A Neuro-Genetic Algorithm for Heteroskedastic Time-Series Processes Empirical Tests on Global Asset Returns |
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Authors: | R. Östermark |
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Affiliation: | ?bo Akademi University, Department of Business Administration, Henriksgatan 7, FIN–20500 ?BO, Finland, FI
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Abstract: | The paper proposes a new neuro-genetic hybrid algorithm (NGHA) for coping with ill-conditioned time-series processes. Extensive testing and comparisons to various heteroskedastic models indicate that the neuro-genetic algorithm may be a useful device for modelling complicated time series. NGHA is used to model a factor price series corresponding to the European factor of a representative set of global asset returns. NGHA provides a platform for adapting evolutionary computation to the search for suitable networks for observed time series. |
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Keywords: | Neural networks, genetic computation, heteroskedastic time series. |
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