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

Jorge Caiado () and Nuno Crato ()

MPRA Paper from University Library of Munich, Germany

Abstract: In 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.

Keywords: Cluster analysis; GARCH; International stock markets; Volatility. (search for similar items in EconPapers)
JEL-codes: C32 G15 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-rmg
Date: 2007
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Persistent link: http://EconPapers.repec.org/RePEc:pra:mprapa:2074

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