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.
Keywords
This is a preview of subscription content, log in via an institution.
Notes
- 1.
In incomplete markets, the derivative can not be replicated via cash and the underlying asset; this is because one can not store, hold or trade weather variables.
References
Wilks, D.S.: Multisite generalization of a daily stochastic precipitation generation model. J. Hydrol. 210, 178–191 (1998)
Rodriguez-Iturbe, I., Cox, D.R., Isham, V.: Some models for rainfall based on stochastic point processes. Proc. R. Soc. Lond. A: Math. Phys. Eng. Sci. 410(1839), 269–288 (1987)
Cramer, S., Kampouridis, M., Freitas, A.A., Alexandridis, A.: Predicting rainfall in the context of rainfall derivatives using genetic programming. In: 2015 IEEE Symposium Series on Computational Intelligence for Financial Engineering and Economics, pp. 711–718, December 2015
Cramer, S., Kampouridis, M., Freitas, A.A.: Feature engineering for improving financial derivatives-based rainfall prediction. In: Proceedings of 2016 IEEE Congress on Evolutionary Computation, Vancouver. IEEE Press, July 2016
Cramer, S., Kampouridis, M., Freitas, A.: A genetic decomposition algorithm for predicting rainfall within financial weather derivatives. In: Proceedings of the Genetic and Evolutionary Computation Conference 2016, GECCO 2016, pp. 885–892. ACM, New York (2016)
Carmona, R., Diko, P.: Pricing precipitation based derivatives. Int. J. Theor. Appl. Financ. 08(07), 959–988 (2005)
Cabrera, B.L., Odening, M., Ritter, M.: Pricing rainfall futures at the CME. J. Bank. Financ. 37(11), 4286–4298 (2013)
Leobacher, G., Ngare, P.: On modelling and pricing rainfall derivatives with seasonality. Appl. Math. Financ. 18(1), 71–91 (2011)
Ritter, M., Mußhoff, O., Odening, M.: Minimizing geographical basis risk of weather derivatives using a multi-site rainfall model. Comput. Econ. 44(1), 67–86 (2014)
Noven, R.C., Veraart, A.E.D., Gandy, A.: A lévy-driven rainfall model with applications to futures pricing. Adv. Stat. Anal. 99(4), 403–432 (2015)
Jewson, S., Ziehmann, C., Brix, A.: Weather Derivative Valuation. Cambridge University Press, Cambridge (2010)
Alexandridis, A., Zapranis, A.: Weather Derivatives: Modeling and Pricing Weather-Related Risk. Springer, New York (2013)
Jenson, B., Nielsen, J.: Pricing by no arbitrage. In: Cox, D., Hinkley, D., Barndorff-Nielsen, O. (eds.) Time Series Models: In Econometrics Finance and Other Fields. Chapman & Hall/CRC/Taylor & Francis, New York (1996)
Benth, F.E., Benth, J.: Modelling and Pricing Derivatives on Precipitation, chap. 8, pp. 179–195. World Scientific (2012)
Bingham, N., Kiesel, R.: Risk-Neutral Valuation: Pricing and Hedging of Financial Derivatives. Springer Finance Textbooks. Springer, Heidelberg (2004)
Gerber, H., Shiu, E.S.W.: Option pricing by Esscher transforms. Insur. Math. Econ. 16(3), 287 (1995)
Plummer, M.: JAGS: a program for analysis of Bayesian graphical models using Gibbs sampling. In: Proceedings of the 3rd International Workshop on Distributed Statistical Computing (2003)
Barndorff-Nielsen, O.E.: Normal inverse Gaussian distributions and stochastic volatility modelling. Scand. J. Stat. 24(1), 1–13 (1997)
López-Ibáñez, M., Dubois-Lacoste, J., Stützle, T., Birattari, M.: The irace package: iterated racing for automatic algorithm configuration. Technical report, IRIDIA, Université Libre de Bruxelles, Belgium (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Cramer, S., Kampouridis, M., Freitas, A.A., Alexandridis, A.K. (2017). Pricing Rainfall Based Futures Using Genetic Programming. In: Squillero, G., Sim, K. (eds) Applications of Evolutionary Computation. EvoApplications 2017. Lecture Notes in Computer Science(), vol 10199. Springer, Cham. https://doi.org/10.1007/978-3-319-55849-3_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-55849-3_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-55848-6
Online ISBN: 978-3-319-55849-3
eBook Packages: Computer ScienceComputer Science (R0)