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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 7/2019

29.08.2015 | Theory and Applications of Soft Computing Methods

A new NN-PSO hybrid model for forecasting Euro/Dollar exchange rate volatility

verfasst von: Ehsan Hajizadeh, Masoud Mahootchi, Akbar Esfahanipour, Mahdi Massahi Kh.

Erschienen in: Neural Computing and Applications | Ausgabe 7/2019

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Abstract

An accurate estimation of exchange rate return volatility is an important step in financial decision making problems. The main goal of this study is to enhance the ability of GARCH-type family models in forecasting the Euro/Dollar exchange rate volatility. For this purpose, a new neural-network-based hybrid model is developed in which a predefined number of simulated data series generated by the calibrated GARCH-type model along with other explanatory variables is used as input variables. The optimum number of these data series and other parameters of the network are tuned by an efficient particle swarm optimization algorithm. Using two datasets of real Euro/Dollar rates, how the proposed hybrid model could reasonably enhance the results of GARCH-type models and the traditional neural network in terms of different performance measures is demonstrated. We also illustrate how the respective simulated data series as the input variable to the network could contribute to improve the accuracy of volatility forecasting.
Vorheriger Artikel Self-adaptive differential evolution with multiple strategies for dynamic optimization of chemical processes
Nächster Artikel Green wave traffic control system optimization based on adaptive genetic-artificial fish swarm algorithm

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Metadaten
Titel
A new NN-PSO hybrid model for forecasting Euro/Dollar exchange rate volatility
verfasst von
Ehsan Hajizadeh
Masoud Mahootchi
Akbar Esfahanipour
Mahdi Massahi Kh.
Publikationsdatum
29.08.2015
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 7/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-015-2032-7

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