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Stochastic model genetic programming: Deriving pricing equations for rainfall weather derivatives

Cramer, Sam and Kampouridis, Michael and Freitas, Alex A and Alexandridis, Antonis (2019) 'Stochastic model genetic programming: Deriving pricing equations for rainfall weather derivatives.' Swarm and Evolutionary Computation, 46. 184 - 200. ISSN 2210-6502

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Abstract

Rainfall derivatives are in their infancy since starting trading on the Chicago Mercantile Exchange (CME) in 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 Genetic Programming (GP) algorithm for pricing contracts. Our novel algorithm, which is called Stochastic Model GP (SMGP), is able to generate and evolve stochastic equations of rainfall, which allows us to probabilistically transform rainfall predictions from the risky world to the risk-neutral world. In order to achieve this, SMGP's representation allows its individuals to comprise of two weighted parts, namely a seasonal component and an autoregressive component. To create the stochastic nature of an equation for each SMGP individual, we estimate the weights by using a probabilistic approach. We evaluate the models produced by SMGP in terms of rainfall predictive accuracy and in terms of pricing performance on 42 cities from Europe and the USA. We compare SMGP to 8 methods: its predecessor DGP, 5 well-known machine learning methods (M5 Rules, M5 Model trees, k-Nearest Neighbors, Support Vector Regression, Radial Basis Function), and two statistical methods, namely AutoRegressive Integrated Moving Average (ARIMA) and Monte Carlo Rainfall Prediction (MCRP). Results show that the proposed algorithm is able to statistically outperform all other algorithms.

Item Type: Article
Uncontrolled Keywords: Weather derivatives, rainfall, pricing, stochastic model genetic programming
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 18 Aug 2020 08:39
Last Modified: 18 Aug 2020 08:39
URI: http://repository.essex.ac.uk/id/eprint/27190

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