Abstract
In this contribution we consider a genetic programming approach to price rainfall derivatives and we test it on a case study based on data collected from a meteorological station in a city in the northeast region of Friuli Venezia Giulia (Italy), characterized by a fairly abundant rainfall.
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References
Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming: An Introduction on the Automatic Evolution of Computer Programs and Its Application. Morgan Kaufmann Publisher Inc., San Francisco (1998)
Cabrera, B.L., Odening, M., Ritter, M.: Pricing rainfall futures at the CME. J. Bank. Fina. 37(11), 4286–4298 (2013)
Carmona, R., Diko, P.: Pricing precipitation based derivatives. Int. J. Theor. Appl. Financ. 08(07), 959–988 (2005)
Chen, S.H., Yeh, C.H., Lee, W.C.: Option pricing with genetic programming. In: Koza, J.R., et al. (eds.) Genetic Programming 1998: Proceedings of the Third Annual Conference, pp. 32-37. Morgan Kaufmann, San Francisco (1998)
Chen S-H.: Genetic Algorithms and Genetic Programming in Computational Finance, Kluwer Academic Publishers, New York (2002)
Chidambaran, N., Triqueros, J., Lee, C.W.J.: Option pricing via genetic programming, In: Chen, S.H. (eds.) Evolutionary Computation in Economics and Finance. Studies in Fuzziness and Soft Computing, vol. 100, pp. 383–397. Physica, Heidelberg (2002)
Cramer, S., Kampouridis, M., Freitas, A.A., Alexandridis, A.: Pricing rainfall based futures using genetic programming. In: 20th European Conference, EvoApplications: European Conference on the Applications of Evolutionary Computation, vol. 10199, pp. 17–33. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55849-3_2
Cramer, S., Kampouridis, M., Freitas, A.A.: Decomposition genetic programming: an extensive evaluation on rainfall prediction in the context of weather derivatives. Appl. Soft Comput. 70, 208–224 (2018)
Cramer, S., Kampouridis, M., Freitas, A.A., Alexandridis, A.: Stochastic model genetic programming: deriving pricing equations for rainfall weather derivatives. Swarm Evol. Comput. 46, 184–200 (2019)
Keber, C.: Option pricing with the genetic programming approach. J. Comput. Intell. Fin. 7(6), 26–36 (1999)
Koza, J.R.: Genetic Programming. MIT Press, Cambridge (1992)
Odening, M., Musshaff, O., Xu, W.: Analysis of rainfall derivatives using daily precipitation models: opportunities and pitfalls. Agric. Fin. Rev. 68, 135–156 (2007)
Schlenker, W., Taylor, C.A.: Market expectations of a warming climate. J. Fin. Econ. 142(2), 627–640 (2021)
Acknowledgement
The authors thank Michela Zonch for her help with data collection and the excellent research assistance of her thesis period.
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Barro, D., Parpinel, F., Pizzi, C. (2022). Pricing Rainfall Derivatives by Genetic Programming: A Case Study. In: Corazza, M., Perna, C., Pizzi, C., Sibillo, M. (eds) Mathematical and Statistical Methods for Actuarial Sciences and Finance. MAF 2022. Springer, Cham. https://doi.org/10.1007/978-3-030-99638-3_11
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DOI: https://doi.org/10.1007/978-3-030-99638-3_11
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