More Web Proxy on the site http://driver.im/

article

Normalized particle swarm optimization for complex chooser option pricing on graphics processing unit

Authors:

Ruppa K. Thulasiram,

Parimala ThulasiramanAuthors Info & Claims

The Journal of Supercomputing, Volume 66, Issue 1

Pages 170 - 192

https://doi.org/10.1007/s11227-013-0893-z

Published: 01 October 2013 Publication History

Abstract

An option is a financial instrument that derives its value from an underlying asset, for example, a stock. There are a wide range of options traded today from simple and plain (European options) to exotic (chooser options) that are very difficult to evaluate. Both buyers and sellers continue to look for efficient algorithms and faster technology to price options for better profit and to beat the competition. There are mathematical models like the Black---Scholes---Merton model used to price options approximately for simple and plain options in the form of closed form solution. However, the market is flooded with various styles of options, which are difficult to price, and hence there are many numerical techniques proposed for pricing. The computational cost for pricing complex options using these numerical techniques is exorbitant for reasonable accuracy in pricing results. Heuristic approaches such as particle swarm optimization (PSO) have been proposed for option pricing, which provide same or better results for simple options than that of numerical techniques at much less computational cost. In this study, we first map the PSO parameters to option pricing parameters. Analyzing the characteristics of PSO and option pricing, we propose a strategy to normalize some of the parameters, which helps in better understanding of the sensitivity of these and other parameters on option pricing results. We then avail of the inherent concurrency of the PSO algorithm while searching for an optimum solution, and design an algorithm for implementation on a modern state-of-the-art graphics processor unit (GPU). Our implementation makes use of the architectural features of GPU in accelerating the computing performance while maintaining accuracy on the pricing results.

References

[1]

Barua S, Thulasiram RK, Thulasiraman P (2005) High performance computing for a financial application using fast Fourier transform. In: The 11th international European parallel computing conference, Lisbon, Portugal. LNCS, vol 3648. Springer, Berlin, pp 1246---1253

[2]

Bastos-Filho CJA, Andrade JD, Pita MRS, Ramos AD (2009) Impact of the quality of random numbers generators on the performance of particle swarm optimization. In: Proceedings of the 2009 IEEE international conference on systems, man, and cybernetics, San Antonio, Texas, October 2009, pp 4988---4993

[3]

Bharadia MAJ, Christofides N, Salkin GR (1995) Computing the Black Scholes implied volatility: generalization of a simple formula. Adv Futures Options Res 8:15---29

[4]

Bharadia MAJ, Christofides N, Salkin GR (1996) A quadratic method for the calculation of implied volatility using the Garman-Kohlhagen model. Financ Anal J 52:61---64

[5]

Black FS, Scholes MS (1973) The pricing of options and corporate liabilities. Eur J Polit Econ 81(3):637---654

[6]

Boyle P (1977) Options: a Monte Carlo approach. J Financ Econ 4:223---238

[7]

Brabazon A, O'Neil M (2006) Biologically inspired algorithms for financial modelling. Natural computing series. Springer, Berlin

[8]

Carr P, Madan DB (1999) Option valuation using the fast Fourier transform. J Comput Finance 2(4):61---73

[9]

Chance DM (1996) A generalized simple formula to compute the implied volatility. Financ Rev 3:859---867

[10]

Chang JF, Chu SC, Roddick JF, Pan JS (2005) A parallel particle swarm optimization algorithm with communication strategies. J Inf Sci Eng 21:809---818

[11]

Chen Y, Yang B, Dong J (2006) Time-series prediction using a local linear wavelet neural network. Neurocomputing 69:449---465

Digital Library

[12]

Chidambaran NK, Lee CWJ, Trigueros JR (2000) Option pricing via genetic programming. In: The 6th international conference on computational finance, Leonard N Stern School of Business, January 1999. MIT Press, Cambridge, pp 583---598

[13]

Dorigo M, Maniezzo V, Colorni A (1996) Ant system---optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern 26:29---41

Digital Library

[14]

Durst MJ (1989) Using linear congruential generators for parallel random number generation. In: Proceedings of the 21st conference on winter simulation, New York, NY, pp 462---466

[15]

Heston S, Zhou G (2000) On the rate of convergence of discrete-time contingent claims. Math Finance 10:53---75

[16]

Hull JC (2011) Options, futures and other derivatives. Prentice Hall, New York

[17]

Hutchinson JM, Lo AW, Poggio T (1994) A nonparametric approach to pricing and hedging derivative securities via learning networks. J Finance 49:851---889

[18]

Jauvion G, Nguyen T (2008) Parallelized trinomial option pricing model on GPU with CUDA. http://www.arbitragis-research.com/cuda-in-computational-finance/coxross-gpu.pdf, August 2008

[19]

Cox J, Ross S, Rubinstein M (1979) Option pricing: a simplified approach. J Financ Econ 7:229---263

[20]

Jha GK, Prasain H, Kumar S, Thulasiram RK, Thulasiraman P (2009) Option pricing using particle swarm intelligence. In: Proceedings ACM Canadian computer science and software engineering conference, May 2009, pp 267---272

[21]

Jin N, Samii YR (2005) Parallel particle swarm optimization and finite-difference time-domain (PSO/FDTD) algorithm for multiband and wide-band patch antenna designs. IEEE Trans Antennas Propag 53(11):3459---3468

[22]

Keber C (1999) Genetically derived approximations for determining the implied volatility. OR Spektrum 21:205---238

[23]

Keber C, Schuster MG (2002) Option valuation with generalized ant programming. In: The ACM genetic and evolutionary computation conference, pp 74---81

[24]

Keber C, Schuster MG (2003) Generalized ant programming in option pricing: determining implied volatilities based on American put options. In: IEEE proceedings of computational intelligence in financial engineering, December 2003, pp 123---130

[25]

Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks, Perth, Australia, November 1995, vol 4, pp 1942---1948

[26]

Kumar S, Thulasiram RK, Thulasiraman P (2008) Ant colony optimization for option pricing. Natural computing in computational finance, vol 2. Springer, Berlin. Chap. 4

[27]

Kumar S, Thulasiram RK, Thulasiraman P (2008) A bioinspired algorithm to price options. In: The ACM C* conference on computer science and software engineering, Montreal, May 2008, pp 11---22

[28]

Li J, Wang D, Chi S, Hu X (2007) An efficient fine-grained parallel particle swarm optimization method based on GPU-acceleration. International Journal of Innovation Comupting, Information and Control 3(B(6)):1707---1714

[29]

Meneses AAM, Machado MD, Schirru R (2009) Particle swarm optimization applied to the nuclear reload problem of a pressurized water reactor. Prog Nucl Energy 51(2):319---326

[30]

Merton RC (1973) Theory of rational option pricing. Bell J Econ Manag Sci 4(1):141---183

[31]

Mussi L, Daolio F, Cagnoni S (2010) Evaluation of parallel particle swarm optimization algorithms within the CUDA architecture. Inf Sci 181(20):4642---4657

Digital Library

[32]

NVIDIA. Cuda 2.0 Programming Guide. NVIDIA, June 2008

[33]

Podlozhnyuk V (2008) Binomial option pricing model. http://developer.download.nvidia.com/compute/cuda/sdk/website/projects/binomialOptions/doc/binomialOptions.pdf

[34]

Prasain H, Thulasiram RK, Thulasiraman P (2010) Mapping PSO and exploitation concurrency for option pricing on a homogeneous multi-core architecture. In: The ACM genetic and evolutionary computation conference, Portland, OR, July 2010

[35]

Prasain H, Thulasiraman P, Thulasiram RK, Jha GK (2010) Performance evaluation of PSO-based algorithm for option pricing on homogeneous multicore architecture. In: Proceedings IASTED computational intelligence, Maui, HI, August 2010

[36]

Rahmayil S, Shiller I, Thulasiram RK (2004) Different estimators of the underlying asset's volatility and option pricing errors: parallel Monte-Carlo simulation. In: Proceedings of the international conference on computational finance and its applications, Bologna, Italy, pp 121---131

[37]

Rubinstein M (1991) Option for the undecided. Risk Mag 4(4):43

[38]

Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M, Tarantola S (2008) Global sensitivity analysis. Wiley, New York

[39]

Schutte JF, Reinbolt JA, Fregly BJ, Hafka RT, George AD (2004) Parallel global optimization with particle swarm algorithm. Int J Numer Methods Eng 61:2296---2315

[40]

Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: Proceedings IEEE world congress on evolutionary computation, Archorage, AK, USA, May 1998, pp 69---73

[41]

Solomon S, Thulasiram RK, Thulasiraman P (2010) Option pricing on the GPU. In: The 12th IEEE international conference on high performance computing and communications, Melbourne, Australia, September 2010, pp 289---296

[42]

Solomon S, Thulasiraman P, Thulasiram RK (2011) Collaborative multi-swarm PSO for task matching using graphics processing units. In: Proceedings ACM genetic and evolutionary computation conference, Dublin, Ireland, July 2011, pp 297---304

[43]

Tavella D, Randall C (2000) Pricing financial instruments: the finite differencing method. Wiley, New York

[44]

Thulasiram RK, Litov L, Nojumi H, Downing C, Gao G (2001) Multithreaded algorithms for pricing a class of complex options. In: Proceedings IEEE/ACM international parallel and distributed processing symposium, San Francisco, CA

[45]

Thulasiram RK, Thulasiraman P (2003) Performance evaluation of a multithreaded fast Fourier transform algorithm for derivative pricing. J Supercomput 26(1):43---58

[46]

Venter G, Sobieszczanski-Sobieski J (2006) A parallel particle swarm optimization algorithm accelerated by asynchronous evaluations. J Aerosp Comput Inf Commun 3(3):123---137

[47]

Yin Z, Brabazon A, Sullivan CO (2007) Adaptive genetic programming for option pricing. In: The ACM genetic and evolutionary computation conference, pp 2588---2594

Cited By

Li JGajpal YBhardwaj AChen HLiu Y(2021)Two-Agent Single Machine Order Acceptance Scheduling Problem to Maximize Net RevenueComplexity10.1155/2021/66270812021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/6627081
Yu XQiao YLi QXu GKang CEstevez CDeng CWang S(2020)Parallelizing Comprehensive Learning Particle Swarm Optimization by Open Computing Language on an Integrated Graphical Processing UnitComplexity10.1155/2020/65896582020Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1155/2020/6589658
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
Show More Cited By

Normalized particle swarm optimization for complex chooser option pricing on graphics processing unit
1. Applied computing

Recommendations

Particle swarm optimization algorithm for option pricing: extended abstract
GECCO '10: Proceedings of the 12th annual conference companion on Genetic and evolutionary computation

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.
Portfolio Management Using Particle Swarm Optimization on GPU
ISPA '12: Proceedings of the 2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications

Mathematical models like the Black-Scholes-Merton model used to price options approximately for simple and plain options in the form of closed form solution. The market is flooded with various styles of options, which are difficult to price. Numerical ...
Option pricing using Particle Swarm Optimization
C3S2E '09: Proceedings of the 2nd Canadian Conference on Computer Science and Software Engineering

Option pricing is one of the challenging areas of computational finance. In this paper an attempt is made to apply Particle Swarm Optimization (PSO) for pricing options. PSO is one of the novel global search algorithm based on swarm intelligence. It is ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image The Journal of Supercomputing

The Journal of Supercomputing Volume 66, Issue 1

October 2013

609 pages

ISSN:0920-8542

Issue’s Table of Contents

Copyright © Copyright © 2013 Springer Science+Business Media New York.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 October 2013

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 16 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Li JGajpal YBhardwaj AChen HLiu Y(2021)Two-Agent Single Machine Order Acceptance Scheduling Problem to Maximize Net RevenueComplexity10.1155/2021/66270812021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/6627081
Yu XQiao YLi QXu GKang CEstevez CDeng CWang S(2020)Parallelizing Comprehensive Learning Particle Swarm Optimization by Open Computing Language on an Integrated Graphical Processing UnitComplexity10.1155/2020/65896582020Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1155/2020/6589658
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
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
Hasanzadeh MMeybodi M(2015)Distributed optimization Grid resource discoveryThe Journal of Supercomputing10.1007/s11227-014-1289-471:1(87-120)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1007/s11227-014-1289-4

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents