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A GARCH-based method for clustering of financial time series: International stock markets evidence

dc.contributor.authorCaiado, Jorge
dc.contributor.authorCrato, Nuno
dc.date.accessioned2023-05-03T09:49:17Z
dc.date.available2023-05-03T09:49:17Z
dc.date.issued2007
dc.description.abstractIn this paper, we introduce a volatility-based method for clustering analysis of financial time series. Using the generalized autoregressive conditional heteroskedasticity (GARCH) models we estimate the distances between the stock return volatilities. The proposed method uses the volatility behavior of the time series and solves the problem of different lengths. As an illustrative example, we investigate the similarities among major international stock markets using daily return series with different sample sizes from 1966 to 2006. From cluster analysis, most European markets countries, United States and Canada appear close together, and most Asian/Pacific markets and the South/Middle American markets appear in a distinct cluster. After the terrorist attack on September 11, 2001, the European stock markets have become more homogenous, and North American markets, Japan and Australia seem to come closer.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationCaiado, Jorge and Nuno Crato .(2007). “A GARCH-based method for clustering of financial time series: International stock markets evidence”. MPRA Paper No. 2074 -2007. (Search PDF in 2023).pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.5/27691
dc.language.isoengpt_PT
dc.publisherMPRA - Munich Personal RePEc Archivept_PT
dc.relation.ispartofseriesMPRA Paper No. 2074/ 2007. (Search PDF in 2023);
dc.subjectCluster Analysispt_PT
dc.subjectGARCHpt_PT
dc.subjectInternational Stock Marketspt_PT
dc.subjectVolatilitypt_PT
dc.titleA GARCH-based method for clustering of financial time series: International stock markets evidencept_PT
dc.typeworking paper
dspace.entity.typePublication
rcaap.rightsopenAccesspt_PT
rcaap.typeworkingPaperpt_PT

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