Stochastic model genetic programming: Deriving pricing equations for rainfall weather derivatives
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- @Article{CRAMER:2019:SEC,
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author = "Sam Cramer and Michael Kampouridis and
Alex A. Freitas and Antonis Alexandridis",
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title = "Stochastic model genetic programming: Deriving pricing
equations for rainfall weather derivatives",
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journal = "Swarm and Evolutionary Computation",
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year = "2019",
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keywords = "genetic algorithms, genetic programming, Weather
derivatives, Rainfall, Pricing, Stochastic model
genetic programming",
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ISSN = "2210-6502",
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DOI = "doi:10.1016/j.swevo.2019.01.008",
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URL = "http://www.sciencedirect.com/science/article/pii/S2210650218305145",
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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",
- }
Genetic Programming entries for
Sam Cramer
Michael Kampouridis
Alex Alves Freitas
Antonis K Alexandridis
Citations