Pricing Rainfall Based Futures Using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Cramer:2017:evoApplications,
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author = "Sam Cramer and Michael Kampouridis and
Alex A. Freitas and Antonis K. Alexandridis",
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title = "Pricing Rainfall Based Futures Using Genetic
Programming",
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booktitle = "20th European Conference on the Applications of
Evolutionary Computation",
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year = "2017",
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editor = "Giovanni Squillero",
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series = "LNCS",
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volume = "10199",
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publisher = "Springer",
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pages = "17--33",
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address = "Amsterdam",
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month = "19-21 " # apr,
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organisation = "Species",
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keywords = "genetic algorithms, genetic programming, Rainfall
derivatives, Derivative pricing, Gibbs sampler",
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isbn13 = "978-3-319-55849-3",
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DOI = "doi:10.1007/978-3-319-55849-3_2",
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size = "18 pages",
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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.",
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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