Hedging derivative securities with genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@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<237::AID-ISAF174>3.0.CO;2-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