Applying Dynamic Training-Subset Selection Methods Using Genetic Programming for Forecasting Implied Volatility

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@Article{journals/ci/HamidaAA16,
  title =        "Applying Dynamic Training-Subset Selection Methods
                 Using Genetic Programming for Forecasting Implied
                 Volatility",
  author =       "Sana Ben Hamida and Wafa Abdelmalek and Fathi Abid",
  journal =      "Computational Intelligence",
  year =         "2016",
  volume =       "32",
  number =       "3",
  pages =        "369--390",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, implied
                 volatility forecast, static training-subset selection,
                 dynamic training-subset selection, mean squared errors,
                 percentage of non-fitted observations",
  bibdate =      "2017-05-27",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/https://doi.org/10.1111/coin.12057;
                 DBLP,
                 http://dblp.uni-trier.de/db/journals/ci/ci32.html#HamidaAA16",
  URL =          "http://dx.doi.org/10.1111/coin.12057",
  DOI =          "doi:10.1111/coin.12057",
  abstract =     "Volatility is a key variable in option pricing,
                 trading, and hedging strategies. The purpose of this
                 article is to improve the accuracy of forecasting
                 implied volatility using an extension of genetic
                 programming (GP) by means of dynamic training-subset
                 selection methods. These methods manipulate the
                 training data in order to improve the out-of-sample
                 patterns fitting. When applied with the static subset
                 selection method using a single training data sample,
                 GP could generate forecasting models, which are not
                 adapted to some out-of-sample fitness cases. In order
                 to improve the predictive accuracy of generated GP
                 patterns, dynamic subset selection methods are
                 introduced to the GP algorithm allowing a regular
                 change of the training sample during evolution. Four
                 dynamic training-subset selection methods are proposed
                 based on random, sequential, or adaptive subset
                 selection. The latest approach uses an adaptive subset
                 weight measuring the sample difficulty according to the
                 fitness cases' errors. Using real data from S&P500
                 index options, these techniques are compared with the
                 static subset selection method. Based on mean squared
                 error total and percentage of non-fitted observations,
                 results show that the dynamic approach improves the
                 forecasting performance of the generated GP models,
                 especially those obtained from the adaptive-random
                 training-subset selection method applied to the whole
                 set of training samples.",
}

Genetic Programming entries for Sana Ben Hamida Wafa Abdelmalek Fathi Abid

Citations