{"title":"A Dynamic Hybrid Option Pricing Model by Genetic Algorithm and Black- Scholes Model","authors":"Yi-Chang Chen, Shan-Lin Chang, Chia-Chun Wu","volume":45,"journal":"International Journal of Economics and Management Engineering","pagesStart":2000,"pagesEnd":2004,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/1530","abstract":"Unlike this study focused extensively on trading\r\nbehavior of option market, those researches were just taken their\r\nattention to model-driven option pricing. For example, Black-Scholes\r\n(B-S) model is one of the most famous option pricing models.\r\nHowever, the arguments of B-S model are previously mentioned by\r\nsome pricing models reviewing. This paper following suggests the\r\nimportance of the dynamic character for option pricing, which is also\r\nthe reason why using the genetic algorithm (GA). Because of its\r\nnatural selection and species evolution, this study proposed a hybrid\r\nmodel, the Genetic-BS model which combining GA and B-S to\r\nestimate the price more accurate. As for the final experiments, the\r\nresult shows that the output estimated price with lower MAE value\r\nthan the calculated price by either B-S model or its enhanced one,\r\nGram-Charlier garch (G-C garch) model. Finally, this work would\r\nconclude that the Genetic-BS pricing model is exactly practical.","references":"[1] F. Black and M. Scholes, \"The pricing of options and corporate\r\nliabilities,\" Journal of Political and Economy, vol. 81, 1973, pp. 637-654.\r\n[2] J-P. Bouchaud and M. Potters, \"Welcome to a non-Black-Scholes world,\"\r\nQuantitative Finance, vol. 1, no.5, 2001, pp. 482-483.\r\n[3] G. Barone-Adesi, R. F. Engle, and L. Mancini, \"A GARCH option pricing\r\nmodel with filtered historical simulation,\" Review of Financial Studies,\r\nvol. 21, no. 3, 2008, pp. 1223-1258.\r\n[4] R. Company, E. Navarro, J. R. Pintos, and E. Ponsoda, \"Numerical\r\nsolution of linear and nonlinear Black-Scholes option pricing equations,\"\r\nComputers and Mathematics with Applications archive, vol. 56, no. 3,\r\n2008, pp. 813-821.\r\n[5] S. K. Mitra, \"Valuation of nifty options using Black's option pricing\r\nformula,\" The Icfai Journal of Derivatives Markets, vol. 5, no. 1, 2008,\r\npp. 50-61\r\n[6] P. Lin and P. Ko, \"Portfolio value-at-risk forecasting with GA-based\r\nextreme value theory,\" Expert Systems with Applications, vol. 36, 2009,\r\npp. 2503-2512.\r\n[7] H. Chou, D. Chen, and C. Wu, \"Valuation and hedging performance of\r\ngram-charlier GARCH option pricing algorithm,\" Journal of Management\r\n& Systems, vol. 14, no. 1, 2007, pp. 95-119.\r\n[8] N. K. Chidambaran, C. H. J. Lee, and J. Trigueros, \"An adaptive\r\nevolutionary approach to option pricing via genetic programming, in:\r\nevolutionary computation in economics and finance, editor: Shu-Hueng\r\nChen,\" Springer Verlag, 2002.\r\n[9] J. Holland, \"Adaptation in natural and artificial systems,\" Originally\r\npublished by the University of Michigan Press, 1975.\r\n[10] S. H. Chu and S. Freund, \"Volatility estimation for stock index options: a\r\nGARCH approach,\" Quarterly Review of Economics and Finance, Vol.\r\n36, 1996, pp. 431-450.\r\n[11] S.-H. Chen, W.-C. Lee, \"Numerical methods in option pricing:\r\nmodel-driven approach vs. data-driven approach,\" Society of Industrial\r\nand Applied Mathematics 45th Anniversary Meeting (SIAM'97), 1997.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 45, 2010"}