A comparison of wavelet networks and genetic programming in the context of temperature derivatives
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- @Article{Alexandridis:2017:IJF,
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author = "Antonis K. Alexandridis and Michael Kampouridis and
Sam Cramer",
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title = "A comparison of wavelet networks and genetic
programming in the context of temperature derivatives",
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journal = "International Journal of Forecasting",
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volume = "33",
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number = "1",
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pages = "21--47",
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year = "2017",
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ISSN = "0169-2070",
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DOI = "doi:10.1016/j.ijforecast.2016.07.002",
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URL = "http://www.sciencedirect.com/science/article/pii/S0169207016300711",
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abstract = "The purpose of this study is to develop a model that
describes the dynamics of the daily average temperature
accurately in the context of weather derivatives
pricing. More precisely, we compare two
state-of-the-art machine learning algorithms, namely
wavelet networks and genetic programming, with the
classic linear approaches that are used widely in the
pricing of temperature derivatives in the financial
weather market, as well as with various machine
learning benchmark models such as neural networks,
radial basis functions and support vector regression.
The accuracy of the valuation process depends on the
accuracy of the temperature forecasts. Our proposed
models are evaluated and compared, both in-sample and
out-of-sample, in various locations where weather
derivatives are traded. Furthermore, we expand our
analysis by examining the stability of the forecasting
models relative to the forecasting horizon. Our
findings suggest that the proposed nonlinear methods
outperform the alternative linear models significantly,
with wavelet networks ranking first, and that they can
be used for accurate weather derivative pricing in the
weather market.",
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keywords = "genetic algorithms, genetic programming, Weather
derivatives, Wavelet networks, Temperature derivatives,
Modelling, Forecasting",
- }
Genetic Programming entries for
Antonis K Alexandridis
Michael Kampouridis
Sam Cramer
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