Pricing Rainfall Based Futures Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Cramer:2017:evoApplications,
  author =       "Sam Cramer and Michael Kampouridis and 
                 Alex A. Freitas and Antonis K. Alexandridis",
  title =        "Pricing Rainfall Based Futures Using Genetic
                 Programming",
  booktitle =    "20th European Conference on the Applications of
                 Evolutionary Computation",
  year =         "2017",
  editor =       "Giovanni Squillero",
  series =       "LNCS",
  volume =       "10199",
  publisher =    "Springer",
  pages =        "17--33",
  address =      "Amsterdam",
  month =        "19-21 " # apr,
  organisation = "Species",
  keywords =     "genetic algorithms, genetic programming, Rainfall
                 derivatives, Derivative pricing, Gibbs sampler",
  DOI =          "doi:",
  size =         "18 pages",
  abstract =     "rainfall derivatives are in their infancy since
                 starting trading on the Chicago Mercentile Exchange
                 (CME) since 2011. Being a relatively new class of
                 financial instruments there is no generally recognised
                 pricing framework used within the literature. In this
                 paper, we propose a novel framework for pricing
                 contracts using Genetic Programming (GP). Our novel
                 framework requires generating a risk-neutral density of
                 our rainfall predictions generated by GP supported by
                 Markov chain Monte Carlo and Esscher transform.
                 Moreover, instead of having a single rainfall model for
                 all contracts, we propose having a separate rainfall
                 model for each contract. We compare our novel framework
                 with and without our proposed contract-specific models
                 for pricing against the pricing performance of the two
                 most commonly used methods, namely Markov chain
                 extended with rainfall prediction (MCRP), and burn
                 analysis (BA) across contracts available on the CME.
                 Our goal is twofold, (i) to show that by improving the
                 predictive accuracy of the rainfall process, the
                 accuracy of pricing also increases. (ii)
                 contract-specific models can further improve the
                 pricing accuracy. Results show that both of the above
                 goals are met, as GP is capable of pricing rainfall
                 futures contracts closer to the CME than MCRP and BA.
                 This shows that our novel framework for using GP is
                 successful, which is a significant step forward in
                 pricing rainfall derivatives.",
  notes =        "EvoApplications2017 held in conjunction with
                 EuroGP'2017, EvoCOP2017 and EvoMusArt2017
                 http://www.evostar.org/2017/cfp_evoapps.php.",
}

Genetic Programming entries for Sam Cramer Michael Kampouridis Alex Alves Freitas Antonis K Alexandridis

Citations