Pricing Rainfall Based Futures Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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",
• isbn13 = "978-3-319-55849-3",
• DOI = "doi:10.1007/978-3-319-55849-3_2",
• 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