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A Comparison between Wavelet Networks and Genetic Programming in the Context of Temperature Derivatives

Alexandridis, Antonis, Kampouridis, Michael, Cramer, Sam (2017) A Comparison between Wavelet Networks and Genetic Programming in the Context of Temperature Derivatives. International Journal of Forecasting, 33 (1). pp. 21-47. ISSN 0169-2070. (doi:10.1016/j.ijforecast.2016.07.002)

Abstract

The purpose of this study is to develop a model that accurately describes the dynamics of the daily average temperature 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, against the classic linear approaches widely used in the pricing of temperature derivatives in the financial weather market and against 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 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 significantly outperform the alternative linear models, with wavelet networks ranking first, and can be used for accurate weather derivative pricing in the weather market.

Item Type: Article
DOI/Identification number: 10.1016/j.ijforecast.2016.07.002
Uncontrolled keywords: weather derivatives, wavelet networks, temperature derivatives, genetic programming, modelling, forecasting
Subjects: H Social Sciences > HG Finance
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.76.E95 Expert Systems (Intelligent Knowledge Based Systems)
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science
Faculties > Social Sciences > Kent Business School > Accounting and Finance
Depositing User: Antonis Alexandridis
Date Deposited: 22 Sep 2016 14:17 UTC
Last Modified: 29 May 2019 17:52 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/57441 (The current URI for this page, for reference purposes)
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