gratefully acknowledges. The second and third authors acknowledge Natural Sciences and Engineering Research Council (NSERC) Canada for partial financial support for this research through Discovery Grants.

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Authors and Affiliations

  1. Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada

    Gobind Preet Singh, Ruppa K. Thulasiram & Parimala Thulasiraman

Authors
  1. Gobind Preet Singh
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  2. Ruppa K. Thulasiram
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  3. Parimala Thulasiraman
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Corresponding author

Correspondence to Gobind Preet Singh .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Engineering, School of Computing and Mathematics, University of Ulster at Jordanstown, Newtownabbey, United Kingdom

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Rahul Bhatia

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Singh, G.P., Thulasiram, R.K., Thulasiraman, P. (2018). Pricing European Options Using a Novel Multi-objective Firefly Algorithm. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. IntelliSys 2016. Lecture Notes in Networks and Systems, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-319-56994-9_47

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  • DOI: https://doi.org/10.1007/978-3-319-56994-9_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56993-2

  • Online ISBN: 978-3-319-56994-9

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