A comparison of wavelet networks and genetic programming in the context of temperature derivatives

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@Article{Alexandridis:2017:IJF,
  author =       "Antonis K. Alexandridis and Michael Kampouridis and 
                 Sam Cramer",
  title =        "A comparison of wavelet networks and genetic
                 programming in the context of temperature derivatives",
  journal =      "International Journal of Forecasting",
  volume =       "33",
  number =       "1",
  pages =        "21--47",
  year =         "2017",
  ISSN =         "0169-2070",
  DOI =          "doi:10.1016/j.ijforecast.2016.07.002",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0169207016300711",
  abstract =     "The purpose of this study is to develop a model that
                 describes the dynamics of the daily average temperature
                 accurately in the context of weather derivatives
                 pricing. More precisely, we compare two
                 state-of-the-art machine learning algorithms, namely
                 wavelet networks and genetic programming, with the
                 classic linear approaches that are used widely in the
                 pricing of temperature derivatives in the financial
                 weather market, as well as with various machine
                 learning benchmark models such as neural networks,
                 radial basis functions and support vector regression.
                 The accuracy of the valuation process depends on the
                 accuracy of the temperature forecasts. Our proposed
                 models are evaluated and compared, both in-sample and
                 out-of-sample, in various locations where weather
                 derivatives are traded. Furthermore, we expand our
                 analysis by examining the stability of the forecasting
                 models relative to the forecasting horizon. Our
                 findings suggest that the proposed nonlinear methods
                 outperform the alternative linear models significantly,
                 with wavelet networks ranking first, and that they can
                 be used for accurate weather derivative pricing in the
                 weather market.",
  keywords =     "genetic algorithms, genetic programming, Weather
                 derivatives, Wavelet networks, Temperature derivatives,
                 Modelling, Forecasting",
}

Genetic Programming entries for Antonis K Alexandridis Michael Kampouridis Sam Cramer

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