The Self-Evolving Logic of Financial Claim Prices

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InCollection{Noe:2002:gagpcf,
  author =       "Thomas H. Noe and Jun Wang",
  title =        "The Self-Evolving Logic of Financial Claim Prices",
  booktitle =    "Genetic Algorithms and Genetic Programming in
                 Computational Finance",
  publisher =    "Kluwer Academic Press",
  year =         "2002",
  editor =       "Shu-Heng Chen",
  chapter =      "12",
  pages =        "249--262",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7923-7601-3",
  URL =          "http://www.springer.com/economics/economic+theory/book/978-0-7923-7601-9",
  DOI =          "doi:10.1007/978-1-4615-0835-9_12",
  abstract =     "In this paper, we use Genetic Programming, an
                 optimisation technique based on the principles of
                 natural selection, to price financial contingent
                 claims. Compared to the traditional arbitrage-based
                 approach, this technique is useful when the underlying
                 asset dynamics are unknown or when the pricing
                 equations are too complicated to solve analytically.
                 Comparing to other established data-driven option
                 pricing techniques such as neural networks, implied
                 binomial trees, etc., genetic programming has the
                 advantage of not restricting the structure of the
                 pricing formulae. In addition, because it is very easy
                 to incorporate existing analytical pricing formulas
                 into the evolutionary process, genetic programming can
                 be applied in combination with existing pricing
                 methods. In this paper, we show that genetic
                 programming can recover Black-Sholes formula from a
                 simulated data sample of fairly small size. The
                 application to S&P 500 futures options also show
                 promising results.",
  notes =        "part of \cite{chen:2002:gagpcf}

                 Author Affiliations: 2. A. B. Freeman School of
                 Business, Tulane University New Orleans, LA,
                 70118-5669, USA 3. SAS Institute Inc., SAS Campus
                 Drive, Room R5217, Cary, NC, 27513, USA",
}

Genetic Programming entries for Thomas H Noe Jun "Jonathan" Wang

Citations