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Pricing Rainfall Based Futures Using Genetic Programming

Cramer, Sam, Kampouridis, Michael, Freitas, Alex A., Alexandridis, Antonis (2017) Pricing Rainfall Based Futures Using Genetic Programming. In: Lecture Notes in Computer Science. Applications of Evolutionary Computation. 10199. pp. 17-33. Springer ISBN 978-3-319-55848-6. (doi:10.1007/978-3-319-55849-3_2)

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Abstract

Rainfall derivatives are in their infancy since starting trading on the Chicago Mercantile 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.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1007/978-3-319-55849-3_2
Uncontrolled keywords: Rainfall derivatives, Derivative pricing, Gibbs sampler, Genetic Programming
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Q Science > QA Mathematics (inc Computing science)
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: S. Cramer
Date Deposited: 16 Feb 2017 00:02 UTC
Last Modified: 09 Jul 2019 09:06 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/60405 (The current URI for this page, for reference purposes)

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