Genetic Programming for the Induction of Seasonal Forecasts: A Study on Weather-derivatives

Files in This Item:
File Description SizeFormat 
AgapitosONeillBrabazon-WeatherDerivativesBookChapter-2.pdf658.66 kBAdobe PDFDownload
Title: Genetic Programming for the Induction of Seasonal Forecasts: A Study on Weather-derivatives
Authors: Agapitos, Alexandros
O'Neill, Michael
Brabazon, Anthony
Permanent link: http://hdl.handle.net/10197/3754
Date: 2012
Abstract: The last ten years has seen the introduction and rapid growth of a market in weather derivatives, financial instruments whose payoffs are determined by the outcome of an underlying weather metric. These instruments allow organisations to protect themselves against the commercial risks posed by weather fluctuations and also provide investment opportunities for financial traders. The size of the market for weather derivatives is substantial, with a survey suggesting that the market size exceeded $45.2 Billion in 2005/2006 with most contracts being written on temperature-based metrics. A key problem faced by buyers and sellers of weather derivatives is the determination of an appropriate pricing model (and resulting price) for the financial instrument. A critical input into the pricing model is an accurate forecast of the underlying weather metric. In this study we induce seasonal forecasting temperature models by means of a Machine Learning algorithm. Genetic Programming (GP) is applied to learn an accurate, localised, long-term forecast of a temperature profile as part of the broader process of determining appropriate pricing model for weather-derivatives. Two different approaches for GP-based time-series modelling are adopted. The first is based on a simple system identification approach whereby the temporal index of the time-series is used as the sole regressor of the evolved model. The second is based on iterated single-step prediction that resembles autoregressive and moving average models in statistical time-series modelling. The major issue of effective model generalisation is tackled though the use of an ensemble learning technique that allows a family of forecasting models to be evolved using different training sets, so that predictions are formed by averaging the diverse model outputs. Empirical results suggest that GP is able to successfully induce seasonal forecasting models, and that search-based autoregressive models compose a more stable unit of evolution in terms of generalisation performance for the three datasets considered. In addition, the use of ensemble learning of 5-model predictors enhanced the generalisation ability of the system as opposed to single-model prediction systems. On a more general note, there is an increasing recognition of the utility of evolutionary methodologies for the modelling of meteorological, climatic and ecological phenomena, and this work also contributes to this literature.
Funding Details: Science Foundation Ireland
Type of material: Book Chapter
Publisher: Springer
Copyright (published version): 2012 Springer Science + Business Media
Keywords: Genetic programming;Weather-deriatives;Grammatical evolution
Subject LCSH: Genetic programming (Computer science)
Weather derivatives
Evolutionary computation
DOI: 10.1007/978-1-4614-3773-4_6
Language: en
Status of Item: Peer reviewed
Is part of: Doumpos, M., Zopounidis, C. and Pardalos, P.M. (eds.). Financial Decision Making using Computational Intelligence, Series in Optimisation and its Applications
Appears in Collections:FMC² Research Collection
Business Research Collection
CASL Research Collection

Show full item record

SCOPUSTM   
Citations 50

6
Last Week
0
Last month
checked on Jun 22, 2018

Page view(s) 10

221
checked on May 25, 2018

Download(s) 5

2,859
checked on May 25, 2018

Google ScholarTM

Check

Altmetric


This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.