Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/49398

TitleModelling and forecasting WIG20 daily returns
Author(s)Amado, Cristina
Silvennoinen, Annastiina
Terasvirta, Timo
KeywordsAutoregressive conditional heteroskedasticity
Forecasting volatility
Modelling volatility
Multiplicative time-varying GARCH
Smooth transition
Issue dateMar-2017
PublisherUniversidade do Minho. Núcleo de Investigação em Políticas Económicas (NIPE)
JournalNIPE Working Paper
CitationAmado, C., Silvennoinen, A., & Teräsvirta, T. (2017). Modelling and forecasting WIG20 daily returns (No. 09/2017). NIPE-Universidade do Minho
Abstract(s)The purpose of this paper is to model daily returns of the WIG20 index. The idea is to consider a model that explicitly takes changes in the amplitude of the clusters of volatility into account. This variation is modelled by a positive-valued deterministic component. A novelty in specification of the model is that the deterministic component is specified before estimating the multiplicative conditional variance component. The resulting model is subjected to misspecification tests and its forecasting performance is compared with that of commonly applied models of conditional heteroskedasticity.
TypeWorking paper
URIhttp://hdl.handle.net/1822/49398
Publisher versionhttp://www.nipe.eeg.uminho.pt/Uploads/WP_2017/NIPE%20WP_09_2017.pdf
Peer-Reviewedno
AccessOpen access
Appears in Collections:NIPE - Documentos de Trabalho

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