A new approach to modelling nonlinear time series: introducing the ExpAR-ARCH and ExpAR-GARCH models and applications

KATSIAMPA, Paraskevi (2014). A new approach to modelling nonlinear time series: introducing the ExpAR-ARCH and ExpAR-GARCH models and applications. OpenAccess Series in Informatics, 37, 34-51.

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Official URL: http://drops.dagstuhl.de/opus/volltexte/2014/4668/
Open Access URL: http://drops.dagstuhl.de/opus/volltexte/2014/4668/ (Published version)
Link to published version:: https://doi.org/10.4230/OASIcs.SCOR.2014.34


The analysis of time series has long been the subject of interest in different fields. For decades time series were analysed with linear models. Nevertheless, an issue that has been raised is whether there exist other models that can explain and fit real data better than linear ones. In this paper, new nonlinear time series models are proposed (namely the ExpAR-ARCH and the ExpAR-GARCH), which are combinations of a nonlinear model in the conditional mean and a nonlinear model in the conditional variance and have the potential of explaining observed data in various fields. Simulated data of these models are presented, while different algorithms (the Nelder-Mead simplex direct search method, the Quasi-Newton line search algorithm, the Active-Set algorithm, the Sequential Quadratic Programming algorithm, the Interior Point algorithm and a Genetic Algorithm) are used and compared in order to check their estimation performance when it comes to these suggested nonlinear models. Moreover, an application to the Dow Jones data is considered, showing that the new models can explain real data better than the AR-ARCH and AR-GARCH models. © Paraskevi Katsiampa.

Item Type: Article
Identification Number: https://doi.org/10.4230/OASIcs.SCOR.2014.34
Page Range: 34-51
SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 21 Jan 2019 12:35
Last Modified: 18 Mar 2021 06:51
URI: https://shura.shu.ac.uk/id/eprint/23002

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