Hedging derivative securities with genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@Article{Chen:1999:ISAFM,

• author = "Shu-Heng Chen and Wo-Chiang Lee and Chia-Hsuan Yeh",
• title = "Hedging derivative securities with genetic programming",
• journal = "Intelligent Systems in Accounting, Finance and Management",
• year = "1999",
• volume = "8",
• number = "4",
• pages = "237--251",
• month = dec,
• note = "Special Issue: Machine Learning and Data Mining in Finance",
• keywords = "genetic algorithms, genetic programming, option pricing, Black-Scholes model, tracking error",
• ISSN = "1099-1174",
• DOI = "doi:10.1002/(SICI)1099-1174(199912)8:4%3C237::AID-ISAF174%3E3.0.CO%3B2-J",
• size = "15 pages",
• abstract = "One of the most recent applications of GP to finance is to use genetic programming to derive option pricing formulae. Earlier studies take the BlackScholes model as the true model and use the artificial data generated by it to train and to test GP. The aim of this paper is to provide some initial evidence of the empirical relevance of GP to option pricing. By using the real data from S&P 500 index options, we train and test our GP by distinguishing the case in-the-money from the case out-of-the-money. Unlike most empirical studies, we do not evaluate the performance of GP in terms of its pricing accuracy. Instead, the derived GP tree is compared with the Black-Scholes model in its capability to hedge. To do so, a notion of tracking error is taken as the performance measure. Based on the post-sample performance, it is found that in approximately 20percent of the 97 test paths GP has a lower tracking error than the Black--Scholes formula. We further compare our result with the ones obtained by radial basis functions and multilayer perceptrons and one-stage GP",
• notes = "See also \cite{chen:1998:hdsGP}",

}

Genetic Programming entries for Shu-Heng Chen Woh-Chiang Lee Chia Hsuan Yeh

Citations