Selecting the Best Forecasting-Implied Volatility Model Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@Article{Abdelmalek:2009:JAMDS,
  title =        "Selecting the Best Forecasting-Implied Volatility
                 Model Using Genetic Programming",
  author =       "Wafa Abdelmalek and Sana {Ben Hamida} and Fathi Abid",
  journal =      "Journal of Applied Mathematics and Decision Sciences",
  year =         "2009",
  publisher =    "Hindawi Publishing Corporation",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://downloads.hindawi.com/journals/ads/2009/179230.pdf",
  URL =          "http://www.hindawi.com/journals/ads/2009/179230.html",
  doi =          "doi:10.1155/2009/179230",
  ISSN =         "11739126",
  bibsource =    "OAI-PMH server at www.doaj.org",
  language =     "eng",
  oai =          "oai:doaj-articles:b3bc3b339d2f713819080ff9b253312a",
  abstract =     "The volatility is a crucial variable in option pricing
                 and hedging strategies. The aim of this paper is to
                 provide some initial evidence of the empirical
                 relevance of genetic programming to volatility's
                 forecasting. By using real data from S\&P500 index
                 options, the genetic programming's ability to forecast
                 Black and Scholes-implied volatility is compared
                 between time series samples and moneyness-time to
                 maturity classes. Total and out-of-sample mean squared
                 errors are used as forecasting's performance measures.
                 Comparisons reveal that the time series model seems to
                 be more accurate in forecasting-implied volatility than
                 moneyness time to maturity models. Overall, results are
                 strongly encouraging and suggest that the genetic
                 programming approach works well in solving financial
                 problems.",
  notes =        "Article ID 179230

                 1RU: MODESFI, Faculty of Economics and Business, Road
                 of the Airport Km 4, 3018 Sfax, Tunisia 2Laboratory of
                 Intelligent IT Engineering, Higher School of Technology
                 and Computer Science, 2035 Charguia, Tunisia",
}

Genetic Programming entries for Wafa Abdelmalek Sana Ben Hamida Fathi Abid