Temperature Forecasting in the Concept of Weather Derivatives: a Comparison between Wavelet Networks and Genetic Programming

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@InProceedings{conf/eann/AlexandirisK13,
  author =       "Antonios K. Alexandiris and Michael Kampouridis",
  title =        "Temperature Forecasting in the Concept of Weather
                 Derivatives: a Comparison between Wavelet Networks and
                 Genetic Programming",
  editor =       "Lazaros S. Iliadis and Harris Papadopoulos and 
                 Chrisina Jayne",
  booktitle =    "Proceedings of 14th International Conference on
                 Engineering Applications of Neural Networks (EANN
                 2013), Part {I}",
  year =         "2013",
  volume =       "383",
  series =       "Communications in Computer and Information Science",
  pages =        "12--21",
  address =      "Halkidiki, Greece",
  month =        sep # " 13-16",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, weather
                 derivatives, wavelet networks, temperature
                 derivatives",
  isbn13 =       "978-3-642-41012-3",
  bibdate =      "2014-01-25",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/eann/eann2013-1.html#AlexandirisK13",
  URL =          "http://dx.doi.org/10.1007/978-3-642-41013-0",
  URL =          "http://dx.doi.org/10.1007/978-3-642-41013-0_2",
  DOI =          "doi:10.1007/978-3-642-41013-0_2",
  abstract =     "The purpose of this study is to develop a model that
                 accurately describes the dynamics of the daily average
                 temperature in the context of weather derivatives
                 pricing. More precisely we compare two state of the art
                 algorithms, namely wavelet networks and genetic
                 programming against the classic linear approaches
                 widely using in the contexts of temperature derivative
                 pricing. The accuracy of the valuation process depends
                 on the accuracy of the temperature forecasts. Our
                 proposed models were evaluated and compared in-sample
                 and out-of-sample in various locations. Our findings
                 suggest that the proposed nonlinear methods
                 significantly outperform the alternative linear models
                 and can be used for accurate weather derivative
                 pricing.",
}

Genetic Programming entries for Antonios K Alexandiris Michael Kampouridis

Citations